Real-time processing method, device and equipment for copper alloy wire production process data

Abstract

The invention is suitable for the technical field of copper alloy wire production, and particularly relates to a real-time data processing method, device and equipment in the copper alloy wire production process, and the method comprises the following steps: firstly, obtaining process parameters, design parameters and operation parameters of a copper alloy wire in process nodes of wire drawing equipment; secondly, based on the operation parameters of the process nodes and the positions of the process nodes in the process flow, obtaining an operation parameter vector set; generating a plurality of deviation diameter data based on the actually measured diameter and the preset diameter of the mold; based on the deviation diameter data and the operation parameter vector set, fine tuning operation data are obtained; and the input parameters of the wire drawing equipment are adjusted in real time. And the operation parameters of each process node are regulated and controlled in real time, so that process errors are reduced, the tolerance of the product is finally reduced, and the superior product rate is improved.

Description

Technical Field

[0001] This application belongs to the field of copper alloy wire production technology, and in particular relates to a method, apparatus and equipment for real-time data processing in the copper alloy wire production process. Background Technology

[0002] Copper alloy wire is widely used in electronics, power, and communications due to its excellent electrical, thermal, and mechanical properties. Its production process typically includes multiple stages such as smelting, casting, drawing, and annealing. Among these, the wire drawing process is the key step that determines the final quality of the copper alloy wire. Copper alloy wire requires multiple drawing passes, and the operating parameters of each pass (such as tensile force, speed, and temperature) directly affect the wire diameter accuracy.

[0003] Traditional methods for testing the quality of copper alloy wire drawing rely solely on adjusting the wire diameter of the final drawing pass. However, deviations can occur in each drawing pass, leading to a low yield of high-quality products. Summary of the Invention

[0004] This application provides a method, apparatus, and equipment for real-time data processing in the copper alloy wire production process, which can solve the problem that deviations in each stretching pass in the stretching process of the copper alloy production line may lead to a poor final yield of high-quality products.

[0005] In a first aspect, embodiments of this application provide a method for real-time data processing in the production process of copper alloy wire, including: The process parameters, design parameters, and operating parameters of the copper alloy wire at the process nodes of the wire drawing equipment are obtained. Among them, the process node includes at least one position node, and each position node is the position node where the wire drawing equipment draws the copper alloy wire each time. The process parameter is the actual measured diameter collected by the mobile laser diameter measuring instrument. The design parameter is the preset diameter of the mold. The operating parameter is the input parameter of the wire drawing equipment. Based on the operating parameters of the process nodes and their positions in the process flow, a set of operating parameter vectors is obtained; where the parameter vector set represents an ordered set of input parameters of each process node of the wire drawing equipment. Multiple deviation diameter data are generated based on the actual measured diameter and the mold preset diameter; among them, the deviation diameter data is used to reflect the deviation value between the actual diameter and the theoretical diameter during the copper alloy wire production process; Based on the deviation diameter data and the set of operating parameter vectors, fine-tuning operating data is obtained; among which, the fine-tuning operating data is used to instruct the wire drawing equipment to correct the set of input parameters in real time. Based on the fine-tuning operation data, the input parameters of the wire drawing equipment are adjusted in real time.

[0006] The technical solutions described in this application embodiment have at least the following technical effects: The real-time data processing method for copper alloy wire production provided in this application first obtains the process parameters, design parameters, and operating parameters of the copper alloy wire at the process nodes of the drawing equipment. Second, based on the operating parameters of the process nodes and their positions in the process flow, a set of operating parameter vectors is obtained. Then, multiple deviation diameter data are generated based on the actual measured diameter and the preset diameter of the mold. Next, fine-tuning operating data is obtained based on the deviation diameter data and the set of operating parameter vectors. Finally, based on the fine-tuning operating data, the input parameters of the drawing equipment are adjusted in real time. Through the above method, the set of operating parameter vectors for each process node is first obtained. Then, deviation diameter data is obtained through the actual measured diameter and the preset diameter of the mold. Fine-tuning operating data is obtained through the set of operating parameter vectors and the deviation diameter data. Finally, the operating parameters of each process node are adjusted in real time based on the fine-tuning operating data. By adjusting the operating parameters of each process node in real time, process errors are reduced, ultimately resulting in reduced product tolerance and increased yield.

[0007] In one possible implementation of the first aspect, a set of operating parameter vectors is obtained based on the operating parameters of the process nodes and the position of the process nodes in the process flow, including: Based on the process nodes, determine the location information of the process nodes in the process flow; The location information is vector-mapped to obtain the process location vector of the process node; where the process location vector is used to represent the sequential position of the process node in the entire process flow. By fusing the process location vector and the operating parameters of the process nodes, a set of operating parameter vectors is obtained.

[0008] In one possible implementation of the first aspect, multiple deviation diameter data are generated based on the actual measured diameter and the preset diameter of the mold, including: Based on the actual measured diameter, the actual diameter curve is obtained; Based on the preset diameter of the mold, the design diameter curve is obtained; Based on the actual diameter curve and the actual diameter curve, the deviation diameter curve is obtained; Based on the deviation diameter curve, multiple deviation diameter data are obtained.

[0009] In one possible implementation of the first aspect, multiple deviation diameter data are obtained based on the deviation diameter curve, including: Based on the deviation diameter curve, a sliding window technique is used for discretization to obtain a denoised curve; the denoised curve is the data curve after eliminating equipment vibration interference. Based on the denoising curve, multiple deviation diameter data were obtained.

[0010] In one possible implementation of the first aspect, fine-tuning operation data is obtained based on the deviation diameter data and the set of operating parameter vectors, including: Based on the deviation diameter data, a diameter deviation sequence is obtained; where the diameter deviation sequence is the difference sequence between the reference diameter and the measured diameter at the corresponding process node; The diameter deviation sequence is mapped to the set of operating parameter vectors to obtain the diameter deviation influence vector set; the diameter deviation influence vector set is used to reflect the correlation between the deviation diameter data of copper alloy wire and each operating parameter. Based on the set of diameter deviation influence vectors, fine-tuning operation data is obtained.

[0011] In one possible implementation of the first aspect, the diameter deviation sequence is mapped to a set of running parameter vectors to obtain a set of diameter deviation influence vectors, including: Based on the diameter deviation sequence and the set of operating parameter vectors, a corrected operating parameter vector is obtained; wherein, the corrected operating parameter vector is a vector that aligns the diameter deviation sequence and the operating parameter vector in the time dimension. Based on the corrected operating parameter vector and the preset mold diameter, a linear influence vector is obtained; where the linear influence vector is a set of vectors representing the degree of linear influence of the operating parameters on the deviation diameter data; An anomaly correlation vector is obtained from the fault mode library based on the knowledge graph; where the anomaly correlation vector is a set of vectors representing the correlation strength between operating parameters and deviation diameter data; the fault mode library of the knowledge graph is obtained from historical fault data; Based on the abnormal correlation vector and the linear influence vector, the set of diameter deviation influence vectors is obtained.

