Intelligent control method and system for enameled wire production line

Abstract

The invention is suitable for the technical field of varnished wire production, and particularly relates to an intelligent control method and system for a varnished wire production line, and the method comprises the steps: obtaining a production order and existing resource information of a varnished wire; determining required material data according to the priority of each production order; under the condition that the existing material data meets the required material data and the equipment loss data does not exceed a loss threshold value, obtaining quality data and energy consumption data of each production link; when the quality data is not in the standard range, obtaining a first adjustment range according to the production order; in the first adjustment range, corresponding preset production parameters are adjusted according to the quality data; when the quality data is in the standard range and the energy consumption data exceeds the energy consumption threshold value, obtaining a second adjustment range according to the first adjustment range; and adjusting the preset production parameters according to the second adjustment range. Therefore, the problem that the quality, the cost and the efficiency are difficult to balance in the enameled wire production process can be solved.

Description

Technical Field

[0001] The present application belongs to the technical field of enameled wire production, and in particular relates to an intelligent control method and system for an enameled wire production line. Background Art

[0002] Enameled wire is the main type of winding wire, consisting of two parts: conductor and insulation layer. The bare wire is annealed and softened, and then painted and baked multiple times.

[0003] Existing enameled wire production relies on manual parameter adjustment based on experience, lacking real-time correlation analysis of data such as quality fluctuations, energy consumption peaks, and equipment load. This often results in process adjustments that "give up one thing for the other and lose another." For example, lowering the furnace temperature to reduce energy consumption may lead to insufficient paint film curing, while simply extending the baking time, while improving quality, will reduce equipment utilization. This discrete decision-making model places quality, cost, and efficiency objectives in a constant state of dynamic competition, making it difficult to reach a global optimal solution. As a result, the enameled wire production process faces the challenge of balancing quality, cost, and efficiency. Summary of the Invention

[0004] The embodiments of the present application provide an intelligent control method and system for an enameled wire production line, which can improve the problem of difficulty in balancing quality, cost and efficiency in the enameled wire production process.

[0005] In a first aspect, an embodiment of the present application provides an intelligent control method for an enameled wire production line, comprising: Obtaining production orders for each batch of enameled wire and existing resource information for each production link; wherein the existing resource information includes existing material data and equipment loss data; Determining required material data based on the priority of each production order; When the existing material data meets the required material data and the equipment loss data does not exceed the loss threshold, obtaining the quality data and energy consumption data of the wire material in each production link; wherein the quality data includes surface defect data and specifications; When the quality data is not within a standard range, obtaining a first adjustment range according to the production order; Within the first adjustment range, adjusting corresponding preset production parameters according to the quality data; When the quality data is within the standard range and the energy consumption data exceeds the energy consumption threshold, a second adjustment range is obtained according to the first adjustment range; The preset production parameter is adjusted according to a second adjustment range.

[0006] The above technical solutions in the embodiments of the present application have at least the following technical effects: The embodiment of the present application provides an intelligent control method for an enameled wire production line, which obtains the production orders of each batch of enameled wire and the existing resource information of each production link; determines the required material data according to the priority of each production order; obtains the quality data and energy consumption data of the wire in each production link when the existing material data meets the required material data and the equipment loss data does not exceed the loss threshold; obtains the first adjustment range according to the production order when the quality data is not within the standard range; adjusts the corresponding preset production parameters according to the quality data within the first adjustment range; obtains the second adjustment range according to the first adjustment range when the quality data is within the standard range and the energy consumption data exceeds the energy consumption threshold; adjusts the preset production parameters according to the second adjustment range. Through the integration of order data and resource information, enterprises can avoid resource conflicts or idle production capacity caused by information islands; dynamic adjustment based on quality data can quickly respond to process deviations; parameter adjustment based on multi-objective optimization can achieve a coordinated improvement in quality, cost, and efficiency. Therefore, the intelligent control method for an enameled wire production line provided by the embodiment of the present application can improve the problem of difficulty in balancing quality, cost, and efficiency in the production process of enameled wire.

[0007] In a possible implementation of the first aspect, before obtaining the quality data and energy consumption data of the wire material in each production link when the existing material data meets the required material data and the equipment loss data does not exceed the loss threshold, the method further includes: Determining whether the existing material data meets the required material data; If the existing material data does not meet the required material data, a corresponding warning message is issued.

[0008] In a possible implementation of the first aspect, the equipment loss data includes a vibration frequency, temperature, and / or current of the production equipment. When the existing material data satisfies the required material data and the equipment loss data does not exceed a loss threshold, before obtaining the quality data and energy consumption data of the wire material in each of the production links, the method further includes: constructing an isolation tree based on the equipment loss data; Obtaining an anomaly score based on the isolation tree; It is determined whether the equipment loss data exceeds the loss threshold according to the abnormality score.

