A method and system for online monitoring of current in a melting furnace

Abstract

This application belongs to the field of current monitoring technology and discloses a method and system for online monitoring of current in a melting furnace. The method includes: first, continuously acquiring current signals from the power supply circuit of the melting furnace to form a current time series; then, determining the complete liquid phase holding stage based on its variation characteristics; subsequently, extracting low-frequency current fluctuation characteristics, such as the characteristic sequence of energy change over time within a preset low-frequency range, from the current time series of this stage; obtaining a set of quantitative parameters reflecting the attenuation state of low-frequency fluctuations based on these characteristics; and finally, determining whether the melt has reached a homogenization state suitable for rolling a predetermined thickness of copper strip foil by comparing the set of quantitative parameters with preset judgment conditions. This method requires no additional detection equipment, can accurately determine the homogenization state of the melt online, ensuring the production quality and efficiency of ultra-thin copper strip foil, and has good engineering applicability.

Description

Technical Field

[0001] This application relates to the field of current monitoring technology, and more specifically, to a method and system for online monitoring of current in a melting furnace. Background Technology

[0002] In the manufacturing of flexible copper strip foil for new energy vehicles, especially in the production of ultra-thin copper strip foil with a predetermined thickness of no more than 0.05 mm, the compositional and temperature uniformity of the melt directly determines the dimensional stability and reliability of the subsequent products. The manufacturing of this type of ultra-thin strip foil typically involves melting copper in an industrial frequency cored furnace and forming it through a semi-continuous casting-rolling-annealing process. If there is implicit component segregation or temperature gradient within the melt, it will be significantly amplified during the subsequent rolling process, leading to increased thickness fluctuations, edge cracks, and other quality defects.

[0003] In existing technologies, determining whether a melt has reached a homogenized state mainly relies on fixed holding times or operator experience, with some processes using only a single temperature parameter for control. These methods cannot effectively distinguish between the state differences of "macroscopically completely liquid phase" and "internal homogenization," making it difficult to identify hidden non-uniformities within the melt and lacking objective, quantitative online judgment criteria.

[0004] Meanwhile, improving the accuracy of traditional monitoring solutions often requires the addition of complex testing equipment, which not only increases production costs but also necessitates modifications to existing production lines. This results in poor adaptability and significant challenges in widespread adoption. Therefore, addressing the technical pain points of existing judgment methods—such as high subjectivity, insufficient accuracy, and poor engineering applicability—to ensure the production quality and efficiency of ultra-thin copper foil strips has become an urgent issue. Summary of the Invention

[0005] In response, this application provides a method and system for online monitoring of melting furnace current, which at least partially solves the above-mentioned technical problems.

[0006] This application provides a method for online monitoring of current in a melting furnace, comprising the following steps: S1: continuously acquiring the current signal of the power supply circuit during the operation of the melting furnace to form a current time series; S2: Determine the complete liquid phase heat preservation stage based on the variation characteristics of the current time series; S3: Extract low-frequency current fluctuation characteristics reflecting the homogenization state inside the melt from the current time series corresponding to the complete liquid phase heat preservation stage. The low-frequency current fluctuation characteristics include at least: a feature sequence of energy change over time within a preset low-frequency range obtained from the spectrum analysis of the current signal. S4: Based on the low-frequency current fluctuation characteristics, obtain a set of quantitative parameters reflecting the attenuation state of the low-frequency current fluctuation; S5: Based on the comparison results between the set of quantitative parameters and the preset judgment conditions, determine whether the melt has reached a homogenization state suitable for rolling a copper strip foil of a predetermined thickness.

[0007] In one possible embodiment, determining the complete liquid phase heat preservation stage based on the variation characteristics of the current time series includes: preprocessing the current time series to remove abnormal data and perform smoothing; calculating the current change rate, current fluctuation amplitude, and current fluctuation coefficient within a continuous time window based on the preprocessed current time series; and comparing the calculated current change rate, current fluctuation amplitude, and current fluctuation coefficient with a preset melting stage determination threshold to determine the complete liquid phase heat preservation stage.

[0008] In one possible embodiment, extracting low-frequency current fluctuation features reflecting the homogenization state inside the melt includes: performing frequency domain transformation on the current time series corresponding to the complete liquid phase heat preservation stage to obtain the spectrum of the current signal; calculating the energy value in the spectrum within a preset low-frequency range; and using a sliding window to traverse the current time series of the complete liquid phase heat preservation stage to obtain the energy value corresponding to each window, forming a feature sequence of energy changing with time within the low-frequency range.

[0009] In one possible embodiment, extracting low-frequency current fluctuation characteristics reflecting the homogenization state inside the melt further includes: detecting peak points and valley points in the current time series during the fully liquid phase heat preservation stage; calculating the time interval between adjacent peaks and valleys based on the time information of the detected peak points and valley points; calculating statistical characteristic parameters of the time interval, the statistical characteristic parameters including the coefficient of variation of the time interval, and forming a characteristic sequence of the coefficient of variation changing over time.

[0010] In one possible embodiment, the set of quantization parameters includes at least one of the instantaneous decay rate, average decay rate, and cumulative decay amount calculated based on the energy characteristic sequence within the low-frequency range; and / or at least one of the rate of change and average rate of change calculated based on the coefficient of variation characteristic sequence of the time interval.