[0012] In one possible implementation of the first aspect, an anomaly association vector is obtained based on a fault mode library using a knowledge graph, including: Based on a knowledge graph-based fault mode library, historical abnormal operating parameters are extracted; where historical abnormal operating parameters are operating parameters under abnormal conditions. Based on historical abnormal operating parameters, the FP-Growth algorithm is used to obtain support and confidence scores. Support reflects the frequency of occurrence of historical abnormal operating parameters in the fault mode library, and confidence scores reflect the probability that historical abnormal operating parameters are greater than a threshold. Based on support and confidence, anomaly association vectors are obtained.

[0013] In one possible implementation of the first aspect, the input parameters of the wire drawing device are adjusted in real time based on fine-tuning operation data, including: Based on the fine-tuning operation data, operation parameter correction instructions are obtained; among them, operation parameter correction instructions are instructions that the control system of the wire pulling equipment can recognize and execute. The input parameters of the wire drawing equipment are adjusted in real time according to the operation parameter correction instructions.

[0014] Secondly, embodiments of this application provide a real-time data processing device for copper alloy wire production process, applied to wire drawing equipment, for implementing the real-time data processing method for copper alloy wire production process as described in the first aspect above, the real-time data processing device for copper alloy wire production process includes: The acquisition unit is used to acquire the process parameters, design parameters, and operating parameters of the copper alloy wire at the process nodes of the wire drawing equipment; wherein, the process node includes at least one position node, and each position node is the position node where the wire drawing equipment stretches the copper alloy wire each time; the process parameter is the actual measured diameter collected by the mobile laser diameter measuring instrument; the design parameter is the preset diameter of the mold; and the operating parameter is the input parameter of the wire drawing equipment. The position unit is used to obtain a set of operating parameter vectors based on the operating parameters of the process nodes and the position of the process nodes in the process flow; wherein, the set of parameter vectors represents an ordered set of input parameters of each process node of the wire drawing equipment; The deviation unit is used to generate multiple deviation diameter data based on the actual measured diameter and the preset diameter of the mold; among them, the deviation diameter data is used to reflect the deviation value between the actual diameter and the theoretical diameter during the copper alloy wire production process; The fine-tuning unit is used to obtain fine-tuning operation data based on the deviation diameter data and the set of operating parameter vectors; wherein, the fine-tuning operation data is used to instruct the wire drawing equipment to correct the set of input parameters in real time; The results unit is used to adjust the input parameters of the wire drawing equipment in real time based on the fine-tuning operation data.

[0015] Thirdly, embodiments of this application provide a wire drawing device, including a wire drawing device body, a controller electrically connected to the wire drawing device body, and a mobile laser diameter measuring instrument disposed on the wire drawing device body. The controller includes a memory, a processor, and a computer program stored in the memory and executable on the processor.

[0016] Fourthly, embodiments of this application provide a computer program product that, when run on a terminal device, causes the terminal device to execute the real-time data processing method for copper alloy wire production process described in any of the first aspects above.

[0017] It is understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a method for real-time data processing in the production process of copper alloy wire according to an embodiment of this application. Figure 2 This is a schematic diagram of the operation of a real-time data processing method for copper alloy wire production process provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of the real-time data processing device for the copper alloy wire production process provided in the embodiments of this application; Figure 4 This is a schematic diagram of the structure of the wire drawing device provided in the embodiments of this application. Detailed Implementation

[0020] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0021] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0022] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0023] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0024] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0025] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of 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 "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0026] In related technologies, copper alloy wire is a material used in construction and industrial production. The surface of the wire should be smooth and clean, free from cracks, burrs, rough draw marks, folds, and inclusions. Minor, localized defects such as scratches, blemishes, dents, and indentations are permissible, as long as they do not cause the wire diameter to exceed permissible deviations.

[0027] Traditional methods for testing the quality of copper alloy wire drawing rely solely on adjusting the wire diameter of the final drawing pass. However, deviations can occur in each drawing pass, leading to a low yield of high-quality products.

[0028] To address the aforementioned issues, this application provides a real-time data processing method for copper alloy wire production. This method first acquires the process parameters, design parameters, and operating parameters of the copper alloy wire at each process node in the drawing equipment. Second, based on the operating parameters of the process nodes and their positions in the process flow, a set of operating parameter vectors is obtained. Then, multiple deviation diameter data are generated based on the actual measured diameter and the mold's preset diameter. Next, fine-tuning operating data is obtained based on the deviation diameter data and the set of operating parameter vectors. Finally, based on the fine-tuning operating data, the input parameters of the drawing equipment are adjusted in real-time. Through this method, the operating parameter vector set for each process node is first obtained. Then, deviation diameter data is obtained through the actual measured diameter and the mold's preset diameter. Fine-tuning operating data is obtained through the set of operating parameter vectors and the deviation diameter data. Finally, the operating parameters of each process node are adjusted in real-time based on the fine-tuning operating data. By adjusting the operating parameters of each process node in real-time, process errors are reduced, ultimately leading to reduced product tolerances and increased yield.

[0029] The real-time data processing method for copper alloy wire production process provided in this application embodiment can be applied to wire drawing equipment. In this case, the wire drawing equipment is the executing entity of the real-time data processing method for copper alloy wire production process provided in this application embodiment. This application embodiment does not impose any restrictions on the specific type of wire drawing equipment.

[0030] For example, a wire drawing device includes a drawing device body, a mobile laser diameter gauge, and a controller. The controller is electrically connected to the mobile laser diameter gauge, which is located on the outside of the drawing device body and is a movable testing device. The mobile laser diameter gauge uses a high-precision linear guide rail and wear-resistant slider to ensure smooth movement along the wire axis. A servo motor drives a synchronous belt to achieve high-speed movement of the diameter gauge. An absolute encoder is installed on the servo motor shaft to provide real-time feedback of the mobile laser diameter gauge's position information to the controller. The mobile laser diameter gauge uses laser scanning technology to non-contactly measure the actual diameter of the copper alloy wire. It can move synchronously with the copper alloy wire to track changes in the actual diameter at the same location. The controller controls the mobile laser diameter gauge to move synchronously with the copper alloy wire in the drawing device body and collects the actual diameter at the same location of the copper alloy wire at each process node. The actual measured diameter is used to calculate the deviation diameter data compared with the preset diameter of the mold. To better understand the real-time data processing method for copper alloy wire production process provided in this application embodiment, the specific implementation process of the real-time data processing method for copper alloy wire production process provided in this application embodiment will be described by way of example below.