[0009] In a possible implementation of the first aspect, the production order includes specifications, quantity, and deadline. When the quality data is not within a standard range, before obtaining the first adjustment range according to the production order, the method further includes: Determining whether the quality data of the wire material in each production link is within the standard range according to the specifications in the production order; If the quality data of the wire material in the production link is within the standard range, it is determined whether the energy consumption data exceeds the energy consumption threshold.

[0010] In a possible implementation of the first aspect, the method further includes: If the energy consumption data does not exceed the energy consumption threshold, continue production and update the equipment loss data.

[0011] In a possible implementation of the first aspect, the first adjustment range is a parameter value range that ensures the production completion time is within the time limit of the production order. When the quality data is not within the standard range, obtaining the first adjustment range based on the production order includes: If the quality data is not within the standard range, determining the production time of each production link according to the deadline and each preset production parameter; A first adjustment range of a corresponding production parameter is obtained according to the production time.

[0012] In a possible implementation of the first aspect, adjusting the corresponding preset production parameter according to the quality data within the first adjustment range includes: determining an objective function based on the deviation of the quality data from the standard range; Obtaining the gradient of each of the preset production parameters according to the objective function; The preset production parameters are updated according to the gradients.

[0013] In a possible implementation of the first aspect, when the quality data is within the standard range and the energy consumption data exceeds the energy consumption threshold, obtaining the second adjustment range according to the first adjustment range includes: Within the first adjustment range, randomly generate multiple sets of production parameters and input them into the energy consumption model and the quality model to obtain energy consumption values ​​and quality scores; Obtaining fitness according to the energy consumption value and the quality score; Obtaining a non-dominated solution according to the fitness; The Pareto front is gradually approached according to the non-dominated solution, and the Pareto front is determined as the second adjustment range.

[0014] In a possible implementation of the first aspect, adjusting the preset production parameter according to the second adjustment range includes: Normalizing the energy consumption values ​​and quality scores of each production parameter within the second adjustment range, and calculating the distance between the energy consumption values ​​and quality scores of each set of production parameters and a positive ideal solution and a negative ideal solution to obtain relative proximity; wherein the positive ideal solution includes the minimum energy consumption value and the maximum quality score, and the negative ideal solution includes the maximum energy consumption value and the minimum quality score; The preset production parameters are adjusted according to the production parameter corresponding to the largest relative proximity.

[0015] In a second aspect, an embodiment of the present application provides an intelligent control system for an enameled wire production line, comprising: The first acquisition module is used to obtain the production order of each batch of enameled wire and the existing resource information of each production link; wherein the existing resource information includes existing material data and equipment loss data; A material module, used to determine the required material data according to the priority of each production order; A second acquisition module is configured to acquire quality data and energy consumption data of the wire material in each of the production links when the existing material data meets the required material data and the equipment loss data does not exceed a loss threshold; wherein the quality data includes surface defect data and specifications; a first adjustment range module, configured to obtain a first adjustment range according to the production order when the quality data is not within a standard range; a first adjustment module, configured to adjust corresponding preset production parameters according to the quality data within the first adjustment range; A second adjustment range module is configured to obtain a second adjustment range according to the first adjustment range when the quality data is within the standard range and the energy consumption data exceeds an energy consumption threshold; The second adjustment module is used to adjust the preset production parameters according to a second adjustment range.

[0016] In a third aspect, an embodiment of the present application provides an intelligent control device for an enameled wire production line, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements a method as described in any one of the first aspects above when executing the computer program.

[0017] In a fourth aspect, an embodiment 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 method described in any one of the first aspects above is implemented.

[0018] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product runs on an intelligent control device for an enameled wire production line, the intelligent control device for the enameled wire production line executes any one of the methods described in the first aspect above.

[0019] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. 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 any creative work.

[0021] Figure 1 This is a flow chart of an intelligent control method for an enameled wire production line provided in one embodiment of the present application; Figure 2 This is a schematic diagram of the implementation process of steps S300, S400, S500, S600 and S700 in the intelligent control method for an enameled wire production line provided in one embodiment of the present application; Figure 3 This is a schematic diagram of the structure of the intelligent control system for the enameled wire production line provided in an embodiment of the present application; Figure 4 It is a structural schematic diagram of the intelligent control equipment of the enameled wire production line provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0023] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0024] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0025] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0026] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0027] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0028] In related technologies, enameled wire production relies on manual experience to adjust parameters, lacking real-time correlation analysis of data such as quality fluctuations, energy consumption peaks, and equipment load. This often results in process adjustments that "lose sight of one thing while focusing on another." For example, lowering the furnace temperature to reduce energy consumption may cause insufficient paint film curing, while simply extending the baking time, while improving quality, will reduce equipment utilization. This discrete decision-making model keeps quality, cost, and efficiency goals in a state of constant dynamic competition, making it difficult to reach a global optimal solution. As a result, the enameled wire production process faces the problem of difficulty in balancing quality, cost, and efficiency.