[0011] In one possible embodiment, the preset determination conditions include: Condition 1: the energy values ​​in the low-frequency range corresponding to a preset number of consecutive sliding windows all fall into a preset stable energy range; Condition 2: the absolute value of the low-frequency energy decay rate corresponding to the preset number of consecutive sliding windows is less than a preset stable decay rate threshold; Condition 3: the coefficient of variation of the time interval corresponding to the preset number of consecutive sliding windows is less than a preset stability threshold; when the set of quantization parameters simultaneously satisfies Condition 1, Condition 2, and Condition 3, it is determined that the melt has reached the homogenization state.

[0012] In one possible embodiment, after step S5, the process further includes: S6: when it is determined that the melt has reached the homogenization state, an instruction is output to allow the melt to enter the next process; when it is determined that the melt has not reached the homogenization state, steps S3 to S5 are continued until the homogenization state is reached or the preset maximum heat preservation time is reached.

[0013] In another aspect, this application also provides an online current monitoring system for a melting furnace, comprising: a data acquisition module for continuously acquiring current signals from the power supply circuit during the operation of the melting furnace to form a current time series; a complete liquid phase holding stage determination module for determining the complete liquid phase holding stage based on the changing characteristics of the current time series; a low-frequency current fluctuation feature extraction module for extracting low-frequency current fluctuation features reflecting the homogenization state inside the melt from the current time series corresponding to the complete liquid phase holding stage, wherein the low-frequency current fluctuation features include at least: a feature sequence of energy changes over time within a preset low-frequency range obtained from spectral analysis of the current signal; a quantization parameter set acquisition module for acquiring a quantization parameter set reflecting the attenuation state of the low-frequency current fluctuations based on the low-frequency current fluctuation features; and a judgment module for judging whether the melt has reached a homogenization state suitable for rolling a predetermined thickness of copper strip foil based on the comparison result of the quantization parameter set and a preset judgment condition.

[0014] In another aspect, this application also provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the online monitoring method for melting furnace current as described above.

[0015] In another aspect, this application provides a computer-readable storage medium having computer program instructions stored thereon, which can be executed by a processor to implement the online monitoring method for melting furnace current as described above.

[0016] Another aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the online monitoring method for melting furnace current as described above.

[0017] This application accurately determines the complete liquid phase holding stage by continuously acquiring the current signal of the melting furnace, extracts the low-frequency current fluctuation characteristics reflecting the melt state, and quantifies its decay process. Finally, based on the quantified parameters, it accurately determines the melt homogenization state. This method requires no additional detection equipment and can be directly deployed on existing production lines. It effectively solves the problem in existing technologies that rely on experience or fixed parameters to fail to identify implicit melt inhomogeneities, accurately distinguishing between the differences between "apparent melting" and "internal homogenization." This method significantly reduces the quality risks of subsequent rolling thickness fluctuations and edge cracks caused by insufficient melt homogenization, providing reliable melt quality assurance for the manufacture of predetermined thicknesses, especially ultra-thin copper strips and foils. It also reduces ineffective holding time, improves production efficiency, and has good engineering applicability and industrial promotion value. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 A schematic diagram of an online current monitoring method for a melting furnace provided in this application embodiment; Figure 2 A schematic diagram illustrating the process for determining the fully liquid-phase insulation stage provided in this application embodiment; Figure 3 This is a schematic diagram of the process for extracting low-frequency current fluctuation features provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an online current monitoring system for a melting furnace provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of a device provided in an embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] It should be noted that all user information (including but not limited to user device information, user personal information, object information corresponding to device usage data, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, device usage data, etc.) involved in all embodiments of this application are information and data authorized by the user or fully authorized by all parties.

[0022] The online current monitoring method for melting furnaces disclosed in this application is applicable to production scenarios where copper is smelted using an industrial frequency cored melting furnace, and the melt is used in a semi-continuous casting-rolling-annealing process. It is particularly suitable for manufacturing flexible copper strips and foils for new energy vehicles of predetermined thickness. The implementation of this method typically relies on a melting process monitoring system deployed on the production site. This system integrates at least a current acquisition unit, a data processing unit, and a status determination unit. These units can be integrated into the same industrial control system or achieve data interaction and command transmission through industrial communication protocols. No modifications to the structure or power supply of the existing melting furnace are required, and it can be directly deployed and implemented on existing production lines.

[0023] The implementation process of the online current monitoring method for melting furnaces described in this application will be explained in detail below with reference to specific embodiments. It should be noted that this embodiment is only used to explain this application and is not intended to limit the scope of protection of this application. Conventional adjustments or substitutions to each step made by those skilled in the art without departing from the concept of this application should be included in the scope of protection of this application.

[0024] like Figure 1 As shown in the figure, this application discloses a schematic diagram of an online current monitoring method for a melting furnace, including the following method steps: S1: Continuously collect the current signal of the power supply circuit during the operation of the melting furnace to form a current time series; S2: Determine the complete liquid phase heat preservation stage based on the variation characteristics of the current time series; S3: Extract low-frequency current fluctuation characteristics reflecting the homogenization state inside the melt from the current time series corresponding to the complete liquid phase heat preservation stage. The low-frequency current fluctuation characteristics include at least: a feature sequence of energy change over time within a preset low-frequency range obtained from the spectrum analysis of the current signal. S4: Based on the low-frequency current fluctuation characteristics, obtain a set of quantitative parameters reflecting the attenuation state of the low-frequency current fluctuation; S5: Based on the comparison results between the set of quantitative parameters and the preset judgment conditions, determine whether the melt has reached a homogenization state suitable for rolling a copper strip foil of a predetermined thickness.

