Method for controlling protective load reduction of copper wire multi-head drawing machine die

Abstract

The application is suitable for the technical field of metal wire processing, and particularly relates to a protective load reduction control method for a copper wire multi-head wire drawing machine. The method comprises the following steps: firstly, based on the running state data of each parallel wire drawing path of the multi-head wire drawing machine, multi-dimensional running features of the running state data are extracted, and precursor characteristic parameters of each wire drawing path are determined based on the multi-dimensional running features; then, based on the precursor characteristic parameters, a load reduction index of the corresponding wire drawing path is determined; finally, based on the load reduction index, a target running speed of the corresponding wire drawing path is obtained; and based on the target running speed, a speed control is performed on the multi-head wire drawing machine. Through the above method, the problem of how to identify the impact precursor by combining multi-dimensional time-frequency feature cross-validation for each wire drawing path can be solved, so that the multi-head wire drawing machine mold can be precisely reduced in a path before the force impact subject arrives.

Description

Technical Field

[0001] This application belongs to the field of metal wire processing technology, and in particular relates to a protective load reduction control method for the mold of a multi-head copper wire drawing machine. Background Technology

[0002] A multi-head copper wire drawing machine is a wire drawing device that includes multiple parallel drawing paths. Each path draws the copper wire by reducing its diameter using drawing dies. As a core consumable component, the drawing die's working condition directly affects the dimensional accuracy and surface quality of the finished copper wire. During the drawing process, internal defects in the copper material can cause transient force impacts when the copper wire enters the die orifice, which is the main cause of micro-cracks or even breakage in the die. Therefore, preventative load reduction protection of the die is necessary before the force impact occurs.

[0003] Current technologies for detecting force impacts primarily compare the real-time drawing force amplitude with a preset safety threshold, triggering load reduction protection when the amplitude exceeds the threshold. However, this method based on a single amplitude threshold comparison has the following problems: There is a precursor stage before the force impact occurs. During this stage, the signal structure of the operating status data, i.e., the time-frequency characteristic distribution, has undergone detectable changes, but the drawing force amplitude has not yet increased significantly, thus failing to be detected by the amplitude threshold. This results in the load reduction protection of the copper wire multi-head drawing machine mold only occurring after the force impact has already exerted stress on the mold, limiting its protective effect. Furthermore, each drawing path has its own unique normal signal pattern due to individual mold differences, motor manufacturing tolerances, lubrication conditions, and other factors. Using a uniform fixed threshold cannot adapt to the individual differences of each path. Therefore, how to identify the precursor of impact by combining multi-dimensional time-frequency characteristics for cross-verification for each drawing path, and thus perform precise path-specific load reduction on the multi-head drawing machine mold before the force impact arrives, becomes a problem that needs to be solved. Summary of the Invention

[0004] This application provides a protective load reduction control method for the mold of a multi-head copper wire drawing machine, which can solve the problem of how to identify the precursor of impact by combining multi-dimensional time and frequency characteristics for each drawing path, so as to perform precise load reduction of the multi-head wire drawing machine mold before the force impact arrives.

[0005] In a first aspect, embodiments of this application provide a protective load reduction control method for the die of a multi-head copper wire drawing machine, including: Based on the operating status data of each parallel wire drawing path of the multi-head wire drawing machine, multi-dimensional operating features of the operating status data are extracted, and precursor feature parameters of each wire drawing path are determined based on the multi-dimensional operating features; wherein, the precursor feature parameters characterize the degree to which the operating status data deviates from the preset normal mode within the preset analysis window; Based on the aforementioned precursor characteristic parameters, the load reduction index for the corresponding wire drawing path is determined; wherein, the load reduction index characterizes the required rate reduction range for the wire drawing path; Based on the aforementioned load reduction index, the target operating rate of the corresponding wire drawing path is obtained; Based on the target operating rate, the multi-head wire drawing machine is speed controlled.

[0006] The technical solutions described in this application embodiment have at least the following technical effects: The copper wire multi-head drawing machine mold protective load reduction control method provided in this application embodiment first extracts multi-dimensional operating features from the operating status data of each parallel drawing path of the multi-head drawing machine, and determines the precursor feature parameters of each drawing path based on the multi-dimensional operating features; wherein, the precursor feature parameters characterize the degree to which the operating status data deviates from the preset normal mode within a preset analysis window; then, based on the precursor feature parameters, the load reduction index of the corresponding drawing path is determined; wherein, the load reduction index characterizes the required rate reduction of the drawing path; finally, based on the load reduction index, the target operating rate of the corresponding drawing path is obtained; and based on the target operating rate, the multi-head drawing machine is speed controlled. This application obtains multi-dimensional operating features from the operating status data of each parallel drawing path, determines the precursor feature parameters of each drawing path, determines the degree to which each parallel drawing path deviates from the preset normal mode, and then determines the load reduction index of the corresponding drawing path, thereby optimizing the target operating rate of a single parallel drawing path and controlling the rate of the multi-head drawing machine. The above method can solve the problem of how to identify the precursors of impact by combining multi-dimensional time and frequency features for cross-verification of each wire drawing path, so as to perform precise load reduction of multi-head wire drawing machine dies in different paths before the main force impact arrives.

[0007] Secondly, embodiments of this application provide a protective load reduction control device for a copper wire multi-head drawing machine die, applied to electronic equipment, for implementing the protective load reduction control method for a copper wire multi-head drawing machine die as described in any one of the first aspects above. The protective load reduction control device for the copper wire multi-head drawing machine die includes: The acquisition unit is used to extract multi-dimensional operating features from the operating status data of each parallel wire drawing path of the multi-head wire drawing machine, and to determine the precursor feature parameters of each wire drawing path based on the multi-dimensional operating features; wherein, the precursor feature parameters characterize the degree to which the operating status data deviates from a preset normal mode within a preset analysis window; The index unit is used to determine the load reduction index of the corresponding wire drawing path based on the precursor characteristic parameters; wherein the load reduction index represents the required rate reduction range of the wire drawing path. The operating unit is used to obtain the target operating rate of the corresponding wire drawing path based on the load reduction index; A control unit is used to control the speed of the multi-head wire drawing machine based on the target operating speed.

[0008] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method as described in any of the first aspects above.

[0009] Fourthly, embodiments of this application provide a computer program product that, when run on an electronic device, causes the electronic device to perform the method described in any one of the first aspects above.

[0010] 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

[0011] 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.

[0012] Figure 1 This is a schematic flowchart of a copper wire multi-head drawing machine mold protective load reduction control method provided in an embodiment of this application; Figure 2 This is a schematic diagram of the operation of a protective load reduction control method for a multi-head copper wire drawing machine mold provided in an embodiment of this application; Figure 3 This is a schematic diagram of the protective load reduction control device for the copper wire multi-head drawing machine mold provided in the embodiments of this application; Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0013] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0014] In the related art, a multi-wire drawing machine for copper wires is a wire drawing apparatus comprising a plurality of parallel wire drawing paths, and each path performs diameter reduction drawing on the copper wire through a wire drawing die. As a core process consumable part, the working state of the wire drawing die directly affects the dimensional accuracy and surface quality of the finished copper wire product. During the wire drawing process, when internal defects in the copper material enter the die orifice of the wire drawing die along with the copper wire, a transient force impact will be triggered, which is the main cause of microcracks and even cracking of the die. Therefore, it is necessary to perform preventive load reduction protection on the die before the force impact occurs.