[0029] To solve the above problems, the embodiment of the present application provides an intelligent control method and system for an enameled wire production line. In this method, the production orders of each batch of enameled wire and the existing resource information of each production link are obtained; the required material data is determined according to the priority of each production order; when the existing material data meets the required material data and the equipment loss data does not exceed the loss threshold, the quality data and energy consumption data of the wire material of each production link are obtained; when the quality data is not within the standard range, a first adjustment range is obtained according to the production order; within the first adjustment range, the corresponding preset production parameters are adjusted according to the quality data; when the quality data is within the standard range and the energy consumption data exceeds the energy consumption threshold, a second adjustment range is obtained according to the first adjustment range; and the preset production parameters are adjusted according to the second adjustment range. By integrating order data and resource information, enterprises can avoid resource conflicts or idle production capacity caused by information islands; dynamic adjustment based on quality data can quickly respond to process deviations; parameter adjustment based on multi-objective optimization can achieve a coordinated improvement in quality, cost, and efficiency. Therefore, the intelligent control method for an enameled wire production line provided by the embodiment of the present application can improve the problem of difficulty in balancing quality, cost, and efficiency in the production process of enameled wire.

[0030] The intelligent control method for the enameled wire production line provided in the embodiment of the present application can be applied to the intelligent control equipment of the enameled wire production line. At this time, the intelligent control equipment of the enameled wire production line is the executor of the intelligent control method for the enameled wire production line provided in the embodiment of the present application. The embodiment of the present application does not impose any restrictions on the specific type of the intelligent control equipment of the enameled wire production line.

[0031] For example, the intelligent control device of the enameled wire production line can be a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a mobile phone, a tablet computer, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a desktop computer, a handheld device with wireless communication capabilities, a computing device or other processing device connected to a wireless modem, a computer, a laptop computer, a handheld communication device, a handheld computing device, etc., but is not limited thereto.

[0032] In order to better understand the intelligent control method for the enameled wire production line provided in the embodiment of the present application, the specific implementation process of the intelligent control method for the enameled wire production line provided in the embodiment of the present application is exemplarily introduced below.

[0033] Figure 1 A schematic flow chart of an intelligent control method for an enameled wire production line provided in an embodiment of the present application is shown. The intelligent control method for an enameled wire production line includes: S100: Obtain production orders for each batch of enameled wire and existing resource information for each production link, wherein the existing resource information includes existing material data and equipment loss data.

[0034] For example, production orders (including the specifications, quantity and deadlines of enameled wires) can be obtained from the ERP system, existing material data (such as the inventory of copper and insulating varnish) can be obtained from the MES system, and equipment loss data (including the vibration frequency, temperature and / or current of production equipment in each production link) can be collected through the SCADA system.

[0035] S200, determining the required material data according to the priority of each production order.

[0036] For example, the priority of each production order can be determined based on its deadline. The orders are sorted in descending order of priority, and the required material information is determined based on the specifications and quantity of the enameled wire in the highest-priority production order. For example, for 0.5mm diameter enameled wire, the copper required per kilometer is: π × (0.25mm²) × 1000m × 8.96g / cm³ = 70.4kg, and the insulating varnish is: 0.1L. Order 001 (the highest priority) requires the production of 500km of 0.5mm diameter enameled wire. The calculated copper required is: 500 × 70.4 = 35,200kg, and the insulating varnish is: 500 × 0.1 = 50L.

[0037] S300: When the existing material data meets the required material data and the equipment loss data does not exceed the loss threshold, the quality data and energy consumption data of the wire material in each production link are obtained. The quality data includes surface defect data and specifications.

[0038] It can be understood that surface defect data includes defect location and defect area. Specifications include insulation thickness, wire diameter and length.

[0039] For example, a reasonable loss threshold can be determined based on historical data and industry standards. For example, if the loss threshold is 50%, the equipment loss rate is determined based on the equipment loss data. If the existing material data meets the required material data and the equipment loss rate is ≤50%, surface defect data is obtained from the online inspection system and energy consumption data per unit of output is read from the electric meter interface.

[0040] In one possible implementation, see Figure 2 S300: When the existing material data meets the required material data and the equipment loss data does not exceed the loss threshold, before obtaining the quality data and energy consumption data of the wire material in each production link, the method further includes: S301, determining whether the existing material data meets the required material data.

[0041] For example, the existing material data can be obtained in real time and compared with the required material data. For example, the required material data includes copper material: 35200 kg, insulating paint: 50 L, and the existing material data includes copper material: 15000 kg, insulating paint: 8000 L, then the existing material data does not meet the required material data.

[0042] S302: If the existing material data does not meet the required material data, a corresponding warning message is issued.

[0043] For example, when the existing material data does not meet the required material data, a warning message is generated (including the order ID, the type of short supply material, and the short supply quantity, such as 20,200 kg of copper material is short in order 001), and the SMTP protocol is used to send it to the purchasing department's mailbox or a warning window pops up in the MES system operation interface.