[0025] In some embodiments, for step S1, the continuous acquisition of the current signal specifically refers to the current acquisition unit in the melting process monitoring system continuously and uninterruptedly acquiring the current signal in the power supply circuit of the melting furnace according to a preset acquisition frequency after the melting furnace is powered on and the melting operation is started. The current acquisition unit can be implemented using a current sensor, which is connected in series with the power supply circuit of the melting furnace or coupled in a non-contact manner to ensure that it can sense the current changes in the power supply circuit in real time and convert the physical current signal into a transmittable and processable electrical signal or digital signal.

[0026] The acquired current signals can be stored according to the correspondence between "timestamp and current value" to form a continuous current time series. The timestamp records the acquisition time of each current value, and its accuracy can be matched to the current acquisition frequency. For example, when the acquisition frequency is 1kHz, the timestamp accuracy can be configured to 1 millisecond to ensure accurate reflection of the dynamic change trajectory of the current signal over time. The current value is stored in digital form, and its quantization accuracy is set according to actual monitoring needs. 16-bit or 32-bit analog-to-digital conversion accuracy can be optionally used to ensure the accuracy of current value measurement.

[0027] During current signal acquisition, the data processing unit receives the current time-series data transmitted by the current acquisition unit in real time and stores it according to the preset storage path and data format. Simultaneously, it monitors the data transmission process in real time. If data loss, transmission interruption, or other abnormalities occur, an alarm mechanism is triggered promptly and the abnormal information is recorded to facilitate troubleshooting by operators. The data processing unit updates the current data record for the current furnace batch in real time, providing complete and continuous input data for determining the complete liquid phase insulation stage in subsequent step S2.

[0028] In some embodiments, the determination of the complete liquid phase holding stage in step S2 addresses the technical problem that the prior art cannot dynamically divide the smelting process stages based on the melt state, leading to the inability to accurately pinpoint the key stage for melt homogenization determination, thus affecting the accuracy of homogenization determination. The principle is that the state change of the copper raw material during smelting, from solid to liquid and then to complete liquid phase holding, causes a regular change in the load characteristics within the melting furnace. This change in load characteristics is directly reflected in the current signal of the power supply circuit. Based on this regularity, the complete liquid phase holding stage can be accurately identified through current signal characteristics, laying the foundation for subsequent current signal analysis focusing on this stage and ensuring the targeted and accurate homogenization determination.

[0029] Please see Figure 2 , Figure 2This is a schematic diagram illustrating the process for determining the complete liquid phase insulation stage provided in this application embodiment. Specifically, it includes the following steps: In S201, preprocessing of the current time series. The current time series acquired in step S1 is preprocessed to remove abnormal data and perform smoothing, eliminating irrelevant interference from affecting the determination of subsequent stages.

[0030] Specifically, the first step is to remove outlier data from the current time series. These outliers are primarily abnormal spikes, typically caused by accidental factors such as power supply fluctuations or transient sensor interference. They are characterized by values ​​far exceeding the normal current fluctuation range and extremely short durations. Removal methods can be based on statistical thresholds, such as calculating the mean of the current time series. and standard deviation It will exceed Current values ​​within a certain range are identified as abnormal spikes and removed. For data gaps after removing abnormal spikes, linear interpolation is used to fill the gaps, ensuring the continuity of the current time series.

[0031] Secondly, the current time series after removing outlier data is smoothed, optionally using a moving average or exponential smoothing method. For example, a moving average window of size N (where N is a positive integer) is used to calculate the smoothed current time series. The purpose of smoothing is to filter out high-frequency noise in the current signal, retaining the trend characteristics and main fluctuation characteristics that reflect the changes in the smelting stage, and avoiding misjudgments in stage determination caused by high-frequency noise.

[0032] In S202, the stage determination index is calculated. Based on the preprocessed current time series, the current change rate, current fluctuation amplitude, and current fluctuation coefficient within a continuous time window are calculated. These three indexes together serve as the core parameters for determining the smelting stage.

[0033] Specifically, a continuous calculation time window is first set. The size of the time window can be reasonably set according to the actual duration of the smelting process, for example, it can be configured to 10 seconds to ensure that a complete fluctuation cycle is covered and to avoid inaccurate index calculations due to an excessively small window. The preprocessed current time series is then iterated through using this time window. For the current data within each sliding time window, three judgment indicators are calculated: the rate of change of current k: used to characterize the degree of change of the current value per unit time, its calculation formula is: in, Let t be the current value at time t within the current sliding time window. Within the current sliding time window The current value at time [time]. To calculate the time interval, The value of can be matched with the current acquisition frequency to ensure accurate reflection of the instantaneous rate of change of the current. In actual calculations, the difference between the maximum and minimum current values ​​within the current window can be divided by the window duration to obtain a representative value of the rate of change of the current within that window.

[0034] Current fluctuation amplitude A: This characterizes the fluctuation range of the current value within the current time window, and its calculation formula is as follows: in, This represents the maximum preprocessed current value within that time window. This is the minimum current value after preprocessing within this time window.