[0031] Figure 1 This is a flowchart illustrating a method for real-time data processing in the production process of copper alloy wire according to an embodiment of this application. Figure 2 This is a schematic diagram illustrating the operation of a real-time data processing method for copper alloy wire production process provided in an embodiment of this application. The real-time data processing method for copper alloy wire production process includes: S100: Obtain the process parameters, design parameters, and operating parameters of the copper alloy wire at the process nodes of the wire drawing equipment. The process nodes include at least one position node, each position node being the location where the wire drawing equipment draws the copper alloy wire each time; the process parameters are the actual measured diameter collected by the mobile laser diameter gauge; the design parameters are the preset diameter of the mold; and the operating parameters are the input parameters of the wire drawing equipment.

[0032] In the production of copper alloy wire, a process node refers to a specific location at the die exit, 10 times the diameter of the wire from that pass, after each drawing pass. This location is crucial for real-time monitoring of production data. For example, if the copper alloy wire diameter is 2mm after a drawing pass, then 20mm from the die exit is the corresponding process node. Process parameters are the actual diameter values ​​of the copper alloy wire collected periodically (every hour is one cycle) at the process node by a mobile laser diameter gauge. These parameters reflect the actual diameter of the copper alloy wire during production. For example, if the mobile laser diameter gauge measures a copper alloy wire diameter of 1.99mm at a process node, this 1.99mm is the process parameter for that moment. Design parameters are the theoretical diameter values ​​that the copper alloy wire should achieve at the corresponding process node, preset by the system based on production standards and die specifications. These are the target size benchmarks for production. For example, if the system presets a copper alloy wire diameter of 2.00mm for a process node, this 2.00mm is the design parameter for that node. Operating parameters are inputs into the wire drawing equipment to control its stretching action, speed, force, and other operating states, directly affecting the stretching effect and final dimensions of the copper alloy wire. Examples include the stretching speed (e.g., 3 m / s) and stretching pressure (e.g., 150 N).

[0033] S200, based on the operating parameters of the process nodes and their positions in the process flow, obtains a set of operating parameter vectors. This set of parameter vectors represents an ordered set of input parameters from each process node of the wire drawing equipment.

[0034] It can be understood that the set of operating parameter vectors is an ordered group of vectors formed by mathematical processing, based on the operating parameters of each process node in the process flow according to their position in the process flow (each vector corresponds to the operating parameter of a process node). The set of operating parameter vectors can be generated by combining the operating parameters of process nodes with their positions in the process flow through mathematical processing (such as vector mapping and fusion). For example, the operating parameters of the second process node are "tension speed 3m / s, pressure 150N", and its position in the process flow is "after the second pass". After processing, it is transformed into a vector [3,150,2] (the first two digits are the operating parameters, and the last digit is the position identifier). Then, it is combined with the vectors of other passes to form the set {[2,100,1],[3,150,2],[4,180,3]}, which gives the set of operating parameter vectors.

[0035] As an optional embodiment of this application, in step S200, based on the operating parameters of the process node and the position of the process node in the process flow, a set of operating parameter vectors is obtained, including: S210, Determine the location information of the process node in the process flow based on the process node.

[0036] As we can understand it, location information refers to the specific sequence or stage identifier of a process node within the entire wire drawing process (such as "after the 1st pass" or "after the 5th pass"), used to clarify the node's position within the process flow. Location information can be obtained by specifying the exact location of the process node within the process flow (using identifiers such as pass number or processing sequence). For example, using the pass counter of the wire drawing equipment, we can determine that the currently monitored process node corresponds to "after the 5th pass".

[0037] S220, perform vector mapping on the location information to obtain the process position vector of the process node. The process position vector is used to represent the sequential position of the process node in the entire process flow.

[0038] It can be understood that the process position vector is used to reflect the sequential position of the process node in the process flow (the dimension and value of the vector correspond one-to-one with the position). The process position vector can be obtained by directly mapping the position information through the sequence number. For example, if there are 4 passes in the process flow, the position information of "after the 2nd pass" is vector-mapped (using sequence number mapping) to obtain the process position vector [2].

[0039] S230 integrates the process location vector and the operating parameters of the process node to obtain a set of operating parameter vectors.

[0040] It is understandable that the set of operating parameter vectors can integrate the process position vector and the operating parameters of the node into a comprehensive vector through mathematical methods (such as splicing and weighted combination) (so that the vector contains both position information and operating parameter information). For example, the process position vector of the third process node is [3], and the operating parameters are "tension speed 4m / s, pressure 180N". The two are merged into [4,180,3] (the first two are the operating parameters and the last one is the position vector). The fused vectors of multiple nodes are arranged in the order of the passes, which gives the set of operating parameter vectors {[2,100,1],[3,150,2],[4,180,3]}.

[0041] By employing steps S210 to S230, the dispersed operating parameters can be systematically integrated according to their position in the process flow, forming a structured set of operating parameter vectors. This ordered and vectorized processing method ensures a strict correspondence between operating parameters and the positions of process nodes, reducing errors in correlation analysis caused by disordered order of operating parameters.

[0042] The S300 generates multiple deviation diameter data based on the actual measured diameter and the mold preset diameter. Among them, the deviation diameter data is used to reflect the deviation between the actual diameter and the theoretical diameter during the copper alloy wire production process.

[0043] It can be understood that the deviation diameter data represents a numerical value (which can be positive (actual > theoretical) or negative (actual < theoretical)) indicating the degree of deviation between the actual measured diameter (process parameter) and the mold's preset diameter (design parameter) in the production of copper alloy wire. The deviation diameter data can be obtained by calculating the difference between the actual measured diameter (process parameter) and the mold's preset diameter (design parameter). For example: the actual measured diameter of the first process node is 3.02 mm, the mold's preset diameter is 3.00 mm, and the deviation diameter data is +0.02 mm; the actual measured diameter of the second process node is 2.51 mm, the mold's preset diameter is 2.50 mm, and the deviation diameter data is +0.01 mm.

[0044] As an optional embodiment of this application, in step S300, multiple deviation diameter data are generated based on the actual measured diameter and the preset diameter of the mold, including: S310, based on the actual measured diameter, obtains the actual diameter curve.