[0015] To solve the above problems, embodiments of the present application provide a die protective load reduction control method for a multi-wire drawing machine for copper wires. In this method, precursor characteristic parameters of each wire drawing path are determined based on multi-dimensional operating features of operating state data of each parallel wire drawing path of the multi-wire drawing machine; wherein the precursor characteristic parameters characterize the degree to which the operating state data deviates from a preset normal pattern within a preset analysis window; a load reduction indicator of the corresponding wire drawing path is determined based on the precursor characteristic parameters; wherein the load reduction indicator characterizes the required rate reduction amplitude of the wire drawing path; a target operating rate of the corresponding wire drawing path is obtained based on the load reduction indicator; and rate control is performed on the multi-wire drawing machine based on the target operating rate. By extracting multi-dimensional information of the multi-dimensional operating features and performing cross-validation and fusion, a multi-dimensional anomaly scoring function is used to replace single threshold comparison, changes in the internal structure of the signal are detected at the precursor stage of the force impact, and load reduction is executed before the main body of the force impact arrives, thereby realizing preventive protection for the die of the multi-wire drawing machine. The above method can solve the problem of how to identify the impact precursor by combining multi-dimensional time-frequency features for cross-validation for each wire drawing path, so as to perform path-specific precise load reduction on the die of the multi-wire drawing machine before the main body of the force impact arrives.

[0016] The die protective load reduction control method for a multi-wire drawing machine for copper wires provided by the embodiments of the present application can be applied to an electronic device. At this time, the electronic device is the execution subject of the die protective load reduction control method for a multi-wire drawing machine for copper wires provided by the embodiments of the present application, and the embodiments of the present application do not impose any limitation on the specific type of the electronic device.

[0017] For example, the electronic device may be a programmable logic controller matched with the multi-wire drawing machine for copper wires, an embedded motion control industrial personal computer, an industrial edge computing unit equipped with a time-frequency analysis module, a multi-channel servo integrated measurement and control device, a host industrial touch all-in-one machine for a wire drawing unit, a distributed industrial gateway, an FPGA signal processing single-board computer, a workshop production line edge server, and the like. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the method according to any one of the above aspects is implemented.

[0018] To better understand the protective load reduction control method for copper wire multi-head drawing machine molds provided in this application embodiment, the specific implementation process of the protective load reduction control method for copper wire multi-head drawing machine molds provided in this application embodiment will be described by way of example below.

[0019] Figure 1 A flowchart illustrating the protective load reduction control method for copper wire multi-head drawing machine dies provided in this application embodiment is shown. Figure 2 The diagram illustrates the operation of the protective load reduction control method for the die of a multi-head copper wire drawing machine provided in this application embodiment. The protective load reduction control method for the die of a multi-head copper wire drawing machine includes: S100 extracts multi-dimensional operational features from the operational status data of each parallel drawing path of the multi-head wire drawing machine, and determines the precursor feature parameters of each drawing path based on the multi-dimensional operational features. The precursor feature parameters characterize the degree to which the operational status data deviates from a preset normal pattern within a preset analysis window.

[0020] It can be understood that the operating status data refers to the real-time monitoring data collected during the continuous operation of each parallel drawing path of the copper wire multi-head drawing machine. This includes the following three types of signals: vibration signals, collected by a piezoelectric accelerometer installed at the die inlet, reflecting the mechanical vibration state during the drawing process; tension signals, collected by a strain gauge force sensor installed at the die inlet, reflecting the tension experienced by the copper wire during drawing; and speed signals, collected by a Hall effect sensor installed at the guide wheel, reflecting the running speed of the drawing path. All three types of signals are time-series signals, collected synchronously at a sampling frequency of 10kHz.

[0021] Multidimensional operational characteristics are three quantitative indicators extracted from operational status data, including energy distribution characteristics, signal fluctuation characteristics, and spectral distribution characteristics. Energy distribution characteristics and signal fluctuation characteristics are time-domain features, reflecting the distribution characteristics of signal energy and the fluctuation characteristics of the signal over time, respectively. Spectral distribution characteristics are frequency-domain features, reflecting the distribution characteristics of the signal's frequency components. These three dimensions characterize the operational status of the wire drawing path from three independent perspectives: energy load, time-domain stationarity, and frequency-domain matching.

[0022] The preset analysis window is a continuous data acquisition and analysis period pre-set according to the characteristics of the wire drawing process. It is used to extract stable and effective time-series data for feature analysis and avoid interference from instantaneous noise data. Its typical duration is 1 to 10 seconds, for example, it can be set to 5 seconds. The preset normal mode is a standardized set of time-frequency feature parameters obtained by statistical fitting of a large amount of historical normal operation data under normal equipment fault-free and wire drawing process compliance conditions. It includes benchmark parameters such as normal value ranges, distribution patterns, and correlations of time-frequency features in various dimensions. In actual implementation, the preset normal mode can be constructed by collecting historical operating condition data of the equipment that has been running stably for a long time, removing abnormal operating condition data such as faults, debugging, start-up and shutdown, and constructing the preset normal mode after statistical analysis and cluster fitting. For example, for different specifications of copper wire (such as different specifications with diameters of 0.1mm, 0.2mm, 0.5mm, etc.) and different wire drawing speeds (such as different speeds of 5m / s, 10m / s, 15m / s, etc.), the corresponding normal time-frequency feature distribution models are fitted to form a multi-scenario normal mode parameter library. The precursor characteristic parameter is a deviation quantification index obtained by comparing the real-time runtime frequency characteristics with the normal mode of the corresponding scenario. It can intuitively reflect the severity of potential abnormal hazards in each wire drawing path.

[0023] As an optional embodiment of this application, S100 involves extracting multi-dimensional operational features from the operational status data of each parallel drawing path of the multi-head wire drawing machine, and determining the precursor feature parameters of each drawing path based on the multi-dimensional operational features, including: S110, based on the operational status data, obtain the energy distribution characteristics in the multi-dimensional operational features. The energy distribution characteristics characterize the degree of energy concentration in the operational status data within a preset analysis window.

[0024] It is understandable that energy distribution characteristics directly reflect the load intensity and signal activity level of the wire drawing equipment's operating conditions, serving as a core foundational signal characterizing the equipment's power consumption, mechanical vibration intensity, and tension stability. Energy distribution characteristics can be derived by performing energy transformation on the vibration signal in the operating status data to obtain an energy envelope signal; then, the energy envelope signal within the analysis window is processed into frames, and the energy of each frame is calculated to obtain a frame energy sequence; finally, based on the frame energy sequence, three indicators are calculated: the total signal energy, the standard deviation of the energy proportion of each frame, and the width of the energy accumulation interval. These three indicators are then weighted and combined to obtain a normalized energy distribution characteristic value. A larger energy distribution characteristic value indicates a more uneven distribution of energy over time, and a higher probability of local load abrupt changes.

[0025] S120, based on the operating status data, obtains the signal fluctuation characteristics in the multi-dimensional operating features. Among them, the signal fluctuation characteristics characterize the instantaneous fluctuation degree of the operating status data.