[0035] Current fluctuation coefficient This is used to characterize the relative degree of current fluctuation, eliminating the influence of the absolute value of the current on the fluctuation determination. Its calculation formula is as follows: in, This represents the standard deviation of the preprocessed current values ​​within that time window. This represents the average value of the preprocessed current within this time window.

[0036] For each sliding time window, the corresponding current change rate k, current fluctuation amplitude A, and current fluctuation coefficient are calculated according to the above formula. This generates time series data for three indicators, providing a quantitative basis for subsequent stage judgments.

[0037] In S203, the determination of the complete liquid phase insulation stage is based on the calculated current change rate k, current fluctuation amplitude A, and current fluctuation coefficient. The results are compared with the preset melting stage judgment threshold to determine the complete liquid phase heat preservation stage.

[0038] The preset threshold for determining the smelting stage is obtained through statistical analysis of current data from historically stable production heats. Specifically, current time-series data from multiple historically stable production heats are collected, and these heats must meet the conditions of good melt homogenization and qualified quality of subsequent rolled products. After performing the same preprocessing and index calculations on the current data of each historical heat, the current change rate k, current fluctuation amplitude A, and current fluctuation coefficient for each stage are statistically analyzed according to the known smelting stages (determined through on-site observation, temperature monitoring, and other auxiliary means). The distribution range is determined to determine the judgment threshold for each stage.

[0039] For example, the threshold ranges for the three stages are obtained through statistical analysis: the rate of change of current during the solid-state melting stage. Current fluctuation amplitude Current fluctuation coefficient The rate of change of current k during the liquid phase expansion stage satisfies The current fluctuation amplitude A satisfies Current fluctuation coefficient satisfy The rate of change of current during the fully liquid-phase heat preservation stage Current fluctuation amplitude Current fluctuation coefficient .in, The threshold value for the rate of change of current. This is the threshold value for the current fluctuation amplitude. The threshold value for the current fluctuation coefficient can be adjusted according to the specific model of the melting furnace, its rated power, the characteristics of the smelting raw materials, and other actual conditions to ensure the accuracy of the determination.

[0040] For example, when performing stage determination, the following process is followed: Set the sliding step size of the sliding time window. The sliding step size can be the same as or smaller than the time window size. For example, when the time window is 10 seconds, the sliding step size can be configured to be 5 seconds to improve the sensitivity of stage determination; for each sliding time window, the calculated... The current melting stage is initially determined based on the comparison results with the preset threshold values ​​for each stage. To ensure the stability of the stage determination and avoid misjudgment due to instantaneous fluctuations, a stage confirmation condition is set: the current melting stage is officially confirmed to have switched to the complete liquid phase heat preservation stage only when M consecutive sliding time windows (M is a positive integer) are all determined to be in the complete liquid phase heat preservation stage, and all current data within these M windows are back-marked. If the determination result of a single window is the complete liquid phase heat preservation stage, but the confirmation condition of M consecutive windows is not met, the previous stage marking is maintained until the stage confirmation condition is met.

[0041] During the smelting process, the above-mentioned sliding traversal, index calculation, threshold comparison and stage determination steps are repeated until the smelting process ends, so as to accurately identify and mark the complete liquid phase heat preservation stage, and define a clear data range for the extraction of low frequency current fluctuation characteristics in the subsequent step S3.

[0042] In some embodiments, step S3, extracting low-frequency current fluctuation characteristics, addresses the technical problem of the lack of quantitative characteristic parameters in the prior art that can directly reflect the homogenization state inside the melt, thus preventing online determination of the melt homogenization state. The principle is that the compositional and temperature uniformity inside the melt affects the distribution of the electromagnetic load in the melting furnace. When there is compositional stratification or temperature gradient inside the melt, the electromagnetic load is in a dynamic adjustment state. This dynamic adjustment generates low-frequency fluctuations within a specific frequency range in the current signal. As the degree of melt homogenization increases, the electromagnetic load gradually stabilizes, and the energy of these low-frequency fluctuations gradually decays and eventually stabilizes at a low level. Simultaneously, the peak-valley interval also tends to stabilize. Based on this pattern, relevant characteristic parameters can be extracted, providing a core basis for the subsequent quantitative determination of the melt homogenization state.

[0043] Please see Figure 3 , Figure 3 This is a schematic diagram of the process for extracting low-frequency current fluctuation features provided in an embodiment of this application. Specifically, it includes the following steps: In S301, frequency domain transformation of the current signal. The current time series corresponding to the fully liquid phase insulation stage determined in step S2 is subjected to frequency domain transformation to convert the time-domain current signal into a frequency-domain signal, thereby obtaining the spectrum of the current signal.

[0044] Before performing frequency domain transformation, the current time series during the fully liquid-phase heat preservation stage needs to be preprocessed. This preprocessing process is similar to that in step S201, including denoising and smoothing, to further eliminate the influence of interference signals on the frequency domain analysis and ensure the accuracy of the spectrum. Specifically, an adaptive filtering algorithm is used to denoise the current signal, constructing an adaptive filter whose input is the current signal during the fully liquid-phase heat preservation stage. The desired output is a noise-free, real current signal. The noise signal is ,satisfy The weight coefficients of the adaptive filter are adjusted in real time using the minimum mean square error criterion, so that the filter output... With expected output The mean square error between them is minimized; the denoised current signal is Gaussian smoothed to obtain a preprocessed current signal sequence, which is used for subsequent frequency domain transformation.

