Colored steel coil production cost optimization method, equipment and medium

Abstract

The invention discloses a color steel coil production cost optimization method and device and a medium, and the method comprises the steps: determining the production cycle of a single coil according to the time information of a new coil ready signal and a coil unloading completion signal; collecting power data of production line equipment in the production cycle, and determining energy consumption cost according to the energy unit price; according to the preset auxiliary material unit price and the production parameters in the production cycle, the auxiliary material cost is determined; constructing a feature matrix and a unit area cost vector according to a preset batch of single volume production data to train a cost prediction model; according to a preset constraint condition, determining an optimal parameter combination through a gradient projection method so as to minimize the unit area cost; and according to the parameter combination, the coating process parameters and the formula parameters in the next production process are adjusted, so that cost optimization closed-loop control is achieved. The cost accounting error is greatly reduced by defining the single-roll production cycle and accounting the energy consumption and the auxiliary material consumption; and meanwhile, a closed loop is driven through data, so that the production cost of the color steel coil is optimized.

Description

Technical Field

[0001] This application relates to the field of intelligent manufacturing of steel, and in particular to a method, equipment and medium for optimizing the production cost of color steel coils. Background Technology

[0002] The production of color steel coils is characterized by continuous operation and non-fixed specifications (thickness and width). Its production cost accounting and process optimization have long faced the problems of extensiveness and disconnection.

[0003] The current mainstream cost accounting methods in the industry rely on ERP systems, mainly including two types: one is the standard cost method based on experience coefficients, which allocates costs at predefined fixed rates such as yuan / ton or yuan / meter. The accounting unit is mostly orders or production batches. The rate setting is highly dependent on historical experience and cannot dynamically adapt to real-time operating conditions such as production line speed fluctuations and process adjustments, resulting in long-term cost accounting errors; the other is the month-end inventory accounting mode, which can only output the comprehensive monthly average cost after allocation and cannot accurately measure the unit consumption of water, electricity, gas and auxiliary materials such as paint in the production process, resulting in a serious disconnect between cost data and actual production.

[0004] Secondly, while IoT technology can quantitatively measure energy consumption, it has not been used for the accurate collection of single-volume costs; although MES systems can realize management functions such as production planning and scheduling and process monitoring, they lack the ability to analyze the correlation between costs and process parameters, making it difficult to support process optimization decisions. Summary of the Invention

[0005] This application provides a method, equipment, and medium for optimizing the production cost of color steel coils, which solves the problems of existing color steel coil production being unable to perform single-coil cost accounting and being unable to accurately identify under-optimized parameters in the production process, resulting in slow optimization speed.

[0006] The embodiments of this application adopt the following technical solutions: On one hand, this application provides a method for optimizing the production cost of color steel coils. The method includes: determining the production cycle of a single coil based on the time information corresponding to the new coil readiness signal of the uncoiler and the uncoiling completion signal of the rewinder; collecting power data of the production line equipment within the production cycle, and determining the energy consumption cost of a single coil through dynamic integration based on the power data and a preset energy unit price; determining the auxiliary material cost of a single coil through dynamic integration based on a preset auxiliary material unit price and production parameters within the production cycle; the production parameters include at least paint film thickness, strip width, and production line speed; constructing a feature matrix and a unit area cost vector corresponding to the feature matrix based on preset batch single coil production data to train a cost prediction model; the cost prediction model is a nonlinear regression model, including process interaction terms, material dependence terms, and environmental correction terms; determining the optimal parameter combination through gradient projection based on preset constraints to minimize the unit area cost output by the cost prediction model; the parameter combination includes a combination of production parameters and curing oven temperature setpoint, coating roller pressure, and paint feeding ratio; and adjusting the coating process parameters and formula parameters in the next production process based on the parameter combination to achieve closed-loop cost optimization control.

[0007] In one example, based on pre-set batch single-roll production data, a feature matrix and a corresponding unit area cost vector are constructed. Specifically, this includes: extracting the feature vector for each roll in the pre-set batch single-roll production data; the feature vector includes equipment process parameters, formula parameters, substrate properties, and environmental variables; combining the feature vectors of each roll to construct a feature matrix of pre-set dimensions; each row in the feature matrix corresponds to the feature vector of a single roll production run; calculating the unit area cost of each roll based on its strip steel production area, energy consumption cost, and auxiliary material cost; and combining the unit area costs of each roll to obtain the unit area cost vector corresponding to the feature matrix.