[0026] Signal fluctuation characteristics are understood to be the frequency and switching rate of changes in the amplitude and value of operating status data within a preset analysis window. They accurately reflect the dynamic stability of the wire drawing equipment during operation. Signal fluctuation characteristics describe the dynamic amplitude and intensity of oscillations of operating signals over a very short timescale, distinguishing them from long-term trend changes and focusing on instantaneous, sudden signal disturbances. In practice, signal fluctuation characteristics are obtained by statistically analyzing the tension signal in the operating status data, including the zero-crossing rate (the number of times the signal crosses zero within a unit of time), peak count (the number of times the signal reaches local extreme points within a unit of time), and mean absolute difference (the average of the absolute values ​​of the differences between adjacent sampling points). These statistical measures are then weighted and combined to obtain the signal fluctuation characteristic value. A larger characteristic value indicates more severe instantaneous signal fluctuations, and a higher probability of potential problems such as guide rail jamming and frequent tension fine-tuning.

[0027] S130, based on the operational status data, obtain the spectral distribution characteristics in the multidimensional operational features. The spectral distribution characteristics characterize the degree of shift in the centroid of the frequency component distribution of the operational status data.

[0028] It is understandable that the spectral distribution characteristics can be obtained by performing spectral transformation on the vibration signal in the operating status data to obtain the power spectral density distribution. Then, based on the power spectral density distribution, three parameters are calculated: the spectral centroid (reflecting the position of the dominant frequency under operating conditions), the spectral bandwidth (reflecting the degree of dispersion of frequency components), and the dominant frequency amplitude (reflecting the energy intensity of the dominant vibration). When the equipment malfunctions, the spectral centroid shifts, the spectral bandwidth widens, and the dominant frequency amplitude attenuates. For example, slight wear on the equipment bearings or a small deviation of the wire drawing speed from the rated value will generate abnormal frequency components, causing the spectral distribution centroid to shift and the spectral distribution characteristics to show a significant deviation.

[0029] By employing steps S110 to S130, it is possible to comprehensively capture various potential abnormalities and hidden dangers such as die wear, equipment jamming, unstable tension, and speed deviation. Simultaneously, it ensures the uniformity and accuracy of feature extraction for each parallel drawing path, improving the completeness of identifying precursors to drawing conditions.

[0030] S140, based on the energy distribution characteristics and the preset energy reference value, obtain the energy deviation value in the precursor characteristic parameters.

[0031] It can be understood that the preset energy reference value is the standard benchmark value of energy distribution characteristics corresponding to the process scenario and preset analysis window under the preset normal mode. It includes the standard value range and central standard value of energy concentration under normal operating conditions, and serves as a reference benchmark for judging whether the energy distribution characteristics are abnormal. The energy deviation value is the quantitative result of the difference between the real-time extracted energy distribution characteristics and the preset energy reference value. It is used to accurately measure the degree to which the energy characteristics of the wire drawing path deviate from normal operating conditions. The larger the absolute value of the deviation value, the more significant the energy condition abnormality. In actual implementation, the corresponding preset energy reference value can be matched according to the real-time process parameters, and the standardized energy deviation value can be obtained by difference calculation and normalized deviation conversion. For example, first calculate the absolute value of the deviation between the energy distribution characteristics and the preset energy reference value, and then normalize it based on the standard deviation of this dimension of characteristics under normal operating conditions, mapping the deviation value to the interval of 0 to 1.

[0032] S150, based on the signal fluctuation characteristics and the preset signal reference value, obtains the signal deviation value in the precursor characteristic parameters.

[0033] It can be understood that the preset signal reference value is the standard benchmark parameter of signal fluctuation characteristics corresponding to different operating conditions under the preset normal mode, including the standard range and optimal center value of the instantaneous fluctuation degree of the signal under normal wire drawing conditions. The signal deviation value is a normalized deviation quantification index between the real-time signal fluctuation characteristics and the preset signal reference value, used to characterize the magnitude of the deviation of the instantaneous operational stability of the equipment from the normal state, directly reflecting the severity of the instantaneous disturbance of the operating condition. In actual implementation, a preset signal reference value matching the current wire drawing rate, metal wire specifications, and equipment running time can be retrieved, and the deviation degree of the real-time fluctuation characteristics can be calculated through a deviation normalization algorithm to obtain the signal deviation value. For example, first calculate the absolute value of the deviation between the signal fluctuation characteristics and the preset signal reference value, and then normalize it based on the standard deviation of this dimension of characteristics under normal operating conditions, mapping the deviation value to the interval between 0 and 1.

[0034] S160, based on the spectral distribution characteristics and the preset spectral reference value, obtain the spectral deviation value in the precursor characteristic parameters.

[0035] It is understandable that the preset spectrum reference value is the standard position and standard offset range of the spectrum distribution centroid corresponding to the standard operating state of the equipment under the preset normal mode. It is the core benchmark parameter for judging whether the frequency operating condition is normal. The spectrum deviation value is the quantitative result of the deviation between the real-time spectrum distribution centroid offset and the standard offset range. It is used to reflect the degree to which the equipment's operating frequency characteristics and mechanical vibration characteristics deviate from the normal design conditions. In actual implementation, the corresponding preset spectrum reference value can be matched according to the equipment model and wire drawing process conditions. The spectrum deviation value can be calculated by normalizing the centroid offset. For example, first calculate the absolute value of the deviation between the spectrum distribution characteristics and the preset spectrum reference value, and then normalize it based on the standard deviation of this dimension characteristic under normal operating conditions, mapping the deviation value to the 0 to 1 interval.

[0036] For example, in S160, based on the spectral distribution characteristics and a preset spectral reference value, the spectral deviation value in the precursor characteristic parameters is obtained, including: S161, extract the spectral centroid, spectral bandwidth and main frequency amplitude of the spectral distribution characteristics within the analysis window.

[0037] It can be understood that the spectral centroid, spectral bandwidth, and dominant frequency amplitude are three core spectral parameters characterizing the frequency domain characteristics of the wire drawing path's operating state. The spectral centroid is the core characteristic frequency position calculated by weighting all frequency components of the spectrum according to their energy proportions, used to characterize the overall shift trend of equipment vibration relative to the dominant frequency of the operating condition. The spectral bandwidth is the width of the distribution range of the effective operating frequency components, used to characterize the dispersion of the operating condition's frequency components. The dominant frequency amplitude is the peak amplitude of the core frequency corresponding to the spectral centroid, used to characterize the energy intensity of the dominant vibration under operating conditions. For example, under normal wire drawing conditions, the spectral centroid is stable, the spectral bandwidth is concentrated, and the dominant frequency amplitude is stable. When the die shows slight wear potential, side-frequency components will emerge, causing the spectral bandwidth to widen, the spectral centroid to shift, and the dominant frequency amplitude to attenuate.

[0038] S162, retrieve the preset spectrum reference value under the standard operating conditions of the corresponding wire drawing path. The preset spectrum reference value includes the standard spectrum centroid, standard spectrum bandwidth, and standard main frequency amplitude.