[0045] The frequency-domain transformation can be implemented using the Fast Fourier Transform (FFT) algorithm. Specifically, first, the length of the preprocessed current signal sequence is regularized. Since the Fast Fourier Transform algorithm has certain requirements for the length of the input signal, usually the length of the input signal needs to be an integer power of 2. Therefore, zero-padding or truncation processing needs to be performed on the preprocessed current signal sequence. For example, if the length of the preprocessed current signal sequence is L, find the smallest integer power of 2, N ( , m is a positive integer), if L < N, then zero-pad the end of the current signal sequence to a length of N; if , then truncate the current signal sequence, retaining the first N data points to obtain a length-regularized current signal sequence (n = 0, 1,..., N - 1).

[0046] At the same time, to reduce the influence of spectral leakage on the spectral decomposition result, the length-regularized current signal sequence is weighted with a window function. Optionally, a Hanning window can be used as the weighting window function. The expression of the Hanning window is: , n = 0, 1,..., N - 1. Multiply the length-regularized current signal sequence point-by-point with the Hanning window to obtain the windowed current signal sequence . Through the window function weighting process, the sidelobe amplitude of the signal spectrum can be effectively reduced, spectral leakage can be reduced, and the resolution of spectral decomposition can be improved.

[0047] Perform the Fast Fourier Transform on the windowed current signal sequence . The calculation formula of the Fast Fourier Transform is: where, where, is the complex form of the frequency-domain signal, containing amplitude and phase information, k is the frequency point index, and j is the imaginary unit. Calculate the amplitude of the frequency-domain signal to obtain the amplitude spectrum of the current signal , where the actual frequency <​​​​​​​​​​The preset low-frequency range is determined based on the characteristics of the smelting process and statistical analysis of historical data. Specifically, by analyzing the current signal spectrum of multiple historical furnace runs, it was found that when there is inhomogeneity within the melt, significant fluctuations in the current signal mainly occur in a specific low-frequency range. As the degree of melt homogenization increases, the energy of the fluctuations in this frequency range attenuates significantly. Therefore, the preset low-frequency range can be optionally set to a certain interval between 0Hz and 10Hz, such as 0.1Hz to 5Hz. This low-frequency range can be adjusted according to the specific parameters of the melting furnace, the characteristics of the smelting raw materials, and other actual conditions to ensure accurate capture of low-frequency fluctuations related to the homogenization state of the melt.

[0050] For amplitude spectrum traverse all frequency points Determine the actual frequency corresponding to each frequency point. Does it fall within the preset low-frequency range? For frequencies that fall within this range... Calculate its corresponding amplitude spectrum. The square value The square value is proportional to the energy at that frequency point. This applies to all frequency points falling within the preset low-frequency range. Summing is performed to obtain the total low-frequency energy of the current signal segment. The calculation formula is: Where K is the set of frequency point indices that meet the preset low-frequency range requirements.

[0051] To eliminate the influence of factors such as signal length and acquisition frequency on the absolute value of low-frequency energy, the total low-frequency energy E is normalized to obtain the normalized low-frequency energy. The formula for normalization is: in, The total energy over the entire frequency range. By normalizing the data, the low-frequency energy from different furnace batches and with different acquisition parameters becomes comparable, providing a unified standard for the construction of subsequent feature sequences.

[0052] In S303, the characteristic sequence of low-frequency energy changing with time is formed. A sliding window is used to traverse the current-time series during the fully liquid-phase heat preservation stage, and the normalized low-frequency energy value corresponding to each window is obtained to form a characteristic sequence of energy changing with time in the low-frequency range.

[0053] Specifically, the length of the sliding window and the sliding step size are set. The length T of the sliding window (T is the time length) needs to be reasonably set according to the periodic characteristics of the low-frequency fluctuations. For example, it can be configured to 30 seconds to ensure that each window can cover a sufficient low-frequency fluctuation period and accurately reflect the low-frequency energy characteristics within that time period; the sliding step size... ( The time step can be less than or equal to the sliding window length, for example... The number of seconds is used to ensure the continuity and timeliness of the feature sequence. The length of the sliding window corresponds to the number of current signal data points. ( (Sampling frequency).

[0054] Starting from the beginning of the current-time series during the fully liquid-phase insulation stage, a length of [length missing] is extracted. The data points are used as the input signal for the first sliding window. Following the steps S301 and S302, frequency domain transformation and low-frequency energy calculation are performed to obtain the normalized low-frequency energy value of the window. Then, move the sliding window according to the sliding step size. Slide backward to extract the next segment of length. The data points are used as the input signal for the second sliding window. The frequency domain transformation and energy calculation process described above are repeated to obtain the normalized low-frequency energy value of the second window. Repeat the above sliding, intercepting, and calculation process until the sliding window traverses the entire current-time series during the complete liquid-phase heat preservation stage, obtaining a series of normalized low-frequency energy values. (P is the number of sliding windows).