[0044] Through the above steps S301 to S302, the existing material data is compared with the required material data to avoid production line shutdowns due to material shortages and ensure production continuity. The warning mechanism prompts the purchasing department to replenish stocks in a timely manner, reduce redundant inventory, and lower inventory costs.

[0045] In one possible implementation, see Figure 2 S300, the equipment loss data includes the vibration frequency, temperature and / or current of the production equipment. When the existing material data meets the required material data and the equipment loss data does not exceed the loss threshold, before obtaining the quality data and energy consumption data of the wire material in each production link, the method further includes: S303: Build an isolation tree based on the equipment loss data.

[0046] For example, historical equipment wear and tear data can be obtained, and a feature (such as temperature) and a split value (such as 7°C) can be randomly selected to partition the data space into two subspaces. Within each subspace, features and split values ​​can be repeatedly randomly selected until a termination condition (such as a unique data point or the tree depth reaches a limit) is met. Multiple isolation trees (e.g., 100 trees) are then repeatedly constructed to form an isolation forest. The path length (number of splits from the root node to a leaf node) of the equipment wear and tear data in each tree is recorded. For example, for device M003, the average path length in 100 isolation trees is 4.2.

[0047] S304: Obtain an anomaly score based on the isolation tree.

[0048] For example, the anomaly score can be calculated based on the isolation tree. For example, the anomaly score s = , where E(h(x)) is the average path length in the isolation forest, c(n) is the corrected average path length for sample number n, c(n)=2H(n−1)−2(n−1) / n, where H(i) is the harmonic number. The E(h(x)) of device M003 in the equipment loss data is 4.2, and the sample number n=3, then c(3)=2(1 / 1+1 / 2)−2(2 / 3)=2(1.5)−1.33=1.67, s=2 −4.2 / 1.67 =2 −2.514 =0.175.

[0049] S305: Determine whether the equipment loss data exceeds a loss threshold based on the anomaly score.

[0050] For example, when the anomaly score s>0.6, the equipment loss data is determined to have exceeded the loss threshold; when the anomaly score s≤0.6, the equipment loss data is determined to have not exceeded the loss threshold. When the anomaly score of the equipment loss data exceeds the preset threshold, a graded response strategy can be adopted. For example, when the anomaly score s≤0.9, the equipment is immediately shut down; when 0.7≤s<0.9, the equipment is scheduled for maintenance; and when 0.6≤s<0.7, the frequency of equipment loss data collection is increased.

[0051] Through the above steps S303 to S305, an isolation tree is constructed based on the equipment loss data, and an intelligent assessment of equipment loss is achieved through the isolation forest algorithm. The degree to which the equipment status deviates from the normal mode is quantified through the anomaly score, realizing adaptive anomaly identification, multi-parameter coupling analysis and real-time response. This enables equipment loss management to shift from passive maintenance to active prediction, thereby extending equipment life and reducing unplanned downtime losses.

[0052] S400: When the quality data is not within the standard range, a first adjustment range is obtained according to the production order.

[0053] For example, if the defect area ratio is greater than 2% or the wire diameter exceeds tolerance, historical production data is obtained. Based on this historical data, the defect rate changes by 0.3% for every 1m / min increase or decrease in the drawing speed. The current drawing speed is 20m / min, with an allowable adjustment range of ±3m / min (i.e., 17-23m / min). The wire diameter fluctuates by 0.001mm for every 5°C change in the annealing temperature. The current annealing temperature is 500°C, with an allowable adjustment range of ±10°C (490-510°C). For example, if the defect area ratio of a batch of enameled wire is 2.2%, the drawing speed needs to be reduced by (2.2-2) / 0.3≈0.67m / min, resulting in an adjustment range of 20-0.67=19.33m / min, but subject to the ±3m / min limit, the final range is 17-20m / min.

[0054] In one possible implementation, see Figure 2S400: The production order includes specifications, quantity, and deadline. If the quality data is not within the standard range, before obtaining the first adjustment range according to the production order, the method further includes: S401: Determine whether the quality data of the wire material in each production link is within the standard range based on the specifications in the production order.

[0055] For example, data can be collected in real time by equipment such as laser diameter gauges and resistance testers. For example, the actual measured value of the wire diameter is 0.502mm. The deviation between the measured value and the tolerance band is calculated. The wire diameter deviation = |0.502-0.500| = 0.002mm<0.005mm, so the wire diameter is within the standard range.

[0056] S402: If the quality data of the wire material in the production process is within the standard range, determine whether the energy consumption data exceeds the energy consumption threshold.

[0057] It can be understood that the energy consumption threshold is that the energy consumption data does not exceed a preset specified value. The preset specified value can be set by ordinary technicians in this field according to actual needs and is not a sole limitation here.

[0058] For example, a reasonable energy consumption threshold can be determined based on historical data and industry standards. For example, the energy consumption threshold for the wire drawing process is 12.0 kWh / kg. After a batch of wire passes quality inspection, the energy consumption of the wire drawing process is determined to be 12.5 kWh / kg, exceeding the energy consumption threshold.