[0039] It is understandable that the preset spectrum reference value is a set of standard spectrum benchmark parameters obtained by fitting the equipment under long-term stable and abnormal operating conditions under the corresponding wire drawing path, copper wire specifications, and rated wire drawing rate. This set serves as a reference standard for judging whether real-time spectrum characteristics deviate from normal operating conditions, and includes three types of fixed benchmark parameters: standard spectrum centroid, standard spectrum bandwidth, and standard main frequency amplitude. This step is used to match the current process scenario and retrieve the appropriate normal spectrum benchmark, ensuring the scenario adaptability of deviation calculation and avoiding misjudgments caused by different process parameters.

[0040] S163, calculate the centroid difference between the spectral centroid and the standard spectral centroid, the bandwidth difference between the spectral bandwidth and the standard spectral bandwidth, and the amplitude difference between the main frequency amplitude and the standard main frequency amplitude, respectively.

[0041] It can be understood that the center-of-gravity difference, bandwidth difference, and amplitude difference are one-dimensional deviation quantification results between the real-time spectral parameters and the standard spectral reference parameters. Specifically, the center-of-gravity difference characterizes the degree of positional shift of the core operating frequency, the bandwidth difference characterizes the magnitude of the deviation in the divergence of frequency components, and the amplitude difference characterizes the degree of deviation in the strength of the dominant vibration energy. These three types of differences quantify frequency domain operating condition anomalies from three independent dimensions: frequency position, frequency distribution, and vibration energy. This step, through dimension-by-dimensional comparison and difference calculation, achieves a refined decomposition of spectral anomalies and accurately locates the specific type of frequency domain anomaly.

[0042] S164, based on the centroid difference, bandwidth difference, and amplitude difference, obtains the spectral deviation value in the precursor characteristic parameters.

[0043] It is understandable that the spectral deviation value is a comprehensive frequency domain anomaly quantification index obtained by integrating the centroid difference, bandwidth difference, and amplitude difference. It is used to uniformly characterize the degree to which the overall frequency domain operating conditions of the current wire drawing path deviate from the normal mode. It does not require weighted fusion of the differences of each dimension and can comprehensively cover various frequency domain hidden dangers such as frequency offset, spectral divergence, and abnormal main frequency energy. The comprehensive spectral deviation value is obtained by averaging the centroid difference, bandwidth difference, and amplitude difference after normalization.

[0044] S170 determines precursor characteristic parameters based on energy deviation, signal deviation, and spectral deviation.

[0045] It can be understood that energy deviation, signal deviation, and spectrum deviation correspond to abnormal deviations in three dimensions: energy operating conditions, time-domain stability, and frequency-domain matching, respectively. These three independently characterize different types of operational hazards, and deviations in different dimensions can corroborate and complement each other, covering various abnormal scenarios. The precursor characteristic parameter is a comprehensive quantitative index of anomaly precursors obtained by integrating three independent deviation information, capable of comprehensively evaluating the overall potential anomaly level of the wire-drawing path. In practical implementation, the correlation and anomaly characterization capabilities of the three-dimensional deviations can be combined, and the three types of deviations can be integrated and optimized by weighted summation (with the spectrum deviation value having the highest weight) to obtain the precursor characteristic parameter. The spectrum dimension has the highest weight because frequency domain characteristics change earliest and are most sensitive in the precursor stage, outputting parameter results that can uniformly characterize the overall precursor anomaly level.

[0046] By employing the steps S140 to S170 described above, it is helpful to accurately distinguish the deviation contribution of different anomaly types such as energy load anomaly, instantaneous fluctuation anomaly, and frequency condition anomaly, thereby improving the precision of precursor identification.

[0047] S200, based on precursor characteristic parameters, determines the load reduction index for the corresponding wire drawing path. The load reduction index characterizes the required rate reduction for the wire drawing path.

[0048] It is understandable that the precursory characteristic parameters precisely quantify the potential abnormality level of the wire drawing path. The higher the abnormality level, the greater the risk to equipment operation, and the greater the required rate reduction. The load reduction index is a standardized rate control quantification parameter matched to the abnormality precursor level. It is used to precisely define the specific magnitude, proportion, or level of rate reduction in the wire drawing path, achieving graded and precise load reduction control, unlike a one-size-fits-all forced load reduction mode. In actual implementation, a correspondence between precursory characteristic parameters and abnormality risk levels can be established, and then the corresponding rate reduction magnitude can be matched according to the risk level to finally determine the load reduction index suitable for the current operating condition. For example, minor precursory abnormalities are matched with small load reductions, moderate precursory abnormalities with moderate load reductions, and severe precursory abnormalities with significant load reductions, maximizing production efficiency while ensuring equipment safety.

[0049] As an optional embodiment of this application, S200, based on the precursor characteristic parameters, determines the load reduction index of the corresponding wire drawing path, including: S210, based on the deviation values ​​of each dimension of the precursor feature parameters, determines the anomaly score value of the precursor feature parameters through a preset multidimensional anomaly scoring function. The multidimensional anomaly scoring function is a mathematical function that takes the deviation values ​​of each dimension as input variables and the anomaly score value as the output result. The multidimensional anomaly scoring function includes individual contribution terms for each dimension's deviation value and cross-contribution terms between the deviation values ​​of each dimension. Cross-contribution terms generate a positive contribution when deviations in the same direction occur simultaneously in multiple dimensions.

[0050] The input variables are three standardized parameters: energy deviation, signal deviation, and spectrum deviation, calculated in real time. The output anomaly score is a comprehensive score quantifying the overall severity of anomalies in the wire drawing path; a higher score indicates a more serious operational hazard and a higher equipment operational risk. Single contribution items are anomaly score calculation items corresponding independently to the deviation values ​​of each dimension, used to reflect the contribution of anomalies in a single dimension. Cross-contribution items are calculation items involving the correlation and coupling of multi-dimensional deviation values, used to reflect the superimposed risk of synchronous anomalies in multiple dimensions, avoiding the limitations of single-dimensional detection. Positive contribution means that when multiple dimensions deviate in the same direction, the cross-contribution item outputs a positive score, improving the overall anomaly score and accurately identifying real composite anomaly hazards. If only a single dimension deviates while the other dimensions are normal, the cross-contribution item has no positive score, avoiding misjudgment. In actual implementation, a multi-dimensional anomaly score mathematical formula containing single-item weighted items and multi-dimensional cross-coupling items can be pre-constructed. The real-time three-dimensional deviation values ​​are substituted into the formula, and the standardized anomaly score is calculated by combining the corresponding weight coefficients. For example, when the deviations in energy, signal, and spectrum increase synchronously and in the same direction, it represents a complex anomaly hazard; the cross-contribution term takes effect, and the anomaly score significantly improves. When there is only a small deviation in a single dimension, there is no cross-contribution, but the score improves slightly, accurately distinguishing between minor disturbances and real hazards. It should be noted that the multidimensional anomaly scoring function... .in For the individual weights of each dimension, The cross-contribution coefficient is only effective when the three-dimensional synchronous direction deviates.

[0051] In one possible implementation, S210, based on the deviation values ​​of each dimension of the precursor feature parameters, an anomaly score value of the precursor feature parameters is determined through a preset multidimensional anomaly scoring function, including: S211, based on the preset individual weights corresponding to the deviation values ​​of each dimension in the precursor feature parameters, obtain the individual anomaly contribution item for each dimension deviation value. The individual anomaly contribution item is used to characterize the degree of independent influence of a single-dimensional operating condition anomaly on the overall anomaly.