[0045] As can be understood, the actual diameter curve is a curve plotted with the sequence of process passes (e.g., pass 1, pass 2, etc.) on the horizontal axis and the actual measured diameter (process parameter) at each process node on the vertical axis (visually showing the trend of actual diameter change during the stretching process). The actual diameter curve can be obtained by associating the actual measured diameters of each process node according to the pass sequence, thus reflecting the trend of actual diameter change. For example, if the actual diameters of 5 process nodes are 4.00mm, 3.50mm, 3.01mm, 2.52mm, and 2.00mm respectively, a continuous curve plotted with the pass number on the horizontal axis and the actual measured diameter on the vertical axis is the "actual diameter curve".

[0046] S320, based on the preset diameter of the mold, obtains the design diameter curve.

[0047] The design diameter curve is understood to be a curve plotted with the sequence of process passes on the horizontal axis and the preset mold diameter (design parameter) at each process node on the vertical axis (reflecting the trend of diameter variation during the stretching process under ideal conditions). The design diameter curve can be obtained by associating the preset mold diameters at each process node in sequence according to the pass order, thus reflecting the trend of theoretical diameter variation. For example, if the design parameters for five passes are 4.00mm, 3.50mm, 3.00mm, 2.50mm, and 2.00mm, the continuous curve plotted with the pass number on the horizontal axis and the preset mold diameter on the vertical axis is the "design diameter curve".

[0048] S330, based on the design diameter curve and the actual diameter curve, obtains the deviation diameter curve.

[0049] The deviation diameter curve can be understood as follows: the horizontal axis is the sequence of the process passes, and the vertical axis is the difference between the actual measured diameter and the mold preset diameter for the same pass. This curve visually demonstrates the trend of the deviation between the actual and theoretical diameter as the stretching process progresses. The deviation diameter curve can be obtained by calculating the difference between the actual diameter curve and the design diameter curve for the same pass, and then plotting the pass as the horizontal axis and the difference as the vertical axis. For example: the actual diameter for the 3rd pass is 3.01mm, the design diameter is 3.00mm, and the deviation is +0.01mm; the actual diameter for the 4th pass is 2.52mm, the design diameter is 2.50mm, and the deviation is +0.02mm. The curve plotted sequentially based on the deviations of each pass is the "deviation diameter curve."

[0050] S340, based on the deviation diameter curve, obtains multiple deviation diameter data.

[0051] It is understandable that the deviation diameter data can be obtained by extracting the specific deviation values ​​corresponding to each process node from the deviation diameter curve. For example, the deviation values ​​of +0.02mm for the first pass, 0mm for the second pass, and +0.01mm for the third pass can be extracted from the deviation diameter curve; these specific deviation values ​​are the deviation diameter data.

[0052] By employing steps S310 to S340, discrete diameter deviations can be transformed into visualized trend data. The actual diameter curve and the design diameter curve visually demonstrate the deviation process between actual processing and the theoretical target. The deviation diameter curve further quantifies the changing patterns of the deviation diameter data (such as whether the deviation continues to increase or at which pass abrupt change occurs). This trend analysis provides a basis for locating key adjustment nodes in subsequent steps, avoiding blind adjustments and improving the targeted nature of the deviation analysis.

[0053] In one possible implementation, S340, based on the deviation diameter curve, obtains multiple deviation diameter data, including: S341, based on the deviation diameter curve, uses the sliding window technique for discretization to obtain the denoised curve. The denoised curve is the data curve after eliminating equipment vibration interference.

[0054] It's understandable that a denoised curve is a deviation diameter curve that eliminates equipment vibration interference. The curve's fluctuations are smoother, more accurately reflecting the actual deviation trend of the copper alloy wire diameter. A denoised curve can be obtained by using a sliding window technique. A fixed-length sliding window (containing 3-5 consecutive data points) is set on the deviation diameter curve, and the window is moved point by point along the curve. Then, for the data within each window, its statistical characteristic value is calculated as a benchmark (such as the median or the average value after removing extreme values) to represent the normal fluctuation level of that area, serving as the benchmark value within the window. Then, by comparing the data point at the center of the window with the benchmark value, if the difference exceeds a preset threshold (such as ±0.03mm), the point is determined to be abnormal interference data. Abnormal data can be corrected using a local substitution method. Points marked as abnormal are not directly deleted but replaced with the average value of other normal data within the window. Only the confirmed abnormal points are modified, while other original data remain unchanged, resulting in a corrected curve that still reflects the true diameter change trend. For example, suppose the original deviation diameter curve has values ​​in a certain interval [+0.02, +0.05, +0.01, +0.08, +0.03], where +0.08 significantly deviates from the other data. By using a sliding window (window size = 3), if the difference between this point and the median of the preceding and following data (+0.02) exceeds a threshold (e.g., 0.05), it is corrected to the average of the other data within the window ((+0.01++0.03) / 2=+0.02), resulting in the corrected denoised curve interval [+0.02, +0.05, +0.01, +0.02, +0.03]. This eliminates abnormal interference while preserving the original data's fluctuation trend.

[0055] S342, based on the denoising curve, obtains multiple deviation diameter data.

[0056] It is understandable that the deviation diameter data is extracted from the denoising curve, corresponding to the deviation diameter data of each process node (these data have eliminated vibration interference and are closer to the true deviation). For example, if the discrete data point of the denoising curve in the 3rd pass is +0.01mm, in the 4th pass it is +0.02mm, and in the 5th pass it is +0.03mm, then the specific values ​​of the deviation diameter data are (+0.01mm, +0.02mm, +0.03mm).

[0057] By employing steps S341 to S342 above to filter out spurious deviations caused by equipment vibration (such as abnormal fluctuations of +0.08mm), the data becomes closer to the true size deviation of the copper alloy wire, improving the accuracy of subsequent analysis and helping to eliminate noise interference. The continuously fluctuating curve is transformed into stable discrete data, facilitating clear observation of the overall variation pattern of the deviation and obtaining a smooth data trend. Based on the denoised true deviation data, fine-tuning the operating parameters can be calculated, more accurately correcting the operating state of the wire drawing equipment, reducing erroneous adjustments caused by spurious data (such as avoiding excessive speed reduction due to vibration interference of +0.08mm), thereby improving adjustment accuracy.

[0058] S400, based on the deviation diameter data and the set of operating parameter vectors, obtains fine-tuning operating data. This fine-tuning operating data serves as a vector set to instruct the wire-drawing equipment to correct the input parameters in real time.

[0059] It can be understood that fine-tuning operation data is a set of vectors used to correct the input parameters of the wire drawing equipment in real time (calculated based on the deviation diameter data and the set of operation parameter vectors, which can directly guide equipment adjustments). Fine-tuning operation data can be calculated by analyzing the correlation between the deviation diameter data and the set of operation parameter vectors to determine the specific values ​​of the operation parameters that need adjustment. Fine-tuning operation data can be calculated from the operation parameters mapped from the deviation diameter data and the set of operation parameter vectors.