[0059] Through steps S401 to S402, the quality of wire materials at each stage is verified to be up to standard based on the production order specifications. Further energy consumption data is only checked for stages that meet quality standards, avoiding ineffective energy consumption analysis for stages that fail to meet quality standards. By using dual thresholds for quality and energy consumption, quality-energy consumption synergy optimization is achieved. Combining energy consumption and quality data from production stages allows for rapid identification of the root causes of energy consumption anomalies, facilitating energy efficiency improvements while ensuring product quality.

[0060] Optionally, see Figure 2 , the method further comprises: S4021: If the energy consumption data does not exceed the energy consumption threshold, continue production and update the equipment loss data.

[0061] For example, a production continuation instruction can be sent through the MES (Manufacturing Execution System) interface, including the current order ID, batch number, and production link identifier (such as "wire drawing - 3rd pass"), and the current production parameters (such as drawing speed, annealing temperature) are marked as "verified". The equipment operation data is obtained from the SCADA system, and a linear accumulation model (such as loss rate = ,in, t is the duration of this production, K负载 is the load factor) to calculate the loss rate and write the calculated loss rate into the equipment file table in the database.

[0062] Through step S4021, the production process is seamlessly integrated, avoiding unnecessary downtime due to energy consumption checks. Continuously updating equipment wear data provides a foundation for equipment life prediction and fault warnings. Real-time equipment wear data provides a basis for preventive maintenance. Updated equipment wear data is directly fed back into the isolation tree in S303, forming a closed loop of "production-assessment-optimization."

[0063] In one possible implementation, see Figure 2 S400: The first adjustment range is a parameter value range that ensures that the production completion time is within the time limit of the production order. If the quality data is not within the standard range, the first adjustment range is obtained according to the production order, including: S410: When the quality data is not within the standard range, the production time of each production link is determined according to the deadline and each preset production parameter.

[0064] For example, the delivery deadline (e.g., 2025-05-30 18:00) and the current system time (e.g., 2025-05-23 14:00) can be extracted from the production order to calculate the remaining available time, obtain the standard time and parameter sensitivity coefficient of each production link (e.g., for every 1 m / min increase in stretching speed, the time decreases by 5%), and adopt mixed integer linear programming (MILP) with the decision variables being the adjustment amount of the preset production parameters of each production link. The maximum allowable production time of each production link, i.e., the production time of each production link, is output.

[0065] S420: Obtain a first adjustment range of a corresponding production parameter according to the production time.

[0066] For example, Monte Carlo simulation can be used according to the production time of each production link, randomly sampling parameter combinations of each production link, statistically analyzing the probability of meeting the time constraint and considering the limits of the equipment in each production link with a safety margin, to generate a confidence interval of the production parameters, i.e., the first adjustment range.

[0067] Through steps S410 to S420, a precise decision-making system for quality repair is established through reverse derivation of delivery constraints and multi-parameter coupling modeling. This allows companies to minimize the impact of production adjustments on delivery while ensuring quality. Furthermore, by limiting the boundaries of parameter adjustments, excessive optimization can be prevented from causing equipment overload or secondary quality issues.

[0068] S500: Adjust corresponding preset production parameters according to the quality data within a first adjustment range.

[0069] For example, the parameter adjustment value can be determined based on the product of the quality deviation and the adjustment coefficient. For example: stretching speed adjustment = defect area deviation × adjustment coefficient = (2.2% - 2%) × (1 / 0.3%) ≈ 0.67 m / min. Adjust the stretching speed from 20 m / min to 19.33 m / min, and record the adjustment log.

[0070] In one possible implementation, see Figure 2 , S500, within the first adjustment range, adjusting the corresponding preset production parameters according to the quality data, including: S510, determining an objective function according to the deviation between the quality data and the standard range.

[0071] For example, the deviation of the quality data from the standard range can be used as an objective function, and the production parameters can be optimized by minimizing the deviation.

[0072] S520: Obtain the gradient of each preset production parameter according to the objective function.

[0073] For example, the back propagation algorithm (for a neural network model) or the numerical differentiation method can be used to calculate the gradient of the objective function with respect to each production parameter, and the production parameters can be iteratively updated according to the update rule of the gradient descent method.

[0074] S530: Update the preset production parameters according to the gradients.

[0075] Exemplarily, the gradient calculation and parameter updating steps can be repeated until the objective function value is less than a preset convergence threshold or the maximum number of iterations is reached. When the objective function value is less than the convergence threshold, the parameter combination obtained at this time is the updated production parameter.

[0076] Through steps S510 to S530, the objective function (e.g., minimizing the sum of squared deviations) is directly defined based on the deviation of quality data from the standard range (e.g., dimensional deviations, substandard performance, etc.). Production parameters are then adjusted through gradient calculation and backpropagation, enabling intelligent adjustment of production parameters. This allows companies to reduce adjustment costs and improve parameter optimization efficiency while ensuring quality. Gradient calculations (e.g., differentiation or numerical approximation) clarify the sensitivity of each production parameter to quality deviations, enabling targeted adjustments and balancing multiple conflicting objectives.