[0052] It can be understood that the preset individual weights are fixed coefficients pre-set based on the sensitivity of each dimension's deviation value to wire drawing die anomalies and impact hazards. These correspond to the independent anomaly contribution ratios of energy deviation, signal deviation, and spectrum deviation, respectively, and are used to characterize the independent influence of a single-dimensional operating condition anomaly on the overall failure risk. The individual anomaly contribution item is the result of the coupled calculation of the single-dimensional deviation value and its corresponding individual weight, used to quantify the risk score of independent anomalies in each dimension, achieving precise decomposition of single-dimensional anomaly contributions. The preset individual weights for each dimension's deviation value are set according to its sensitivity to impact hazards in the wire drawing die, with the spectrum deviation value having the highest weight (e.g., set to 0.45), followed by energy deviation and signal deviation values ​​(e.g., set to 0.3 and 0.25 respectively). The individual anomaly contribution item is the product of the deviation value and its corresponding weight.

[0053] S212, based on deviation values ​​in at least two dimensions, obtain multi-dimensional cross-anomaly contribution terms. These cross-anomaly contribution terms only take effect when the corresponding dimension deviations are in the same direction and their magnitudes match. The more multi-dimensional synchronous anomalies there are, the larger the gain coefficient of the cross-contribution terms.

[0054] It can be understood that the multi-dimensional cross-anomaly contribution item is a collaborative risk gain item constructed based on the correlation and coupling characteristics of deviation values ​​in different dimensions. Here, "same-direction deviation" means that the deviation values ​​in different dimensions are simultaneously positive or simultaneously negative, representing that the abnormal operating conditions have a unified trend. "Amplitude matching" means that after normalization, the absolute value of the difference between any two pairs of deviation values ​​in different dimensions is less than a preset matching tolerance threshold, which is a normalized value between 0.15 and 0.25. For example, when the normalized result of the energy deviation value is 0.6, the normalized result of the signal deviation value is 0.7, and the normalized result of the spectrum deviation value is 0.65, and the difference between any two pairs of these three values ​​is less than 0.2, it is determined to be amplitude matching, and the cross-anomaly contribution item takes effect. S213, integrate individual anomaly contribution items and cross-anomaly contribution items to obtain anomaly score values ​​within the standard range. It is understandable that the anomaly score is a standardized comprehensive risk score obtained by integrating all independent individual anomaly contributions and multidimensional collaborative cross-contributions. It is a core quantitative indicator that uniformly represents the overall anomaly risk level of the wire drawing path and the credibility of force impact precursors. The score is within a preset standard range, ensuring the stability and comparability of the scoring results. This step takes into account both single-dimensional independent anomaly risks and multi-dimensional coupled and superimposed risks, achieving accurate differentiation between real anomalies and false disturbances.

[0055] S220, based on the preset mapping relationship between abnormal score values ​​and load reduction indicators, determines the load reduction indicators.

[0056] It is understandable that the preset mapping relationship is a standardized correspondence rule obtained by fitting massive historical operating condition data, anomaly score data, and load reduction control effect data. This can take the form of a piecewise function, a graded comparison table, or a linear fitting model, establishing a precise correlation between anomaly score values ​​and the rate reduction magnitude. The preset mapping relationship comprehensively considers the fault risk level, equipment tolerance, and production process adaptability corresponding to different anomaly scores, ensuring that the load reduction control intensity is perfectly matched with the anomaly risk. In actual implementation, a piecewise mapping method is adopted, with different load reduction magnitudes corresponding to different anomaly score ranges. For example, a score value below 0.2 corresponds to no load reduction, 0.2 to 0.4 corresponds to a 5% load reduction, 0.4 to 0.6 corresponds to a 15% load reduction, 0.6 to 0.8 corresponds to a 25% load reduction, 0.8 to 0.95 corresponds to a 40% load reduction, and above 0.95 corresponds to a warning shutdown. The boundary values ​​of each segment can be calibrated on-site according to actual operating conditions and equipment characteristics.

[0057] By employing steps S210 to S220, high-risk hazards of multi-dimensional synchronous anomalies can be accurately identified, distinguishing between false disturbances and real faults, and avoiding excessive or insufficient load reduction. A pre-defined standardized mapping relationship enables automated and precise conversion of anomaly scores to load reduction indicators, balancing equipment operational safety and production efficiency.

[0058] S300, based on the load reduction index, obtains the target operating rate of the corresponding wire drawing path.

[0059] It can be understood that the target operating rate is the final adjusted standard rate calculated based on the load reduction index. The load reduction index clarifies the reduction range of the wire drawing path rate, and the operating rate is the final real-time wire drawing speed executed by the equipment after load reduction adjustment. It is the optimal process rate to adapt to current abnormal potential risks and ensure stable equipment operation. In actual implementation, the adjusted target operating rate can be calculated by combining the correspondence between the equipment's rated rate, the current real-time operating rate, and the load reduction index, thus achieving adaptive speed adjustment based on operating conditions.

[0060] As an optional embodiment of this application, S300, based on the load reduction index, obtains the target operating rate of the corresponding wire drawing path, including: S310 determines the rate transition strategy based on the load reduction index. The rate transition strategy is used to control the process of transitioning the rate from the current operating rate to the target operating rate.

[0061] It is understandable that the rate transition strategy is a set of control rules that regulate the rate switching process. It includes core parameters such as the rate transition duration, the step size of the speed adjustment in stages, and the transition mode of uniform speed or gradual reduction. This is used to avoid problems such as wire breakage due to sudden and large rate changes, sudden changes in equipment load, and excessive process fluctuations. In practice, differentiated transition strategies can be matched according to the magnitude of the load reduction target. The smaller the load reduction, the shorter the transition duration and the larger the speed adjustment step size. The larger the load reduction and the higher the risk, the longer the transition duration and the smaller the speed adjustment step size, using a gradual reduction mode for a smooth transition. For example, a short-duration uniform speed adjustment strategy can be used for small load reductions, while a multi-stage, stepped gradual reduction strategy can be used for large, high-risk load reductions.

[0062] S320, based on the rate transition strategy and the current operating rate, obtains the target operating rate of the corresponding wire drawing path.

[0063] This step, as understood, involves a phased and gradual reduction of the current operating rate according to a preset rate transition rule. The final output is the dynamic operating rate of the device in real time, achieving smooth rate transition control. In practice, the device operating rate can be iteratively updated in real time based on the step size and duration parameters of the rate transition strategy until the target load reduction rate is reached, completing stable control throughout the process. For example, under a stepped transition strategy, the rate is gradually reduced at fixed time intervals, approaching the target rate without drastic fluctuations.

[0064] By adopting the above steps S310 to S320, it is helpful to achieve a smooth and stable switching of wire drawing rate, effectively avoiding secondary problems such as wire breakage, sudden tension changes, and equipment impact wear caused by sudden rate changes. While realizing load reduction and risk avoidance under abnormal working conditions, it maximizes the stability of the wire drawing process and the consistency of product quality.