[0008] In one example, based on preset constraints, the optimal parameter combination is determined using the gradient projection method to minimize the unit area cost output by the cost prediction model. Specifically, this includes: performing a sensitivity analysis on the cost prediction model; calculating the partial derivative of the unit area cost with respect to each process parameter to determine the process parameter with the greatest impact on the unit area cost; constructing a feasible region with the goal of minimizing the unit area cost; the feasible region includes process parameter boundary constraints and coating film thickness quality constraints; and performing an iterative search within the feasible region using the gradient projection method to determine the optimal parameter combination.

[0009] In one example, the production cycle of a single roll is determined based on the time information corresponding to the uncoiler new roll ready signal and the rewinder unwinding completion signal, respectively. Specifically, this includes: establishing communication with the production line PLC via the Modbus protocol to obtain the uncoiler new roll ready signal and the rewinder unwinding completion signal; recording the time corresponding to the uncoiler new roll ready signal as the start timestamp of the single roll production cycle, and recording the time corresponding to the rewinder unwinding completion signal as the end timestamp of the single roll production cycle; and determining the production cycle of a single roll by calculating the time interval based on the start timestamp and the end timestamp.

[0010] In one example, power data of production line equipment is collected during the production cycle. Based on the power data and a preset energy unit price, the energy consumption cost of a single roll is determined through dynamic integration. Specifically, this includes: collecting instantaneous power data of production line equipment during the production cycle; the production line equipment includes at least an uncoiler, a coating machine, and a curing oven; integrating the instantaneous power data during the production cycle using a dynamic integration algorithm to obtain the total energy consumption of the production line equipment during the production cycle; and obtaining the energy consumption cost of a single roll based on the total energy consumption and the preset energy unit price.

[0011] In one example, the auxiliary material cost per roll is determined by dynamic integration based on the preset auxiliary material unit price and the production parameters within the production cycle. Specifically, this includes: acquiring paint film thickness data, strip width data, and production line speed data within the production cycle; integrating the paint film thickness data, strip width data, and production line speed data within the production cycle using a dynamic integration algorithm to obtain the paint consumption per roll within the production cycle; and determining the auxiliary material cost per roll based on the paint consumption and the preset auxiliary material unit price.

[0012] In one example, the coating process parameters and formula parameters in the next production process are adjusted according to the parameter combination. Specifically, this includes: sending the coating roller pressure, production line speed and paint feeding ratio in the parameter combination to the production line PLC through the control interface; and adjusting the coating roller pressure through the PID control algorithm to control the paint film thickness in the production process within the preset quality constraint range.

[0013] In one example, the method also includes: in the calculation of auxiliary material costs, the thickness of the paint film is collected by an X-ray thickness gauge according to the preset sampling frequency and sampling accuracy.

[0014] On the other hand, embodiments of this application provide a color steel coil production cost optimization device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform any of the above-mentioned color steel coil production cost optimization methods.

[0015] On the other hand, embodiments of this application provide a non-volatile computer storage medium for optimizing the production cost of color-coated steel coils, which stores computer-executable instructions capable of executing any of the above-mentioned methods for optimizing the production cost of color-coated steel coils.

[0016] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects: This application precisely defines the production cycle of a single roll through an event-triggered mechanism (uncoiler clamp closure and rewinder unloading completion signals), and combines this with a dynamic integral algorithm to calculate energy consumption and auxiliary material consumption in real time, achieving accurate cost aggregation at the single roll level and reducing cost accounting errors to 1.8%. Through real-time monitoring of paint film thickness and closed-loop optimization of the feed ratio, it achieves refined control of paint usage, effectively reducing waste caused by over-spraying and formula mismatch, lowering the waste rate to 4.3%. By constructing a historical feature matrix and cost prediction model, and using sensitivity analysis to quickly solve for the optimal combination of process parameters, it improves process adaptability and response speed, significantly shortening the optimization cycle. Finally, through data-driven cost and process relationship modeling, it achieves accurate prediction of order costs and profit optimization, enhancing the enterprise's decision support capabilities. Attached Figure Description