[0077] S600: When the quality data is within the standard range and the energy consumption data exceeds the energy consumption threshold, a second adjustment range is obtained according to the first adjustment range.

[0078] For example, if the energy consumption per unit of output exceeds a threshold (e.g., 15 kWh / kg), the first adjustment range is further restricted. Based on the first adjustment range, the parameter boundaries can be narrowed based on the energy consumption data. For example, for every 1 m / min decrease in stretching speed, energy consumption decreases by 0.5 kWh / kg, but the lower limit of stretching speed is tightened from 17 m / min to 18 m / min. For every 5°C increase in annealing temperature, energy consumption increases by 0.3 kWh / kg, and the upper limit of temperature is tightened from 510°C to 505°C. The stretching speed in the first adjustment range is 17-23 m / min, and after narrowing, it is 18-20 m / min in the second adjustment range. The annealing temperature in the first adjustment range is 490-510°C, and after narrowing, it is 495-505°C in the second adjustment range.

[0079] In one possible implementation, see Figure 2 S600: When the quality data is within the standard range and the energy consumption data exceeds the energy consumption threshold, a second adjustment range is obtained according to the first adjustment range, including: S610: Randomly generate multiple sets of production parameters within a first adjustment range and input them into an energy consumption model and a quality model to obtain energy consumption values ​​and quality scores.

[0080] Exemplarily, parameter boundaries can be extracted from the first adjustment range, and Latin hypercube sampling (LHS) can be used to generate uniformly distributed parameter combinations in the parameter space. A multivariate linear regression model can be trained based on historical production data to obtain an energy consumption model. A support vector machine (SVM) classifier can be used to obtain a quality model. The input parameters are combined into the energy consumption model and the quality model, and the energy consumption value and quality score are output.

[0081] S620, obtaining fitness according to the energy consumption value and the quality score.

[0082] For example, the energy consumption value and quality score can be mapped to the interval [0,1], and then a linear weighting method is used to balance energy consumption and quality. For example, the fitness F= E (1-E)+ Q Q, where E is the normalized energy consumption value and Q is the normalized quality score.

[0083] S630: Obtain a non-dominated solution according to the fitness.

[0084] For example, the dominance relationship between parameter combinations can be compared based on energy consumption and quality scores (parameter combination A dominates parameter combination B if and only if A's energy consumption ≤ B and its quality score ≥ B, and at least one objective is strictly better). Non-dominated solutions can then be obtained based on fitness.

[0085] S640: gradually approaching the Pareto frontier according to the non-dominated solution, and determining the Pareto frontier as a second adjustment range.

[0086] For example, the first adjustment range can be adjusted based on the non-dominated solution (e.g., the speed range can be narrowed to 18–22 m / min), and a new solution can be generated until the frontier surface changes by <1% (convergence). An energy consumption-mass scatter plot is drawn, and the non-dominated solutions are connected to form a Pareto frontier. The common range is extracted from the frontier surface parameters as the second adjustment range.

[0087] Through steps S610 to S640, random sampling and model prediction are used to generate candidate solutions for the conflicting objectives of energy consumption and quality, gradually approaching the Pareto frontier (i.e., the set of optimal trade-off solutions between energy consumption and quality). This sampling-based multi-objective optimization establishes an efficient mechanism for exploring production parameters, improving optimization efficiency, enhancing decision-making transparency, and dynamic convergence capabilities, enabling enterprises to quickly find the energy-quality balance point under complex constraints.

[0088] S700: Adjust the preset production parameters according to the second adjustment range.

[0089] For example, within the second adjustment range, with the goals of achieving quality standards and minimizing energy consumption, the optimal parameter combination can be selected through weighted scoring. For example, if the stretching speed is 19 m / min and the annealing temperature is 500°C, the quality score is 95, the energy score is 85, and the overall score is 90; if the stretching speed is 18.5 m / min and the annealing temperature is 505°C, the quality score, energy score, and overall score are 90. The combination with the highest overall score (e.g., 19 m / min and 500°C) is selected and updated to the production line.

[0090] In one possible implementation, see Figure 2 , S700, adjusting the preset production parameters according to the second adjustment range, including: S710: Normalize the energy consumption values ​​and quality scores of each production parameter within the second adjustment range, and calculate the distance between the energy consumption values ​​and quality scores of each set of production parameters and the positive ideal solution and the negative ideal solution to obtain a relative proximity. The positive ideal solution includes the minimum energy consumption value and the maximum quality score, and the negative ideal solution includes the maximum energy consumption value and the minimum quality score.

[0091] Exemplarily, the energy consumption values ​​and quality scores of each production parameter in the second adjustment range can be normalized, and the Euclidean distance between the energy consumption values ​​and quality scores of each group of production parameters and the positive ideal solution and the negative ideal solution can be calculated. The relative proximity of the energy consumption values ​​and quality scores of each group of production parameters to the positive ideal solution can be obtained based on the distance between the energy consumption values ​​and quality scores of each group of production parameters and the positive ideal solution and the negative ideal solution.