[0065] As an optional embodiment of this application, S300, after obtaining the target operating rate of the corresponding wire drawing path based on the load reduction index, further includes: S330 records the precursor characteristic parameters when generating the load reduction index as precursor samples to be verified.

[0066] It is understandable that the precursor samples to be verified are a complete set of precursor feature data that triggered load reduction control during this regulation process. This includes complete correlated data such as three-dimensional deviation values, mutual corroboration, precursor feature parameters, anomaly scores, and corresponding load reduction indicators. This data forms the foundation for subsequent verification of the accuracy of precursor identification and optimization of the algorithm model. In actual implementation, a dedicated sample database can be established to bind and store the precursor feature data, operating condition timestamps, wire drawing path numbers, and regulation parameters corresponding to each load reduction control, forming a structured precursor sample to be verified.

[0067] S340, when the signal fluctuation characteristics of the running status data of the wire drawing path corresponding to the precursor sample to be verified exceed the preset strong anomaly threshold within the preset observation time, the precursor sample to be verified is recorded as a valid precursor sample. When it does not exceed the preset strong anomaly threshold, the precursor sample to be verified is recorded as an invalid precursor sample.

[0068] It is understandable that the preset observation period is a fault verification cycle set according to the response characteristics of the wire drawing process. This is used to observe whether the operating condition will develop into a real strong anomaly after the initial warning, avoiding judgment bias caused by instantaneous observation. The preset strong anomaly threshold is the critical threshold for an operating condition where the equipment clearly malfunctions and the process is severely out of tolerance. This is different from the threshold for minor precursor anomalies. Reaching the preset strong anomaly threshold indicates that a substantial fault has occurred in the wire drawing path. Valid precursor samples indicate that the warning accurately predicted the subsequent actual fault, and the warning logic is effective and reliable. Invalid precursor samples indicate that the warning only captured a minor instantaneous disturbance and did not develop into a substantial fault; these are redundant warnings or minor interference warnings. In actual implementation, after generating the samples to be verified, the amplitude of the corresponding wire drawing path's operating status data is continuously monitored. After the preset observation period ends, the signal fluctuation characteristics are compared with the strong anomaly threshold to complete the sample validity classification and labeling. For example, if the operating condition continues to deteriorate within the observation period after the warning, and the signal fluctuation characteristics exceed the strong anomaly threshold, it is labeled as a valid sample. If the operating condition quickly recovers to stability after the warning and there is no substantial anomaly, it is labeled as an invalid sample.

[0069] S350 adjusts the function parameters of the multidimensional anomaly scoring function based on valid and invalid precursor samples.

[0070] It is understandable that the function parameters, including the weight coefficients of each dimension of the multidimensional anomaly scoring function, the coefficients of cross-contribution terms, and the anomaly scoring threshold, are optimizable parameters that directly determine the accuracy of anomaly scoring and the sensitivity of early warning. The core of this step is to iteratively optimize the model parameters using real-world working condition sample data, thereby enhancing the ability to identify effective early warnings and mitigating the interference of ineffective disturbances. In practice, feature difference analysis of massive amounts of effective and ineffective samples can be used to specifically adjust the function parameters, improving the model's accuracy in identifying real anomaly precursors and reducing the false alarm rate.

[0071] By adopting the above steps S330 to S350, the accuracy of precursor identification and load reduction control can be continuously improved, the algorithm model can be adaptively iteratively upgraded, and the probability of false and missed warnings can be reduced in the long term.

[0072] In one possible implementation, S350 adjusts the function parameters of the multidimensional anomaly scoring function based on valid and invalid precursor samples, including: S351, based on valid precursor samples, obtains the valid distribution statistics of deviation values ​​for each dimension.

[0073] It can be understood that the effective distribution statistics are quantitative parameters obtained by statistically analyzing the distribution patterns of energy deviation, signal deviation, and spectral deviation in all effective precursor samples. They include core statistical indicators such as the mean, variance, distribution range, and peak characteristics of deviations in each dimension, accurately reflecting the inherent distribution patterns of deviations in each dimension under real fault precursors. In practice, the three-dimensional deviation data of all effective precursor samples in the database can be extracted in batches. Through statistical fitting and cluster analysis, the effective distribution statistics of deviations in each dimension can be calculated, constructing a characteristic distribution model of the real anomaly. For example, the concentrated distribution range of deviations in each dimension in the effective samples can be statistically analyzed to clarify the standard deviation characteristic range corresponding to the real fault.

[0074] S352, based on inefficient precursor samples, obtains the inefficient distribution statistics of deviation values ​​for each dimension.

[0075] It can be understood that the inefficient distribution statistics are quantitative parameters obtained by statistically analyzing the distribution patterns of deviations in the three dimensions across all inefficient precursor samples. These parameters include statistical indicators such as the mean, dispersion, and distribution interval of each dimension's deviation, accurately reflecting the distribution patterns of false deviations caused by invalid disturbances and minor interferences. In practice, the three-dimensional deviation data of inefficient samples are extracted in batches, and the inefficient distribution statistics are obtained through statistical analysis. A characteristic distribution model of invalid anomalies is then constructed to distinguish the characteristic differences between real faults and false disturbances. For example, the deviation values ​​of inefficient samples generally have small amplitudes, large dispersion, and irregular distributions, which are significantly different from the regular deviation distribution of efficient samples.

[0076] S353 adjusts the function parameters of the multidimensional anomaly scoring function based on the discriminant strength between the effective and ineffective distribution statistics. Here, the discriminant strength represents the ratio of the mean difference between the effective and ineffective distribution statistics across each dimension to the sum of their dispersions.

[0077] It can be understood that the mean difference is the difference between the average deviation values ​​of corresponding dimensions of valid and invalid samples, representing the core feature differences between the two types of samples. Dispersion is the variance or standard deviation of the deviation values ​​of the two types of samples, representing the degree of fluctuation and dispersion of the sample data. Discrimination is the core indicator for quantifying the significance of the feature differences between the two types of samples. Higher discrimination means the dimension can accurately distinguish between real faults and false disturbances, resulting in higher detection value. Lower discrimination means the dimension's feature recognition is low, making it prone to misjudgment. In practice, the discrimination of valid and invalid distributions can be calculated dimension by dimension. For dimensions with high discrimination, the function weight coefficient and cross-contribution ratio can be increased. For dimensions with low discrimination and poor recognition, the weights can be appropriately reduced, the anomaly scoring function parameters can be corrected, and the model's recognition ability can be optimized. For example, if the two types of samples have extremely high discrimination in the spectrum dimension, the individual weight of the spectrum deviation value can be increased. If the signal dimension has low discrimination, the weights can be fine-tuned to reduce misjudgment interference. The discrimination formula can be... .in, For dimensions that deviate from the mean, The standard deviation of the sample is denoted as . To determine the level of differentiation. The higher the level of differentiation, the higher the weight of the corresponding dimension.

[0078] By adopting the above steps S351 to S353, it is helpful to specifically enhance the detection effect of high-discrimination dimensions, weaken the interference of low-discrimination dimensions, accurately improve the ability of the multi-dimensional anomaly scoring function to distinguish between real anomaly precursors and invalid disturbances, continuously optimize the model detection accuracy, and enable the entire wire drawing control method to have the ability to learn adaptively and optimize itself, so as to adapt to complex and ever-changing production conditions in the long term.