[0017] To more clearly illustrate the technical solution of this application, some embodiments of this application will be described in detail below with reference to the accompanying drawings, in which: Figure 1 A flowchart illustrating a method for optimizing the production cost of color-coated steel coils, provided as an embodiment of this application; Figure 2 A system architecture diagram of a method for optimizing the production cost of color steel coils provided in this application embodiment; Figure 3 A system data closed-loop flowchart of a method for optimizing the production cost of color steel coils provided in this application embodiment; Figure 4 A flowchart illustrating the closed-loop control of auxiliary material costs in a method for optimizing the production cost of color steel coils, provided in this application embodiment; Figure 5 A data closed-loop training flowchart for a method to optimize the production cost of color steel coils provided in this application embodiment; Figure 6 A flowchart illustrating the optimal material feeding ratio algorithm for a method to optimize the production cost of color steel coils provided in this application embodiment; Figure 7 This is a schematic diagram of a cost optimization device for color steel coil production provided in an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] Some embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0020] Figure 1 This is a flowchart illustrating a method for optimizing the production cost of color-coated steel coils, provided as an embodiment of this application. This method can be applied to different business areas. Certain input parameters or intermediate results in this process can be manually adjusted to help improve accuracy.

[0021] The analysis method involved in the embodiments of this application can be implemented by a terminal device or a server, and this application does not impose any special limitations on it. For ease of understanding and description, the following embodiments are all described in detail using a server as an example.

[0022] Based on this Figure 1 The process may include the following steps: S101: Determine the production cycle of a single roll based on the time information corresponding to the new roll ready signal of the uncoiler and the unwinding completion signal of the rewinder.

[0023] In some embodiments of this application, in this step, the system establishes a communication connection with the production line PLC through an IoT platform using the Modbus protocol, and monitors and captures physical event signals from the equipment layer in real time. Specifically: The system monitors and captures the new roll ready signal of the uncoiler (i.e., when the PLC point uncoiler_clamp closed state is 1) and the unwinding completion signal of the rewinder (i.e., when the PLC point unwinding trolley_reset complete state is 1). The system then records the timestamp corresponding to the unwinding signal. Record the timestamp corresponding to the unloading completion signal as the start time of a single roll production cycle. As the end time.

[0024] Furthermore, by calculating the time interval between the start and end times, the independent production cycle of each color-coated steel coil is precisely defined. The specific formula is:

[0025] This method uses physical events as triggers, replacing the traditional extensive method that relies on manual timing or fixed shift divisions. It defines a cost accounting time window for each independent color steel coil without overlap or omission, effectively avoiding the problem of cycle overlap or omission caused by continuous production line operation. It provides an accurate time benchmark for subsequent single coil cost accounting, ensuring the accuracy of cost collection objects from the source.

[0026] S102: Collect power data of production line equipment during the production cycle, and determine the energy consumption cost of a single roll by dynamic integration based on the power data and the preset energy unit price.

[0027] In some embodiments of this application, during the production cycle Internally, the system uses an energy management system or smart meters to collect real-time instantaneous power data from key production line equipment such as uncoilers, coating machines, and curing ovens at a high sampling frequency. The energy unit price is set at... (Unit: Yuan / kWh)

[0028] Furthermore, based on the collected instantaneous power data, the system uses a dynamic integration algorithm to integrate the power change over time to obtain the total energy consumption during the production process of the roll.

[0029]

[0030] Furthermore, based on the total energy consumption and the unit price of energy, the energy cost of this single roll is calculated.

[0031] This method achieves precise binding of energy consumption to specific production volumes, solving the measurement deviation caused by traditional batch or time-based average allocation.

[0032] S103: Determine the cost of auxiliary materials per roll by dynamic integration based on the preset unit price of auxiliary materials and the production parameters within the production cycle; the production parameters include at least the paint film thickness, strip width and production line speed.

[0033] In some embodiments of this application, during the production cycle Inside, the system uses an X-ray thickness gauge (sampling frequency ≥10Hz, accuracy ±0.1μm) to collect paint film thickness data in real time. Simultaneously, the strip width is obtained from the PLC. Production line speed data Obtain coating density from the process database. Get the paint unit price from the ERP system. .

[0034] It should be noted that if the measured deviation of the paint film thickness is greater than the threshold, the pressure of the coating roller and the pump speed need to be adjusted.

[0035] Furthermore, based on these production parameters, the system uses a dynamic integral algorithm to integrate the paint film thickness, strip width, and production line speed to calculate the paint consumption volume per roll, and then combines the paint density to convert it into the consumption mass.