[0055] By correlating the normalized low-frequency energy value of each sliding window with its corresponding timestamp (using the start time of each sliding window as the timestamp), a characteristic sequence of energy variation over time within the low-frequency range is formed. ,in Let be the start time of each sliding window, and This characteristic sequence can fully reflect the dynamic change of low-frequency energy over time during the complete liquid phase heat preservation stage, and directly corresponds to the evolution of the homogenization state inside the melt.

[0056] In S304, the peak and valley points are detected. When it is necessary to extract the stability characteristics of the current peak-valley interval in step S3, the peak and valley points in the current time series during the complete liquid phase heat preservation stage are detected first.

[0057] Specifically, the sliding difference method is used for peak and valley detection, and the detection window length is set to W (W is a positive integer). For each data point in the current signal sequence after preprocessing during the complete liquid phase heat preservation stage... (i=W,…,L-1-W, where L is the length of the current signal sequence in this stage), calculate the average difference between it and the first W data points and the average difference between it and the last W data points: The meanings of each parameter are explained below: : No. Data points and previous The mean difference between adjacent data points; : No. Data points and after The mean difference between adjacent data points; : Index of data points in a time-domain current signal sequence; : Length of the sliding difference detection window (positive integer); : Summation index, with value ranges as follows and ; : No. Current values ​​at each data point; : No. The current value of each data point.

[0058] like and This indicates that the data points If it is greater than W data points before and after it, then determine... Record the amplitude at the peak point. and time ;like and This indicates that the data points If it is less than W data points before and after it, then determine... Record the amplitude of the valley point. and time .

[0059] Optionally, to avoid false detection, an amplitude threshold condition is set: the amplitude at the peak point must be greater than the average value of the current signal in that segment. ( (The value is a positive integer), and the amplitude at the valley point must be less than the mean of the current signal segment. ,in The mean value of the current signal sequence after pretreatment during the complete liquid phase heat preservation stage. The standard deviation is used. By applying an amplitude threshold condition, false peaks and valleys caused by minor noise are eliminated to ensure the accuracy of the detection results.

[0060] In S305, the time interval between adjacent peaks and valleys is calculated. Based on the time information of the detected peak and valley points, the time interval between adjacent peaks and valleys is calculated.

[0061] First, the detected peak and trough points are sorted in chronological order to obtain an ordered peak sequence. Valley value sequence (M and N represent the number of peak points and valley points, respectively).

[0062] Then, the time interval between adjacent peaks and troughs is calculated, including the interval from the peak to the next trough. The interval between the trough and the next peak By summing up all peak-valley interval values, a peak-valley interval sequence is obtained. This sequence reflects the temporal pattern of the peak and valley changes in the current signal, and its stability is closely related to the internal state of the melt.

[0063] In S306, the calculation of statistical characteristic parameters of peak-valley intervals and the formation of characteristic sequences are performed. The statistical characteristic parameters of the peak-valley interval sequence are calculated, including the coefficient of variation of the peak-valley intervals, and a characteristic sequence of how this coefficient of variation changes over time is formed.

[0064] Specifically, using the same sliding window and sliding step size as in step S303, the current-time series of the fully liquid-phase heat preservation stage is traversed. For each sliding window, the peak-valley interval values ​​contained within that window are extracted to form the local peak-valley interval subsequence corresponding to that window. For each local peak-valley interval subsequence Calculate its statistical characteristic parameters: Mean of peak-valley interval The formula is: The meanings of each parameter are explained below: : The mean of the local peak-valley interval subsequence; : The length of the local peak-valley interval subsequence (positive integer); : The index of the local peak-valley interval subsequence, with a value range of ; : The first in the local peak-valley interval subsequence Peak-valley interval values.

[0065] Peak-to-valley interval standard deviation The formula is: The meanings of each parameter are explained below: Standard deviation of local peak-valley interval subsequence; : The length of the local peak-valley interval subsequence (positive integer); : The index of the local peak-valley interval subsequence, with a value range of ; : The first in the local peak-valley interval subsequence Peak-valley interval values; : The mean of the local peak-valley interval subsequence.

[0066] Peak-to-valley interval variation coefficient Peak-to-valley interval variation coefficient It can intuitively reflect the relative fluctuation of the peak-valley interval within the window. The smaller the value, the more stable the peak-valley interval, and the more uniform the internal state of the melt. The larger the value, the greater the fluctuation in the peak-valley interval, and the more uneven the internal state of the melt.

[0067] The coefficient of variation of the peak-valley interval for each sliding window Corresponding to its timestamp (consistent with the timestamp of the low-frequency energy characteristic sequence), a characteristic sequence of the coefficient of variation over time is formed. ,in Each sequence corresponds one-to-one with the timestamp of the low-frequency energy characteristic sequence. This characteristic sequence, together with the low-frequency energy change characteristic sequence, constitutes the low-frequency current fluctuation characteristic, providing comprehensive feature support for the subsequent acquisition of the quantization parameter set.

[0068] In some embodiments, step S4, obtaining a set of quantitative parameters, addresses the technical problem that the prior art cannot quantitatively describe the melt homogenization process, leading to the inability to establish an objective and accurate homogenization judgment standard. The principle is that as the degree of melt homogenization increases, the component stratification and temperature gradient within the melt gradually disappear, the electromagnetic load in the melting furnace tends to stabilize, and the corresponding low-frequency current fluctuation energy shows a continuous decreasing trend. The peak-valley interval variation coefficient also gradually decreases and eventually stabilizes within a certain range. By quantitatively analyzing the attenuation process of these characteristics, the evolution of the melt homogenization state can be accurately reflected, providing a quantitative basis for judging the melt homogenization state.