[0060] As an optional embodiment of this application, in step S400, fine-tuning operation data is obtained based on the deviation diameter data and the set of operating parameter vectors, including: S410, based on the deviation diameter data, obtain the diameter deviation sequence. The diameter deviation sequence is the sequence of differences between the design parameters and actual parameters at the corresponding process node.

[0061] It can be understood that the diameter deviation sequence is a sequence formed by arranging the deviation diameter data of each process node according to their pass order in the process flow (reflecting the continuous change law of deviation with the stretching process). The diameter deviation sequence can be obtained by arranging the difference between the "measured diameter and reference diameter" of each process node in pass order. For example: if the deviation diameter data of 5 passes are +0.02mm, +0.05mm, +0.01mm, +0.02mm, +0.03mm, the diameter deviation sequence is [+0.02, +0.05, +0.01, +0.02, +0.03].

[0062] S420 maps the diameter deviation sequence to the set of operating parameter vectors, resulting in a set of diameter deviation influence vectors. This set of diameter deviation influence vectors reflects the correlation between the deviation diameter data of the copper alloy wire and various operating parameters.

[0063] It can be understood that the diameter deviation influence vector set is a vector group formed by associating each deviation value in the diameter deviation sequence with the preset diameter of each mold and its corresponding operating parameter vector (each vector contains the deviation value and the corresponding operating parameter, used to analyze which operating parameters have a greater impact on the deviation). This can be achieved by establishing a correlation between the diameter deviation sequence and the corresponding vector in the operating parameter vector set (e.g., the deviation value of the nth pass corresponds to the operating parameter vector of the nth pass). After this association, the diameter deviation influence vector set is obtained. For example, if the diameter deviation data for the first pass is +0.02mm, the corresponding operating parameters can be calculated to reduce the stretching speed by 0.048m / s and increase the stretching pressure by 0.56N to offset the influence of the diameter deviation data. The preset diameter of the mold for the first pass is 4mm, and the operating parameter vector for the first pass is [2,100,1] (stretching speed 2m / s, pressure 100N, pass number 1). After association, the diameter deviation influence vector is formed as [4,+0.02,2(-0.048),100(+0.56),1]. The set of such vectors from multiple passes is the diameter deviation influence vector set.

[0064] In one possible implementation, S420, the diameter deviation sequence is mapped to a set of operating parameter vectors to obtain a set of diameter deviation influence vectors, including: S421, Based on the diameter deviation sequence and the set of operating parameter vectors, a corrected operating parameter vector is obtained. The corrected operating parameter vector is a vector that aligns the diameter deviation sequence and the operating parameter vectors in the time dimension.

[0065] It can be understood that the corrected running parameter vector is a vector obtained by aligning the diameter deviation sequence and the running parameter vector in the time dimension (i.e., the deviation data and running parameters at the same time point correspond, facilitating subsequent analysis of their correlation). The corrected running parameter vector can be obtained by aligning the data points in the diameter deviation sequence and the running parameter vector based on the same time index or track number (e.g., the diameter deviation of track 3 corresponds to the running parameters of track 3). For example, the diameter deviation sequence is [+0.02, +0.05, +0.01] (corresponding to tracks 1, 2, and 3), and the set of running parameter vectors is {[2, 100, 1], [3, 150, 2], [4, 180, 3]} (the running parameter vectors for tracks 1, 2, and 3, respectively). By aligning with the time dimension, the deviation of the second pass +0.05 is associated with the running parameter vector of the second pass [3,150,2] to obtain the corrected running parameter vector [+0.05,3,150,2] (the first position is the deviation value, and the following positions are the running parameters).

[0066] S422, based on the corrected operating parameter vector and the preset mold diameter, a linear influence vector is obtained. Here, the linear influence vector is a set of vectors representing the degree of linear influence of the operating parameters on the deviation diameter data.

[0067] It can be understood that the linear influence vector is a set of vectors representing the degree of linear influence of operating parameters on the diameter deviation data (each vector corresponds to an operating parameter, and the vector value represents the magnitude and direction of the influence of the parameter change on the diameter deviation). The linear influence vector can be obtained by calculating the linear relationship between operating parameters and diameter deviation through methods such as linear regression and correlation analysis. For example, by performing linear regression analysis on the modified operating parameter vector set {[+0.02,2,100,1],[+0.05,3,150,2],[+0.01,4,180,3]} and the preset mold diameter, it was found that: for every 1 m / s increase in stretching speed, the diameter deviation increases by an average of 0.2 mm (influence coefficient +0.2); for every 10 N increase in stretching pressure, the diameter deviation decreases by an average of 0.1 mm (influence coefficient -0.1). Then the linear influence vector set is {[+0.2],[-0.1]} (corresponding to the influence coefficients of stretching speed and stretching pressure, respectively).

[0068] S423, based on the fault mode library of the knowledge graph, anomaly correlation vectors are obtained. Here, the anomaly correlation vector is a set of vectors representing the correlation strength between operating parameters and deviation diameter data; the fault mode library of the knowledge graph is obtained based on historical fault data.

[0069] It can be understood that the fault mode library of the knowledge graph is a knowledge base built based on historical fault data (storing information such as fault type, abnormal operating parameters, and fault causes in a graph structure, used to analyze the correlation between operating parameters and diameter deviation). The fault mode library of the knowledge graph includes operating parameters and deviation diameter data, and there is a mapping relationship between the operating parameters and deviation diameter data. Optionally, the fault mode library of the knowledge graph can be obtained by recording historical fault data. The abnormal correlation vector is a set of vectors representing the correlation strength between operating parameters and diameter deviation data.

[0070] For example, in step S423, based on the fault mode library of the knowledge graph, an anomaly association vector is obtained, including: S4231, based on a knowledge graph-based fault mode library, extracts historical abnormal operating parameters. These historical abnormal operating parameters are the operating parameters under abnormal conditions.

[0071] It is understandable that historical abnormal operating parameters refer to the operating parameters recorded when historical faults occurred (e.g., when a diameter deviation fault occurred, the stretching speed was 5 m / s and the pressure was 200 N, exceeding the normal range). Historical abnormal operating parameters can be extracted from the fault pattern library of the knowledge graph as a data source. For example, the fault pattern library records three "diameter too large" faults, with corresponding operating parameters as follows: Fault 1: stretching speed 4.5 m / s (normal range 2-4 m / s), pressure 180 N; Fault 2: stretching speed 5 m / s, pressure 190 N; Fault 3: stretching speed 4.8 m / s, pressure 175 N. The historical abnormal operating parameters extracted from these records are {[4.5,180],[5,190],[4.8,175]}.