[0092] S720: Adjust the preset production parameters according to the production parameter corresponding to the maximum relative proximity.

[0093] For example, each preset production parameter may be adjusted accordingly to a parameter combination with the greatest relative proximity.

[0094] Through steps S710 to S720, the production parameter combinations within the second adjustment range are normalized, and the Euclidean distance between each parameter set and the positive ideal solution (lowest energy consumption, highest quality) and the negative ideal solution (highest energy consumption, lowest quality) is calculated. Ultimately, the overall performance of each parameter combination is quantified using relative proximity. This approach enables unbiased multi-objective decision-making using positive and negative ideal solutions, establishing a quantitative framework for multi-objective decision-making. This improves decision-making objectivity, dynamic adaptability, and computational efficiency, enabling enterprises to shorten decision-making cycles in complex multi-objective scenarios.

[0095] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0096] Corresponding to the intelligent control method of the enameled wire production line described in the above embodiment, the embodiment of the present application also provides an intelligent control system for an enameled wire production line, and each module of the device can implement each step of the intelligent control method of the enameled wire production line. Figure 3 The structural block diagram of the intelligent control system of the enameled wire production line provided in an embodiment of the present application is shown. For the sake of convenience, only the parts related to the embodiment of the present application are shown.

[0097] Reference Figure 3 , the system comprises: The first acquisition module is used to obtain the production order of each batch of enameled wire and the existing resource information of each production link; wherein the existing resource information includes existing material data and equipment loss data; A material module, used to determine the required material data according to the priority of each production order; A second acquisition module is configured to acquire quality data and energy consumption data of the wire material in each of the production links when the existing material data meets the required material data and the equipment loss data does not exceed a loss threshold; wherein the quality data includes surface defect data and specifications; a first adjustment range module, configured to obtain a first adjustment range according to the production order when the quality data is not within a standard range; a first adjustment module, configured to adjust corresponding preset production parameters according to the quality data within the first adjustment range; A second adjustment range module is configured to obtain a second adjustment range according to the first adjustment range when the quality data is within the standard range and the energy consumption data exceeds an energy consumption threshold; The second adjustment module is used to adjust the preset production parameters according to a second adjustment range.

[0098] It should be noted that the information interaction, execution process and other contents between the above modules are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0099] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0100] The embodiment of the present application also provides an intelligent control device for an enameled wire production line, Figure 4 This is a schematic diagram of the structure of the intelligent control device for the enameled wire production line provided in one embodiment of the present application. Figure 4 As shown, the intelligent control device 6 for the enameled wire production line of this embodiment includes: at least one processor 60 ( Figure 4 Only one is shown), at least one memory 61 ( Figure 4 Only one is shown in the figure) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, the enameled wire production line intelligent control device 6 implements the steps of any of the above-mentioned enameled wire production line intelligent control method embodiments, or implements the functions of each module / unit in the above-mentioned device embodiments.

[0101] For example, the computer program 62 can be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program 62 in the enameled wire production line intelligent control device 6.

[0102] The intelligent control device 6 of the enameled wire production line can be a computing device such as a desktop computer, a notebook, a palmtop computer, a cloud server, etc. The intelligent control device of the enameled wire production line can include, but is not limited to, a processor 60 and a memory 61. It can be understood by those skilled in the art that Figure 4 It is only an example of the intelligent control device 6 for the enameled wire production line and does not constitute a limitation on the intelligent control device 6 for the enameled wire production line. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, buses, etc.

[0103] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0104] In some embodiments, the memory 61 can be an internal storage unit of the enameled wire production line intelligent control device 6, such as a hard drive or memory within the device. In other embodiments, the memory 61 can also be an external storage device within the device, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the memory 61 can include both the internal storage unit and an external storage device within the device. The memory 61 is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of a computer program. The memory 61 can also be used to temporarily store data that has been output or is about to be output.

[0105] An embodiment of the present application further 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 in any of the above method embodiments are implemented.

[0106] An embodiment of the present application provides a computer program product. When the computer program product runs on an intelligent control device for an enameled wire production line, the intelligent control device for the enameled wire production line implements the steps of any of the above-mentioned method embodiments.

[0107] If the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process steps in the above-mentioned method embodiments by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to the intelligent control equipment of the enameled wire production line, a recording medium, computer memory, read-only memory (ROM), random access memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Examples include a USB flash drive, a removable hard drive, a magnetic disk, or an optical disk.