[0036]

[0037] Finally, based on the preset unit price of auxiliary materials, the system calculates the cost of auxiliary materials for this volume.

[0038]

[0039] This integral method can accurately track consumption changes caused by fluctuations in specifications and speed, achieving precise measurement of auxiliary material costs and effectively addressing the measurement distortion problem caused by fluctuations in steel coil specifications.

[0040] S104: Based on the preset batch single-roll production data, construct a feature matrix and the unit area cost vector corresponding to the feature matrix to train a cost prediction model; the cost prediction model is a nonlinear regression model, including process interaction terms, material dependence terms and environmental correction terms.

[0041] In some embodiments of this application, the system extracts feature vectors from single-volume production data of a preset batch (e.g., the most recent 100 volumes). This includes equipment process parameters (production line speed, curing oven temperature, coating roller pressure, coating feed ratio), substrate properties (thickness, width, material code), and environmental variables (ambient temperature, humidity).

[0042] Furthermore, these feature vectors are combined to construct a feature matrix X, with each row corresponding to one single roll production. Simultaneously, the system calculates the unit area cost based on the strip steel production area, energy consumption cost, and auxiliary material cost for each roll. And combine them to form the unit area cost vector Y.

[0043] ,

[0044] Among them, unit area cost , Let be the strip production area of ​​volume i.

[0045] Furthermore, based on the feature matrix X and the cost vector Y, a nonlinear regression model is trained as the cost prediction model. .

[0046]

[0047] in, For nonlinear basis functions, For the j-th nonlinear basis function The coefficient.

[0048] The model captures the coupling effect between parameters through process interaction terms, reflects the influence of substrate characteristics through material dependence terms, and compensates for the influence of environmental fluctuations through environmental correction terms.

[0049] S105: Based on preset constraints, determine the optimal parameter combination using the gradient projection method to minimize the unit area cost output by the cost prediction model; the parameter combination includes a combination of production parameters, curing oven temperature setpoint, coating roller pressure, and coating feed ratio.

[0050] In some embodiments of this application, the system first performs a sensitivity analysis on the cost prediction model, identifying the key parameters with the greatest impact on cost by calculating the partial derivatives of unit area cost with respect to various process parameters. Subsequently, the system aims to minimize the unit area cost. To achieve the goal, construct a feasible domain that includes process parameter boundary constraints (such as speed, temperature, and pressure ranges) and film thickness quality constraints (such as thickness compliance requirements):

[0051] The quality constraint is the predicted film thickness. The process requirements must be met: The boundary constraint is that all process parameters must be within their feasible range: .

[0052] Furthermore, the system employs gradient projection to perform iterative search within the feasible region to quickly determine the optimal combination of process parameters (including production line speed, curing oven temperature, coating roller pressure, coating feed ratio, etc.).

[0053] The algorithm first calculates the objective function. By analyzing the gradients of each parameter (sensitivity analysis), the direction of fastest cost reduction is determined. Then, during the iterative search, the search direction is projected onto the feasible region constituted by the above constraints, ensuring that the solution at each iteration satisfies all constraints, thereby quickly and reliably converging to the global or local optimum. To minimize production costs while ensuring product quality.

[0054] S106: Based on the parameter combination, adjust the coating process parameters and formula parameters in the next production process to achieve closed-loop control for cost optimization.

[0055] In some embodiments of this application, the system uses a control interface to combine the optimized parameters. (Such as coating roller pressure, production line speed, and paint feeding ratio) are sent to the production line PLC. During the production process, the system dynamically adjusts the actuators such as coating roller pressure through a PID control algorithm to ensure that the paint film thickness is controlled in real time within the preset quality constraint range.

[0056] Meanwhile, the data (feature vectors) of newly produced steel coils and actual cost The data will be collected and added to the historical dataset to periodically update the cost prediction model f(x), enabling the model to adapt to changes in the production line, thereby achieving a self-learning and continuously improving cost optimization closed-loop control system.

[0057] It should be noted that, although the embodiments in this application are based on... Figure 1 Steps S101 to S106 will be described sequentially, but this does not mean that steps S101 and S106 must be performed in a strict order. The reason this embodiment follows this order is... Figure 1 The order in which steps S101 to S106 are described is provided to facilitate understanding of the technical solutions of the embodiments of this application by those skilled in the art. In other words, in the embodiments of this application, the order of steps S101 to S106 can be appropriately adjusted according to actual needs.