[0072] S4232, based on historical abnormal operating parameters, uses the FP-Growth algorithm to obtain support and confidence. Support reflects the frequency of occurrence of historical abnormal operating parameters in the fault mode library; confidence reflects the probability that historical abnormal operating parameters exceed a threshold.

[0073] As we can understand it, the FP-Growth algorithm is a frequent itemset mining algorithm (it doesn't need to generate candidate sets; it directly constructs a frequent pattern tree from the database, efficiently discovering association rules between parameters). Support represents the frequency of occurrence of historical anomalous operating parameters in the fault pattern library (e.g., if "stretching speed > 4 m / s" appears 60 times out of 100 faults, its support is 60%). Confidence reflects the reliability of the association rule (e.g., the confidence of "if stretching speed > 4 m / s, then diameter deviation > 0.03 mm" is 80%, meaning that in faults with speed > 4 m / s, 80% are accompanied by a diameter deviation > 0.03 mm). Support and confidence can be obtained by mining historical anomalous operating parameters using the FP-Growth algorithm. For example, when the FP-Growth algorithm was applied to the historical abnormal operating parameters {[4.5,180],[5,190],[4.8,175]}, it was found that the support for "stretching speed > 4.5 m / s" was 60% (3 / 5 faults); the confidence for "if stretching speed > 4.5 m / s, then diameter deviation > 0.04 mm" was 80% (all 4 faults with speed > 4.5 m / s were accompanied by diameter deviation > 0.04 mm).

[0074] S4233, based on support and confidence, obtains the abnormal association vector.

[0075] It is understandable that the anomaly correlation vector can be obtained by converting the support and confidence scores into vector form. For example, the support for a tensile speed > 4 m / s and a diameter deviation > 0.03 mm is 60%, and the confidence score is 80%; the support for a tensile pressure < 150 N and a diameter deviation > 0.03 mm is 40%, and the confidence score is 70%. Combining the support and confidence scores into the correlation strength (e.g., correlation strength = support × confidence), we get: the correlation strength for tensile speed = 0.6 × 0.8 = 0.48; the correlation strength for tensile pressure = 0.4 × 0.7 = 0.28. Then the anomaly correlation vector is {[0.48], [0.28]}, which represents the proportion of the operating parameters (tensile speed and tensile pressure) affecting the deviation diameter data.

[0076] S424, based on the abnormal correlation vector and the linear influence vector, obtain the set of diameter deviation influence vectors.

[0077] It can be understood that the diameter deviation influence vector set comprehensively considers the correction operating parameter vector, the abnormal correlation vector (based on historical fault modes), and the linear influence vector (based on the linear relationship of current data). The final diameter deviation influence vector set can be generated by fusing these three elements. For example, in the abnormal correlation vector, under deviation conditions: tensile speed accounts for 48%, and tensile pressure accounts for 28%. In the linear influence vector, for every 1 m / s increase in tensile speed, the diameter deviation increases by an average of 0.2 mm (influence coefficient +0.2); for every 10 N increase in tensile pressure, the diameter deviation decreases by an average of 0.1 mm (influence coefficient -0.1). Based on the inverse operation of the linear influence vector, when the diameter deviation data is +0.01 mm, according to the linear influence vector set, the deviation operating parameter is either a decrease in tensile speed of 0.05 m / s or an increase in tensile pressure of 1 N. Therefore, the tensile speed deviation in the diameter deviation influence vector set is (-0.05 × 0.48 = -0.02). 4) The tensile pressure deviation is (+1×0.28=+0.28), so the set of diameter deviation influence vectors can be {[4,+0.02,2(-0.048),100(+0.56),1],[3.5,+0.05,3(-0.12),150(+1.4),2],[3,+0.01,4(-0.024),180(+0.28),3]}, where, in order, they represent the mold preset diameter, diameter deviation data, tensile speed (deviation value), tensile pressure (deviation value), and pass number.

[0078] By employing the steps S421 to S424 described above, it is possible to accurately locate influencing factors. By combining historical fault experience (abnormal correlation vector) and the linear relationship of current data (linear influence vector), the influence of operating parameters on diameter deviation data can be more comprehensively identified. Furthermore, the deviation values ​​of operating parameters corresponding to diameter deviation data can be obtained through the abnormal correlation vector and the linear influence vector, thereby obtaining the set of diameter deviation influence vectors.

[0079] S430, based on the set of diameter deviation influence vectors, obtains fine-tuning operation data.

[0080] It is understandable that fine-tuning operation data can be obtained by extracting the deviation values ​​of the operation parameters corresponding to the deviation diameter data in the set of diameter deviation influence vectors. For example, the fine-tuning operation data for the first pass could be [-0.048m / s, +0.56N].

[0081] By employing steps S410 to S430, a quantitative correlation between the deviation diameter data and operating parameters is established, providing a basis for parameter adjustment. The mapping between the diameter deviation sequence and the operating parameter vector clarifies the specific operating parameter corresponding to each deviation diameter data point; the parameter adjustment matrix further quantifies the influence weight of each operating parameter on the deviation diameter data. This correlation analysis shifts subsequent equipment adjustments from "experience-based judgment" to "data-driven" approaches, enabling adjustment measures (such as speed increase / deceleration, pressure increase / depressurization) to accurately offset deviations and improve the effectiveness of real-time correction.

[0082] The S500 adjusts the operating parameters of the wire drawing equipment in real time based on fine-tuning operation data.

[0083] It is understandable that real-time adjustment of the operating parameters of the wire drawing equipment involves immediately modifying the operating parameters based on fine-tuning data to adapt the equipment state to deviations and achieve stable production ("real-time" emphasizes the timeliness of the adjustment, completed in milliseconds / seconds). For example, if the fine-tuning data is [-0.048m / s, +0.56N], the control system of the wire drawing equipment, upon receiving the data, will reduce the stretching speed from 3m / s to 2.952m / s and increase the pressure from 150N to 150.56N.

[0084] As an optional embodiment of this application, S500, based on fine-tuning operation data, adjusts the operating parameters of the wire-pulling device in real time, including: S510 generates operating parameter correction instructions based on fine-tuning operating data.

[0085] It is understandable that operating parameter correction instructions are instructions that the control system of the wire drawing equipment can recognize and execute (clearly specifying the names of the parameters to be adjusted and the target values). This can be obtained by converting the fine-tuning operating data (numerical values) into an instruction format (such as text or code) that the equipment can understand. For example, fine-tuning operating data of [-0.048m / s, +0.56N] can be converted into the operating parameter correction instruction "decrease the stretching speed from 3m / s to 2.952m / s, and increase the pressure from 150N to 150.56N".