[0108] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0109] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0110] In the embodiments provided in this application, it should be understood that the disclosed intelligent control device and method for an enameled wire production line can be implemented in other ways. For example, the embodiments of the intelligent control device for an enameled wire production line described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0111] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0112] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. An intelligent control method for an enameled wire production line, characterized in that: include: Obtaining production orders for each batch of enameled wire and existing resource information for each production link; wherein the existing resource information includes existing material data and equipment loss data; Determining required material data based on the priority of each production order; When the existing material data meets the required material data and the equipment loss data does not exceed the loss threshold, obtaining the quality data and energy consumption data of the wire material in each production link; wherein the quality data includes surface defect data and specifications; When the quality data is not within a standard range, obtaining a first adjustment range according to the production order; Within the first adjustment range, adjusting corresponding preset production parameters according to the quality data; When the quality data is within the standard range and the energy consumption data exceeds the energy consumption threshold, a second adjustment range is obtained according to the first adjustment range; The preset production parameter is adjusted according to a second adjustment range.

2. The intelligent control method for an enameled wire production line according to claim 1, characterized in that: Before obtaining the quality data and energy consumption data of the wire material in each production link when the existing material data meets the required material data and the equipment loss data does not exceed the loss threshold, the method further includes: Determining whether the existing material data meets the required material data; If the existing material data does not meet the required material data, a corresponding warning message is issued.

3. The intelligent control method for an enameled wire production line according to claim 1, wherein: The equipment loss data includes vibration frequency, temperature and / or current of the production equipment. When the existing material data satisfies the required material data and the equipment loss data does not exceed the loss threshold, before obtaining the quality data and energy consumption data of the wire material in each production link, the method further includes: constructing an isolation tree based on the equipment loss data; Obtaining an anomaly score based on the isolation tree; It is determined whether the equipment loss data exceeds the loss threshold according to the abnormality score.

4. The intelligent control method for an enameled wire production line according to claim 1, wherein: The production order includes specifications, quantity, and deadline. When the quality data is not within the standard range, before obtaining the first adjustment range according to the production order, the method further includes: Determining whether the quality data of the wire material in each production link is within the standard range according to the specifications in the production order; If the quality data of the wire material in the production link is within the standard range, it is determined whether the energy consumption data exceeds the energy consumption threshold.

5. The intelligent control method for an enameled wire production line according to claim 4, characterized in that: The method further comprises: If the energy consumption data does not exceed the energy consumption threshold, continue production and update the equipment loss data.

6. The intelligent control method for an enameled wire production line according to claim 1, characterized in that: The first adjustment range is a parameter value range that ensures that the production completion time is within the time limit of the production order. When the quality data is not within the standard range, the first adjustment range is obtained according to the production order, including: If the quality data is not within the standard range, determining the production time of each production link according to the deadline and each preset production parameter; A first adjustment range of a corresponding production parameter is obtained according to the production time.

7. The intelligent control method for an enameled wire production line according to claim 1, wherein: Adjusting corresponding preset production parameters according to the quality data within the first adjustment range includes: determining an objective function based on the deviation of the quality data from the standard range; Obtaining the gradient of each of the preset production parameters according to the objective function; The preset production parameters are updated according to the gradients.

8. The intelligent control method for an enameled wire production line according to claim 1, wherein: When the quality data is within the standard range and the energy consumption data exceeds the energy consumption threshold, obtaining a second adjustment range according to the first adjustment range includes: Within the first adjustment range, randomly generate multiple sets of production parameters and input them into the energy consumption model and the quality model to obtain energy consumption values ​​and quality scores; Obtaining fitness according to the energy consumption value and the quality score; Obtaining a non-dominated solution according to the fitness; The Pareto front is gradually approached according to the non-dominated solution, and the Pareto front is determined as the second adjustment range.

9. The intelligent control method for an enameled wire production line according to claim 1, wherein: The adjusting the preset production parameter according to the second adjustment range includes: Normalizing the energy consumption values ​​and quality scores of each production parameter within the second adjustment range, and calculating the distance between the energy consumption values ​​and quality scores of each set of production parameters and a positive ideal solution and a negative ideal solution to obtain relative proximity; wherein the positive ideal solution includes the minimum energy consumption value and the maximum quality score, and the negative ideal solution includes the maximum energy consumption value and the minimum quality score; The preset production parameters are adjusted according to the production parameter corresponding to the largest relative proximity.

10. An intelligent control system for an enameled wire production line, characterized in that: include: The first acquisition module is used to obtain the production order of each batch of enameled wire and the existing resource information of each production link; wherein the existing resource information includes existing material data and equipment loss data; A material module, used to determine the required material data according to the priority of each production order; A second acquisition module is configured to acquire quality data and energy consumption data of the wire material in each of the production links when the existing material data meets the required material data and the equipment loss data does not exceed a loss threshold; wherein the quality data includes surface defect data and specifications; a first adjustment range module, configured to obtain a first adjustment range according to the production order when the quality data is not within a standard range; a first adjustment module, configured to adjust corresponding preset production parameters according to the quality data within the first adjustment range; A second adjustment range module is configured to obtain a second adjustment range according to the first adjustment range when the quality data is within the standard range and the energy consumption data exceeds an energy consumption threshold; The second adjustment module is used to adjust the preset production parameters according to a second adjustment range.

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