[0079] S400 controls the speed of a multi-head wire drawing machine based on the target operating speed.

[0080] It is understandable that, based on the target operating speed, the speed control of the multi-head wire drawing machine is performed. By controlling the speed of the multi-head wire drawing machine according to the target operating speed, the protective load of the multi-head wire drawing machine mold can be achieved. The speed can be adjusted to the target operating speed through the control panel of the multi-head wire drawing machine mold.

[0081] 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.

[0082] Corresponding to the protective load reduction control method for copper wire multi-head drawing machine molds described in the above embodiments, this application also provides a protective load reduction control device for copper wire multi-head drawing machine molds. Each unit of this device can realize each step of the protective load reduction control method for copper wire multi-head drawing machine molds. Figure 3The diagram shows a structural block diagram of the protective load reduction control device for the copper wire multi-head drawing machine mold provided in the embodiment of this application. For ease of explanation, only the parts related to the embodiment of this application are shown.

[0083] Reference Figure 3 The device includes: The acquisition unit is used to extract multi-dimensional operational features from the operational status data of each parallel drawing path of the multi-head wire drawing machine, and to determine the precursor feature parameters of each drawing path based on the multi-dimensional operational features. The precursor feature parameters characterize the degree to which the operational status data deviates from a preset normal pattern within a preset analysis window.

[0084] The index unit is used to determine the load reduction index for the corresponding wire drawing path based on the precursor characteristic parameters. The load reduction index characterizes the required rate reduction for the wire drawing path.

[0085] The operating unit is used to obtain the target operating rate of the corresponding wire drawing path based on the load reduction index.

[0086] The control unit is used to control the speed of the multi-head wire drawing machine based on the target operating speed.

[0087] 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.

[0088] 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 device can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0089] Figure 4 This is a schematic structural diagram of an electronic device provided in an exemplary embodiment. For example... Figure 4As shown, the electronic device 400 mainly consists of a communication interface 402, a user interface 404, a processor 406, and a data storage 408. These components are interconnected and communicate with each other through a system bus, network, or other connection mechanism 410. The communication interface 402 enables the electronic device 400 to communicate with other devices, access networks, and transmission networks via analog or digital modulation. For example, the communication interface 402 may include a chipset and antenna for wireless communication with a radio access network or access point. Furthermore, the communication interface 402 can be a wired interface such as Ethernet, Token Ring, or a USB port, or a wireless interface such as Wi-Fi, Bluetooth, Global Positioning System (GPS), or a wide-area wireless interface (e.g., WiMAX or LTE). Of course, the communication interface 402 can also support other forms of physical layer interfaces and standard or proprietary communication protocols. The communication interface 402 may also include multiple physical communication interfaces, such as Wi-Fi interfaces, Bluetooth interfaces, and wide-area wireless interfaces.

[0090] User interface 404 includes receiving user input and providing output to the user. Therefore, user interface 404 may include input components such as a keyboard, touch-sensitive or presence-sensitive panel, computer mouse, trackball, joystick, microphone, still camera, and video camera, and output components such as a display screen (which may be combined with a touch-sensitive panel), CRT, LCD, LED, display using DLP technology, printer, and other similar devices known or developed in the future. User interface 404 may also generate auditory output via speakers, speaker jacks, audio output ports, audio output devices, headphones, and other similar devices known or developed in the future. In some embodiments, user interface 404 may include software, circuitry, or other forms of logic capable of transmitting and receiving data from external user input / output devices. Additionally or alternatively, electronic device 400 may support remote access from other devices via communication interface 402 or another physical interface (not shown). User interface 404 may be configured to receive user input, the position and movement of which may be indicated by indicators or cursors described herein. User interface 404 may also be configured as a display device for rendering or displaying text fragments.

[0091] Processor 406 may contain one or more general-purpose processors and / or special-purpose processors.

[0092] Data storage 408 may include one or more volatile and / or non-volatile storage components and may be integrated wholly or partially with processor 406. Data storage 408 may include removable and non-removable components.

[0093] Processor 406 is capable of executing program instructions 418 (e.g., compiled or uncompiled program logic and / or machine code) stored in data storage 408 to perform the various functions described herein. Data storage 408 may comprise a non-transitory computer-readable medium on which program instructions are stored, which, when executed by electronic device 400, enable electronic device 400 to perform any methods, processes, or functions disclosed in this specification and / or the accompanying drawings. Processor 406 executing program instructions 418 may result in processor 406 using data 412.

[0094] For example, program instructions 418 may include an operating system 422 (e.g., an operating system kernel, device drivers, and / or other modules) installed on electronic device 400, and one or more application programs 420 (e.g., a wire drawing machine condition monitoring application, a time-frequency analysis application, or a load reduction control application). Similarly, data 412 may include operating system data 416 and application data 414. Operating system data 416 is primarily accessible to operating system 422, while application data 414 is primarily accessible to one or more application programs 420. Application data 414 may reside in a file system that is visible or hidden from the user of electronic device 400.

[0095] Application 420 can communicate with operating system 422 through one or more application programming interfaces (APIs). These APIs help application 420 read and / or write application data 414, transmit or receive information via communication interface 402, receive or display information on user interface 404, etc.

[0096] In some terminology, application 420 may be simply referred to as "app". Furthermore, application 420 can be downloaded to electronic device 400 through one or more online app stores or app markets. However, applications can also be installed on electronic device 400 in other ways, such as through a web browser or a physical interface on electronic device 400 (e.g., a USB port).

[0097] Based on the above device structure, an exemplary embodiment of this specification also provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor executes the executable instructions to implement the steps of the method as described in any of the above embodiments.

[0098] For ease of description, the protective load reduction control device for the copper wire multi-head drawing machine mold described in this specification is divided into various modules or units based on their functions. Of course, when implementing one or more of these specifications, the functions of each module or unit can be implemented in the same or different software and / or hardware, or a module that performs the same function can be implemented by a combination of multiple sub-modules or sub-units. For example, the division of units is merely a logical functional division; in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0099] What those skilled in the art will understand is: In this specification, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, product, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, product, or apparatus. Without further limitation, the presence of additional identical or equivalent elements in a process, method, product, or apparatus that includes said elements is not excluded.

[0100] In this specification, “a,” “an,” and “the” do not specifically refer to the singular, but may also include the plural.

[0101] In this specification, ordinal numbers such as "first," "second," etc., do not necessarily indicate order; they are often used to distinguish between objects. For example, "first server" and "second server" usually refer to two servers. To differentiate between these two servers, they are described as "first server" and "second server." Of course, sometimes these two servers may be the same server.

[0102] In this specification, unless explicitly stated otherwise, "receiving and sending data" does not necessarily mean direct receiving and sending; it can also mean indirect receiving and sending. For example, A receiving data sent by B can be understood as A directly receiving the data sent by B, or it can be understood as A indirectly receiving the data sent by B through other entities such as C. Similarly, B sending data to A can be understood as B sending the data directly to A, or it can be understood as B indirectly sending the data to A through other entities such as C. Here, C can be one entity, or it can be two or more entities.