[0058] pass Figure 1 This application employs an event-triggered mechanism (uncoiler clamp closure and rewinder unloading completion signals) to precisely define the production cycle of a single roll. Combined with a dynamic integral algorithm, it calculates energy consumption and auxiliary material consumption in real time, achieving accurate cost aggregation at the single-roll level and reducing cost accounting errors to 1.8%. Through real-time monitoring of paint film thickness and closed-loop optimization of the feed ratio, it achieves refined control of paint usage, effectively reducing waste caused by over-spraying and formula mismatch, lowering the waste rate to 4.3%. By constructing a historical feature matrix and cost prediction model, and using sensitivity analysis to quickly solve for the optimal combination of process parameters, it improves process adaptability and response speed, significantly shortening the optimization cycle. Finally, through data-driven cost and process relationship modeling, it achieves accurate prediction of order costs and profit optimization, enhancing enterprise decision support capabilities.

[0059] Figure 2 This is a system architecture diagram of a method for optimizing the production cost of color steel coils provided in an embodiment of this application.

[0060] exist Figure 2 The diagram shows a structure including a device layer, a data acquisition layer, an intelligent decision-making layer, and an execution layer. The data acquisition layer collects device data from the device layer, the intelligent decision-making layer makes decisions based on the data to determine the optimal parameter combination, and then the execution layer executes the data.

[0061] Figure 3A system data closed-loop flowchart of a method for optimizing the production cost of color steel coils provided in this application embodiment.

[0062] exist Figure 3 In the process, newly generated data from the execution layer will be collected again to update the prediction model and optimize the process, thereby achieving a closed loop.

[0063] Figure 4 This application provides a flowchart of the closed-loop control process for auxiliary material costs in a method for optimizing the production cost of color steel coils.

[0064] exist Figure 4 The diagram shows the calculation process for the auxiliary material cost of this application. It should be noted that if the collected deviation of the paint film thickness is greater than the threshold, the pressure of the coating roller and the pump speed need to be adjusted.

[0065] Figure 5 A data closed-loop training flowchart for a method to optimize the production cost of color steel coils provided in this application embodiment.

[0066] exist Figure 5 The middle section describes the training and updating process of the cost prediction model for this application.

[0067] Figure 6 The flowchart illustrates the optimal material feeding ratio algorithm for a method to optimize the production cost of color steel coils provided in this application embodiment.

[0068] exist Figure 6 The diagram illustrates the process of deriving the optimal feed ratio for this application. It should be noted that after the solution is derived through the steps shown in the diagram, it needs to be decoded, converted, and manually reviewed before it can be distributed to the PLC.

[0069] Figure 7 A schematic diagram of a cost optimization device for color steel coil production provided in this application embodiment includes: At least one processor; and, A memory that is communicatively connected to at least one processor; wherein, A method for optimizing the production cost of color steel coils, wherein the memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor to enable at least one processor to perform any of the above-mentioned tasks.

[0070] Some embodiments of this application provide a non-volatile computer storage medium for optimizing the production cost of color-coated steel coils, which stores computer-executable instructions capable of executing any of the above-mentioned methods for optimizing the production cost of color-coated steel coils.

[0071] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and medium embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the description of the method embodiments.

[0072] The devices and media provided in this application are one-to-one with the methods. Therefore, the devices and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0073] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0074] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0075] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0076] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0077] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0078] Memory may include non-persistent storage in computer-readable media, random access memory (RAM), and non-volatile memory such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0079] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0080] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0081] The above are merely embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the technical principles of this application should fall within the protection scope of this application.

Claims

1. A method for optimizing the production cost of color-coated steel coils, characterized in that, The method includes: The production cycle of a single roll is determined based on the time information corresponding to the new roll ready signal of the uncoiler and the unwinding completion signal of the rewinder. Collect power data of production line equipment during the production cycle, and determine the energy consumption cost of a single roll by dynamic integration based on the power data and the preset energy unit price. The cost of auxiliary materials per roll is determined by dynamic integration based on the preset unit price of auxiliary materials and the production parameters within the production cycle; the production parameters include at least the paint film thickness, strip width, and production line speed. Based on the pre-set batch single-roll production data, a feature matrix and the corresponding unit area cost vector of the feature matrix are constructed to train a cost prediction model; the cost prediction model is a non-linear regression model, including process interaction terms, material dependence terms and environmental correction terms; Based on preset constraints, the optimal parameter combination is determined by gradient projection method to minimize the unit area cost output by the cost prediction model; the parameter combination includes the combination of production parameters, curing oven temperature setpoint, coating roller pressure, and coating feed ratio. Based on the parameter combination, the coating process parameters and formula parameters in the next production process are adjusted to achieve closed-loop control for cost optimization.