[0069] The quantization parameter set includes at least one of the instantaneous decay rate, average decay rate, and cumulative decay calculated based on energy characteristic sequences within the low-frequency range; and / or at least one of the rate of change and average rate of change calculated based on the coefficient of variation characteristic sequences over time intervals. The specific calculation process is as follows: For the calculation of quantization parameters based on low-frequency energy feature sequences, the instantaneous decay rate is calculated as follows: based on low-frequency energy feature sequences Calculate the instantaneous decay rate of low-frequency energy between two adjacent sliding windows. The calculation formula is: in Let be the normalized low-frequency energy value of the i-th sliding window. This represents the start time of the i-th sliding window. Instantaneous decay rate. A positive value indicates that the low-frequency energy is decreasing during this period, which corresponds to an increase in the homogenization of the melt. A negative value indicates that the low-frequency energy is on the rise, which may lead to increased inhomogeneity in the melt. A value close to zero indicates that low-frequency energy is stabilizing.

[0070] The average decay rate calculation involves calculating the average decay rate over a certain time range to avoid the influence of instantaneous fluctuations on the assessment of the decay trend. The calculation window size is set to K (K is a positive integer), meaning that the average decay rate is obtained by averaging every K consecutive instantaneous decay rates. The calculation formula is: The meanings of each parameter are explained below: : No. The average attenuation rate of low-frequency energy corresponding to each sliding window; : Window size for calculating average decay rate (positive integer); : Sliding window index, with a value range of ( (Total number of sliding windows during the complete liquid phase insulation stage). : Summation index, with a range of values. ; : No. The sliding window and the first Low-frequency energy instantaneous decay rate between sliding windows.

[0071] Average decay rate It can more stably reflect the overall attenuation trend of low-frequency energy, reduce the interference of instantaneous fluctuations, and provide more reliable rate parameters for judgment.

[0072] The calculation of cumulative attenuation includes: calculating the cumulative attenuation of low-frequency energy during the complete liquid-phase insulation stage. The calculation formula is: in, This represents the normalized low-frequency energy value of the first sliding window during the fully liquid-phase insulation stage. This is the normalized low-frequency energy value for the last sliding window. Cumulative attenuation. It can reflect the overall decrease in low-frequency energy during the complete liquid-phase insulation stage. The larger the value, the more significant the improvement in melt homogenization.

[0073] For the calculation of quantization parameters based on the peak-valley interval coefficient of variation characteristic sequence, the rate of change calculation is as follows: based on the peak-valley interval coefficient of variation characteristic sequence Calculate the rate of change of the coefficient of variation between two adjacent sliding windows. The calculation formula is: in, Let be the coefficient of variation of the peak-valley interval for the i-th sliding window. This represents the starting time of the i-th sliding window. Rate of change. A positive value indicates that the coefficient of variation of the peak-valley interval is decreasing during this period, the stability of the peak-valley interval is improving, and the corresponding degree of melt homogenization is improving. A negative value indicates that the coefficient of variation of the peak-valley interval is increasing, the stability of the peak-valley interval is decreasing, and the corresponding melt inhomogeneity may be aggravated. A value close to zero indicates that the coefficient of variation of the peak-valley interval tends to stabilize, and the melt state is stable.

[0074] The calculation of the average rate of change involves setting the window size for calculating the average rate of change to K (the same as the window size for calculating the average decay rate), and averaging every K consecutive rates of change to obtain the average rate of change. The calculation formula is: The meanings of each parameter are explained below: : No. The average rate of change of the coefficient of variation of the peak-valley interval corresponding to each sliding window; : Window size for calculating the average rate of change (positive integer); : Sliding window index, with a value range of ( (Total number of sliding windows during the complete liquid phase insulation stage). : Summation index, with a range of values. ; : No. The sliding window and the first Instantaneous rate of change of the coefficient of variation of the peak-valley interval between sliding windows.

[0075] Average rate of change It can more stably reflect the overall trend of the peak-valley interval variation coefficient, reduce the interference of instantaneous fluctuations, and together with the average decay rate, constitute the core rate parameter reflecting the homogenization state of the melt.

[0076] The parameters obtained from the above calculations, such as instantaneous decay rate, average decay rate, cumulative decay, coefficient of variation change rate, and average coefficient of variation change rate, are summarized to form a set of quantitative parameters reflecting the decay state of low-frequency current fluctuations, providing a comprehensive and quantitative basis for the homogenization state determination in the subsequent step S5.

[0077] In some embodiments, the technical problem addressed by step S5, the determination of the melt homogenization state, is the lack of objective and quantitative homogenization judgment criteria based on the internal state of the melt in the prior art. This leads to the inability to accurately determine whether the melt meets the manufacturing requirements of ultra-thin copper strip foil. The principle is that when the melt reaches a homogenized state, the energy of low-frequency current fluctuations will decay to a stable range and remain stable, and the peak-valley interval will also tend to stabilize. The corresponding quantitative parameters will meet the preset judgment conditions. Based on the comparison between the quantitative parameters and the preset judgment conditions, the melt state can be accurately determined, achieving online and precise determination of the melt homogenization state, providing a reliable basis for subsequent process connections.