[0086] The S520 adjusts the operating parameters of the wire pulling equipment in real time according to the operating parameter correction command.

[0087] It is understandable that real-time adjustment of the operating parameters of the wire drawing equipment is the process by which the control system of the wire drawing equipment immediately modifies its own operating parameters according to the instructions after receiving the instructions to correct the operating parameters. For example, after the control system receives the instruction "the stretching speed decreases from 3 m / s to 2.952 m / s and the pressure increases from 150 N to 150.56 N", it starts the actuator and completes the adjustment of speed and pressure within 0.5 seconds.

[0088] By employing steps S510 to S520, abstract fine-tuning data is transformed into real-time actions that the wire drawing equipment can execute. The operating parameter correction command clearly defines the adjustment target, and the real-time adjustment is completed within seconds. This step directly connects to the wire drawing equipment, translating the data analysis results from the previous steps into actual operation, forming a closed loop of "data acquisition-analysis-adjustment-feedback," ultimately achieving real-time and precise control of the copper alloy wire diameter and improving the product dimensional qualification rate.

[0089] It should be understood that the sequence number of each step in the above embodiments does not imply 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.

[0090] Corresponding to the real-time data processing method for copper alloy wire production process described in the above embodiments, this application also provides a real-time data processing device for copper alloy wire production process, wherein each unit of the device can realize each step of the real-time data processing method for copper alloy wire production process. Figure 3 The diagram shows a structural block diagram of a real-time data processing device for copper alloy wire production process provided in an embodiment of this application. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0091] Reference Figure 3 The device includes: The acquisition unit is used to acquire the process parameters, design parameters, and operating parameters of the copper alloy wire at the process nodes of the wire drawing equipment. The process nodes include at least one position node, each position node being the location where the wire drawing equipment draws the copper alloy wire each time; the process parameters are the actual measured diameter collected by the mobile laser diameter gauge; the design parameters are the preset diameter of the mold; and the operating parameters are the input parameters of the wire drawing equipment. The position unit is used to obtain a set of operating parameter vectors based on the operating parameters of the process nodes and their positions in the process flow. The parameter vector set represents an ordered set of input parameters from each process node of the wire drawing equipment. The deviation unit is used to generate multiple deviation diameter data based on the actual measured diameter and the preset diameter of the mold. These deviation diameter data reflect the deviation between the actual diameter and the theoretical diameter during the copper alloy wire production process. The fine-tuning unit is used to obtain fine-tuning operation data based on the deviation diameter data and the set of operating parameter vectors. This fine-tuning operation data serves as a vector set that instructs the wire-drawing equipment to correct the input parameters in real time. The results unit is used to adjust the input parameters of the wire drawing equipment in real time based on the fine-tuning operation data.

[0092] It should be noted that the information interaction and execution process between the above-mentioned units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0093] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units is used as an example. In practical applications, the above functions can be assigned to different functional units as needed, that is, the internal structure of the device can be divided into different functional units to complete all or part of the functions described above. The functional units in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0094] This application also provides a wire pulling device. Figure 4 This is a schematic diagram of the structure of a wire-pulling device provided in one embodiment of this application. Figure 4 As shown, the wire-pulling device 6 of this embodiment includes: at least one processor 60 ( Figure 4 Only one is shown in the image), at least one memory 61 ( Figure 4 (Only one is shown in the image) 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, it causes the wire drawing device 6 to perform the steps in any of the above-described real-time data processing method embodiments for copper alloy wire production processes, or causes the wire drawing device 6 to perform the functions of each unit in the above-described device embodiments.

[0095] For example, the computer program 62 may be divided into one or more units, which are stored in the memory 61 and executed by the processor 60 to complete this application. The one or more units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 62 in the wire drawing device 6.

[0096] The wire drawing device 6 may include a wire drawing device body, a mobile laser diameter measuring instrument, and a controller. The mobile laser diameter measuring instrument is located outside the wire drawing device body. It is a movable detection device that uses laser scanning technology to non-contactly measure the actual diameter of the copper alloy wire. It can move synchronously with the copper alloy wire to track changes in the actual diameter at the same location. The controller controls the mobile laser diameter measuring instrument to move synchronously with the copper alloy wire in the wire drawing device body and collects the actual diameter at the same location of the copper alloy wire at each process node. The actual measured diameter is used to calculate the deviation diameter data between the actual diameter and the preset diameter of the mold. The controller is electrically connected to the mobile laser diameter measuring instrument. The wire drawing device 6 may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will understand that… Figure 4 This is merely an example of cable pulling device 6 and does not constitute a limitation on cable pulling device 6. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, buses, etc.

[0097] The processor 60 can be a Central Processing Unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0098] In some embodiments, the memory 61 may be an internal storage unit of the drawing device 6, such as a hard disk or memory of the drawing device 6. In other embodiments, the memory 61 may be an external storage device of the drawing device 6, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the drawing device 6. Further, the memory 61 may include both internal and external storage units of the drawing device 6. The memory 61 is used to store operating systems, applications, bootloaders, data, and other programs, such as the program code of computer programs. The memory 61 can also be used to temporarily store data that has been output or will be output.

[0099] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0100] This application provides a computer program product that, when run on a wire drawing device, enables the wire drawing device to perform the steps described in any of the above method embodiments.

[0101] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to the cable pulling device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, such as a USB flash drive, a portable hard drive, a magnetic disk, or an optical disk.

[0102] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0103] Those skilled in the art will recognize that the units and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art 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.

[0104] In the embodiments provided in this application, it should be understood that the disclosed real-time data processing device, wire drawing equipment, and real-time data processing method for copper alloy wire production processes can be implemented in other ways. For example, the embodiments of the real-time data processing device and wire drawing equipment for copper alloy wire production processes described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units may be electrical, mechanical, or other forms.