2. The method according to claim 1, characterized in that, The step of constructing a feature matrix and a unit area cost vector corresponding to the feature matrix based on the pre-set batch single-roll production data specifically includes: Extract the feature vector of each roll from the pre-set batch single-roll production data; the feature vector includes equipment process parameters, formula parameters, substrate properties and environmental variables; The feature vectors of each volume are combined to construct a feature matrix of a preset dimension; each row of the feature matrix corresponds to the feature vector produced in a single volume. Calculate the unit area cost of each coil based on the production area of ​​each coil of strip steel, energy consumption cost, and auxiliary material cost. The unit area cost of each volume is combined to obtain the unit area cost vector corresponding to the feature matrix.

3. The method according to claim 1, characterized in that, The step of determining the optimal parameter combination using the gradient projection method based on preset constraints to minimize the unit area cost output by the cost prediction model specifically includes: A sensitivity analysis was performed on the cost prediction model, and the process parameters with the greatest impact on the unit area cost were determined by calculating the partial derivative of the unit area cost with respect to each process parameter. With the goal of minimizing the cost per unit area, a feasible region is constructed; the feasible region includes process parameter boundary constraints and coating film thickness quality constraints. The optimal combination of parameters is determined by iteratively searching within the feasible region using the gradient projection method.

4. The method according to claim 1, characterized in that, The step of determining the production cycle of a single roll based on the time information corresponding to the new roll ready signal from the uncoiler and the unwinding completion signal from the rewinder specifically includes: Establish communication with the production line PLC via Modbus protocol to obtain the new roll ready signal of the uncoiler and the unwinding completion signal of the rewinder; The time corresponding to the new roll ready signal of the uncoiler is recorded as the start timestamp of the single roll production cycle, and the time corresponding to the unwinding completion signal of the rewinder is recorded as the end timestamp of the single roll production cycle; based on the start timestamp and the end timestamp, the production cycle of a single roll is determined by calculating the time interval.

5. The method according to claim 1, characterized in that, The process of collecting power data from production line equipment during the production cycle, and determining the energy cost per roll through dynamic integration based on the power data and a preset energy unit price, specifically includes: During the production cycle, instantaneous power data of the production line equipment is collected; the production line equipment includes at least an uncoiler, a coating machine, and a curing oven; During the production cycle, the instantaneous power data is integrated using a dynamic integration algorithm to obtain the total energy consumption of the production line equipment during the production cycle. Based on the total energy consumption and the preset energy unit price, the energy cost per roll is obtained.

6. The method according to claim 1, characterized in that, The step of determining the cost of auxiliary materials per roll through dynamic integration based on the preset unit price of auxiliary materials and the production parameters within the production cycle specifically includes: Acquire data on paint film thickness, strip width, and production line speed during the production cycle; During the production cycle, the paint film thickness data, strip width data, and production line speed data are integrated using a dynamic integration algorithm to obtain the paint consumption per roll during the production cycle. The cost of auxiliary materials per roll is determined based on the amount of paint consumed and the preset unit price of auxiliary materials.

7. The method according to claim 1, characterized in that, The step of adjusting the coating process parameters and formula parameters for the next production process based on the parameter combination specifically includes: The coating roller pressure, production line speed, and coating feed ratio in the parameter combination are sent to the production line PLC through the control interface; The pressure of the coating roller is adjusted by using a PID control algorithm to control the paint film thickness within a preset quality constraint range during the production process.

8. The method according to claim 1, characterized in that, The method further includes: In the calculation of the auxiliary material cost, the thickness of the paint film is collected by an X-ray thickness gauge according to the preset sampling frequency and sampling accuracy.

9. A production cost optimization device for color steel coils, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method for optimizing the production cost of color steel coils as described in any one of claims 1-8.

10. A storage medium for optimizing the production cost of color-coated steel coils, storing computer-executable instructions, characterized in that, The computer-executable instructions are capable of executing the method for optimizing the production cost of color steel coils as described in any one of claims 1-8.

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