[0103] In this specification, unless explicitly stated otherwise, the relationships between structures can be direct or indirect. For example, when describing "A is connected to B," unless it is explicitly stated that A and B are directly connected, it should be understood that A can be directly connected to B or indirectly connected to B. Similarly, when describing "A is on top of B," unless it is explicitly stated that A is directly above B (AB is adjacent and A is above B), it should be understood that A can be directly above B or indirectly above B (AB is separated by other elements, and A is above B). And so on.

[0104] This specification uses specific terms to describe embodiments thereof. For example, "one embodiment" and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an alternative embodiment" mentioned twice or more in different places in this specification do not necessarily refer to the same embodiment. Furthermore, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples, without contradiction.

[0105] Although one or more embodiments of this specification provide method steps as described in the embodiments or flowcharts, it is understood that the order of steps listed in the embodiments or flowcharts is only one of many possible execution orders and does not represent the only execution order. Therefore, when the claims involve method steps, any changes or adjustments to the order of such steps, or the parallelism between steps, are also within the scope of protection of the claims.

Claims

1. A protective load reduction control method for a multi-head copper wire drawing machine mold, characterized in that, include: Based on the operating status data of each parallel wire drawing path of the multi-head wire drawing machine, multi-dimensional operating features of the operating status data are extracted, and precursor feature parameters of each wire drawing path are determined based on the multi-dimensional operating features; wherein, the precursor feature parameters characterize the degree to which the operating status data deviates from the preset normal mode within the preset analysis window; Based on the aforementioned precursor characteristic parameters, the load reduction index for the corresponding wire drawing path is determined; wherein, the load reduction index characterizes the required rate reduction range for the wire drawing path; Based on the load reduction index, the target operating rate of the corresponding wire drawing path is obtained; Based on the target operating rate, the multi-head wire drawing machine is speed controlled.

2. The method as described in claim 1, characterized in that, The multi-dimensional operational features of the operational status data, based on the operational status data of each parallel drawing path of the multi-head wire drawing machine, are extracted, including: Based on the operational status data, energy distribution characteristics in the multidimensional operational features are obtained; wherein, the energy distribution characteristics characterize the degree of energy concentration of the operational status data in the analysis window; Based on the operational status data, signal fluctuation features are obtained in the multidimensional operational features; wherein, the signal fluctuation features characterize the instantaneous fluctuation degree of the operational status data; Based on the operational status data, the spectral distribution characteristics in the multidimensional operational features are obtained; wherein, the spectral distribution characteristics characterize the degree of shift of the centroid of the frequency component distribution of the operational status data.

3. The method as described in claim 2, characterized in that, The preset normal mode includes a preset energy reference value, a preset signal reference value, and a preset spectrum reference value. The determination of the precursor characteristic parameters for each wire drawing path based on the multi-dimensional operating characteristics includes: Based on the energy distribution characteristics and the preset energy reference value, the energy deviation value in the precursor characteristic parameters is obtained; Based on the signal fluctuation characteristics and the preset signal reference value, the signal deviation value in the precursor characteristic parameters is obtained; Based on the spectral distribution characteristics and the preset spectral reference value, the spectral deviation value in the precursor characteristic parameters is obtained; The precursor characteristic parameters are determined based on the energy deviation value, the signal deviation value, and the spectrum deviation value.

4. The method as described in claim 3, characterized in that, The step of obtaining the spectral deviation value in the precursor characteristic parameters based on the spectral distribution characteristics and the preset spectral reference value includes: Extract the spectral centroid, spectral bandwidth, and dominant frequency amplitude of the spectral distribution characteristics within the analysis window; Retrieve the preset spectrum reference value under the standard working condition of the corresponding wire drawing path; wherein, the preset spectrum reference value includes the standard spectrum centroid, standard spectrum bandwidth and standard main frequency amplitude; Calculate the centroid difference between the centroid of the spectrum and the centroid of the standard spectrum, the bandwidth difference between the spectrum bandwidth and the standard spectrum bandwidth, and the amplitude difference between the main frequency amplitude and the standard main frequency amplitude, respectively. Based on the centroid difference, the bandwidth difference, and the amplitude difference, the spectral deviation value in the precursor characteristic parameters is obtained.

5. The method as described in claim 3, characterized in that, The determination of the load reduction index for the corresponding wire drawing path based on the precursor characteristic parameters includes: Based on the deviation values ​​of each dimension in the precursor feature parameters, the anomaly score value of the precursor feature parameters is determined by a preset multidimensional anomaly scoring function; wherein, the anomaly score value represents the overall severity and confidence level of potential anomalies in the current wire drawing path. The load reduction index is determined based on the preset mapping relationship between the abnormal score value and the load reduction index.

6. The method as described in claim 5, characterized in that, The step of determining the anomaly score value of the precursor feature parameters based on the deviation values ​​of each dimension in the precursor feature parameters and through a preset multidimensional anomaly scoring function includes: Based on the preset individual weights corresponding to the deviation values ​​of each dimension in the precursor feature parameters, individual anomaly contribution items for each dimension deviation value are obtained; wherein, the individual anomaly contribution item is used to characterize the degree of independent influence of a single dimension operating condition anomaly on the overall anomaly; Based on deviation values ​​in at least two dimensions, a multi-dimensional cross-anomaly contribution term is obtained; wherein, the cross-anomaly contribution term only takes effect when the deviations in the corresponding dimensions are in the same direction and the magnitudes match; By integrating the individual anomaly contribution items and the cross-anomaly contribution items, an anomaly score value within the standard range is obtained.

7. The method as described in claim 5, characterized in that, After obtaining the target operating rate of the corresponding wire drawing path based on the load reduction index, the method further includes: Record the precursor characteristic parameters when generating the load reduction index as precursor samples to be verified. When the signal fluctuation characteristics of the operating status data of the wire drawing path corresponding to the precursor sample to be verified exceed a preset strong anomaly threshold within a preset observation period, the precursor sample to be verified is recorded as a valid precursor sample; when it does not exceed the preset strong anomaly threshold, the precursor sample to be verified is recorded as an invalid precursor sample. Based on the valid precursor samples and the invalid precursor samples, the function parameters of the multidimensional anomaly scoring function are adjusted.

8. The method as described in claim 7, characterized in that, The step of adjusting the function parameters of the multidimensional anomaly scoring function based on the valid precursor samples and the invalid precursor samples includes: Based on the aforementioned effective precursor samples, the effective distribution statistics of the deviation values ​​in each dimension are obtained; Based on the aforementioned ineffective precursor samples, the ineffective distribution statistics of the deviation values ​​in each dimension are obtained; Based on the discriminant value between the effective distribution statistics and the ineffective distribution statistics, the function parameters of the multidimensional anomaly scoring function are adjusted; wherein, the discriminant value represents the ratio of the mean difference between the effective distribution statistics and the ineffective distribution statistics in each dimension to the sum of their dispersions.

9. The method as described in claim 1, characterized in that, The step of obtaining the target operating rate of the corresponding wire drawing path based on the load reduction index includes: Based on the load reduction index, a rate transition strategy is determined; wherein, the rate transition strategy is used to control the process of the rate transitioning from the current operating rate to the target operating rate; Based on the rate transition strategy and the current operating rate, the target operating rate of the corresponding wire drawing path is obtained.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 9.

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