[0105] The units described as separate components may or may not be physically separate. 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 the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0106] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for real-time data processing in the production process of copper alloy wire, characterized in that, The method is applied to a wire drawing device, which includes a wire drawing device body and a mobile laser diameter measuring instrument, wherein the mobile laser diameter measuring instrument is disposed on the outside of the wire drawing device body. The process parameters, design parameters, and operating parameters of the copper alloy wire at the process nodes of the wire drawing equipment are obtained; wherein, the process node includes at least one position node, and each position node is the position node where the wire drawing equipment draws the copper alloy wire each time; the process parameter is the actual measured diameter collected by the mobile laser diameter measuring instrument; the design parameter is the preset diameter of the mold; and the operating parameter is the input parameter of the wire drawing equipment. Based on the operating parameters of the process node and the position of the process node in the process flow, an operating parameter vector set is obtained; wherein, the parameter vector set represents an ordered set composed of the input parameters of each process node of the wire drawing equipment; Multiple deviation diameter data are generated based on the actual measured diameter and the preset diameter of the mold; wherein, the deviation diameter data is used to reflect the deviation value between the actual diameter and the theoretical diameter during the copper alloy wire production process; Based on the deviation diameter data and the set of operating parameter vectors, fine-tuning operating data is obtained; wherein, the fine-tuning operating data is used to instruct the wire drawing device to correct the set of input parameter vectors in real time; Based on the aforementioned fine-tuning operation data, the input parameters of the wire drawing equipment are adjusted in real time.

2. The real-time data processing method for copper alloy wire production process according to claim 1, characterized in that, The set of operating parameter vectors obtained based on the operating parameters of the process node and the position of the process node in the process flow includes: Based on the process node, determine the location information of the process node in the process flow; The location information is vector-mapped to obtain the process location vector of the process node; wherein, the process location vector is used to characterize the sequential position of the process node in the entire process flow. The process location vector and the operating parameters of the process node are fused to obtain a set of operating parameter vectors.

3. The real-time data processing method for copper alloy wire production process according to claim 1, characterized in that, The generation of multiple deviation diameter data based on the actual measured diameter and the preset diameter of the mold includes: Based on the actual measured diameter, the actual diameter curve is obtained; Based on the preset diameter of the mold, the design diameter curve is obtained; Based on the actual diameter curve and the actual diameter curve, the deviation diameter curve is obtained; Based on the aforementioned deviation diameter curve, multiple deviation diameter data are obtained.

4. The real-time data processing method for copper alloy wire production process according to claim 3, characterized in that, Based on the deviation diameter curve, multiple deviation diameter data are obtained, including: Based on the aforementioned deviation diameter curve, a sliding window technique is used for discretization to obtain a denoised curve; wherein, the denoised curve is the data curve after eliminating equipment vibration interference; Based on the denoising curve, multiple deviation diameter data are obtained.

5. The real-time data processing method for copper alloy wire production process according to claim 1, characterized in that, The fine-tuning operation data obtained based on the deviation diameter data and the set of operating parameter vectors includes: Based on the deviation diameter data, a diameter deviation sequence is obtained; wherein, the diameter deviation sequence is a sequence of differences between the reference diameter and the measured diameter at the corresponding process node; The diameter deviation sequence is mapped to the set of operating parameter vectors to obtain the diameter deviation influence vector set; wherein, the diameter deviation influence vector set is used to reflect the correlation between the deviation diameter data of the copper alloy wire and each operating parameter; Based on the set of diameter deviation influence vectors, fine-tuning operation data is obtained.

6. The real-time data processing method for copper alloy wire production process according to claim 5, characterized in that, The step of mapping the diameter deviation sequence to a set of operating parameter vectors to obtain a set of diameter deviation influence vectors includes: Based on the diameter deviation sequence and the set of operating parameter vectors, a corrected operating parameter vector is obtained; wherein, the corrected operating parameter vector is a vector that aligns the diameter deviation sequence and the operating parameter vector in the time dimension. Based on the corrected operating parameter vector and the preset mold diameter, a linear influence vector is obtained; wherein, the linear influence vector is a set of vectors representing the degree of linear influence of the operating parameters on the deviation diameter data; An anomaly correlation vector is obtained based on a fault mode library of a knowledge graph; wherein, the anomaly correlation vector is a set of vectors representing the correlation strength between operating parameters and deviation diameter data; the fault mode library of the knowledge graph is obtained based on historical fault data; Based on the abnormal correlation vector and the linear influence vector, a set of diameter deviation influence vectors is obtained.

7. The real-time data processing method for copper alloy wire production process according to claim 6, characterized in that, The knowledge graph-based fault mode library yields anomaly association vectors, including: Based on a knowledge graph-based fault mode library, historical abnormal operating parameters are extracted; wherein, the historical abnormal operating parameters are the operating parameters under abnormal conditions. Based on the historical abnormal operating parameters, the support and confidence scores are obtained using the FP-Growth algorithm; wherein, the support score reflects the frequency of occurrence of the historical abnormal operating parameters in the fault mode library; and the confidence score reflects the probability that the historical abnormal operating parameters are greater than a threshold. Based on the support and confidence, an anomaly association vector is obtained.

8. The real-time data processing method for copper alloy wire production process according to claim 1, characterized in that, The real-time adjustment of the input parameters of the wire drawing device based on the fine-tuning operation data includes: Based on the fine-tuning operation data, an operation parameter correction instruction is obtained; wherein, the operation parameter correction instruction is an instruction that the wire drawing equipment control system can recognize and execute; The input parameters of the wire drawing device are adjusted in real time according to the operation parameter correction instructions.

9. A real-time data processing device for copper alloy wire production process, characterized in that, Applied to wire drawing equipment, for implementing the real-time data processing method for copper alloy wire production process as described in any one of claims 1 to 8, the real-time data processing device for copper alloy wire production process includes: The acquisition unit is used to acquire the process parameters, design parameters, and operating parameters of the copper alloy wire at the process nodes of the wire drawing equipment; wherein, the process node includes at least one position node, and each position node is the position node where the wire drawing equipment stretches the copper alloy wire each time; the process parameter is the actual measured diameter collected by the mobile laser diameter measuring instrument; the design parameter is the preset diameter of the mold; and the operating parameter is the input parameter of the wire drawing equipment. A position unit is used to obtain a set of operating parameter vectors based on the operating parameters of the process node and the position of the process node in the process flow; wherein, the set of parameter vectors represents an ordered set composed of the input parameters of each process node of the wire drawing equipment; A deviation unit is used to generate multiple deviation diameter data based on the actual measured diameter and the preset diameter of the mold; wherein, the deviation diameter data is used to reflect the deviation value between the actual diameter and the theoretical diameter during the copper alloy wire production process; The fine-tuning unit is used to obtain fine-tuning operation data based on the deviation diameter data and the set of operating parameter vectors; wherein the fine-tuning operation data is used to instruct the wire drawing device to correct the set of input parameter vectors in real time; The result unit is used to adjust the input parameters of the wire drawing device in real time based on the fine-tuning operation data.

10. A wire drawing device, characterized in that, The device includes a wire drawing equipment body, a controller electrically connected to the wire drawing equipment body, and a mobile laser diameter measuring instrument disposed on the wire drawing equipment body. The controller includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method as described in any one of claims 1 to 8.

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