[0078] In this step, the preset judgment conditions include Condition 1, Condition 2, and Condition 3. When the set of quantified parameters simultaneously meets these three conditions, the melt is determined to have reached a homogenized state. The specific process is as follows: The thresholds in the preset judgment conditions are obtained through data analysis of a large number of historical stable production furnaces to ensure that the thresholds can accurately distinguish whether the melt homogenization state meets the standard. The specific steps are as follows: Data collection: Collect complete data from M historical stable production furnaces (M is a positive integer), including the low-frequency current fluctuation characteristic sequence of each furnace, the set of quantified parameters, and the quality inspection results of subsequent rolled products, such as thickness fluctuation range and edge cracking rate; Screening of qualified furnaces: Select furnaces with qualified subsequent rolled product quality inspection results as qualified furnaces, requiring that the product quality of qualified furnaces meet the usage requirements of copper strip foil of predetermined thickness; Threshold statistics: Perform statistical analysis on the quantified parameters of all qualified furnaces to determine the preset thresholds. For example, for the stable energy range Calculate the average low-frequency energy value of the melt after stabilization in the furnace batch that meets the standard. and standard deviation The stable energy range is set as ( (where the integer is positive), ensuring that the low-frequency energy values ​​under most steady-state conditions fall within this range; for the steady-state decay rate threshold The 95th percentile of the absolute value of the average decay rate under stable conditions for all qualified furnace cycles was taken as... For stability threshold The 95th percentile of the coefficient of variation of the peak-valley interval under stable conditions for all compliant furnace runs was taken as... For a continuously preset number of sliding windows Q, the number of windows maintaining a stable state in the qualified furnace batches is statistically determined to ensure the stability of the judgment results. Threshold verification and adjustment: The determined initial threshold is applied to some historical furnace batches that were not included in the threshold statistics to verify the judgment accuracy of the threshold, i.e., the consistency between the judgment result and the product quality inspection result. If the judgment accuracy meets the preset requirements, the threshold is determined as the final preset threshold; if the judgment accuracy does not meet the requirements, the threshold parameters are adjusted and re-verified until the judgment accuracy reaches the preset standard.

[0079] The final preset judgment conditions are as follows: Condition 1: The energy values ​​within the low-frequency range corresponding to a consecutive preset number of sliding windows all fall within the preset stable energy range; Condition 2: The absolute value of the low-frequency energy decay rate corresponding to the above-mentioned consecutive preset number of sliding windows is less than the preset stable decay rate threshold. Condition 3: The coefficient of variation of the time interval corresponding to the above-mentioned consecutive preset number of sliding windows is less than the preset stability threshold.

[0080] Based on the quantization parameter set obtained in step S4, the three conditions are verified respectively: Among them, the verification of condition one: extract the normalized low-frequency energy value of each sliding window in the quantization parameter set, and check whether there are Q consecutive sliding windows with normalized low-frequency energy values ​​(Q is a preset number of consecutive windows). All fall within the stable energy range Internally, traverse the low-frequency energy feature sequence and record continuously satisfied... The number of windows is determined, and when this number reaches Q, condition one is satisfied; where, the verification of condition two is as follows: extract the average decay rate corresponding to the above Q consecutive sliding windows. Check whether the absolute value of the average decay rate for each window is less than the stable decay rate threshold. ,Right now (i is the index of the continuous window). If all windows meet this requirement, then condition two is satisfied; where, the verification of condition three is: extract the peak-valley interval variation coefficient corresponding to the above Q consecutive sliding windows. Check whether the coefficient of variation of each window is less than the stability threshold. ,Right now (i is the index of the consecutive window). If all windows meet this requirement, then condition three is satisfied.

[0081] When the set of quantification parameters simultaneously satisfies conditions one, two, and three, the melt is ultimately determined to have reached a homogenized state suitable for rolling copper strip foil of a predetermined thickness; if any one of the three conditions is not met, the melt is determined not to have reached a homogenized state.

[0082] During the judgment process, if abnormal situations such as a continuous upward trend in low-frequency energy or a continuous increase in the peak-valley interval variation coefficient occur, it indicates that the homogenization state of the melt has deteriorated. At this time, regardless of whether the above judgment conditions are met, it is determined that the homogenization state has not been reached, and an alarm mechanism is triggered to prompt the operator to check whether the melting process parameters are abnormal.

[0083] In some embodiments, for step S6, specifically, when it is determined that the melt has reached a homogenized state, the status determination unit of the smelting process monitoring system outputs an instruction allowing the melt to enter the next process. This instruction is transmitted to the smelting or casting process control module via industrial communication protocols such as Modbus and Profinet. After receiving the instruction, the process control module sends an opening instruction to the discharge valve control unit of the melting furnace, allowing the melt to enter the next casting or transfer process. At the same time, the status determination unit records the determination result, quantitative parameters, and related process parameters such as holding time and holding temperature for the current furnace batch, and stores them in the production database for subsequent process optimization and quality traceability.

[0084] When the melt is determined not to have reached a homogenized state, the process control module does not send a discharge command, maintains the furnace in a completely liquid-phase holding state, and continues to execute steps S3 to S5, namely, re-extracting low-frequency current fluctuation characteristics, obtaining a set of quantitative parameters, and judging the homogenization state. This cycle continues until the melt reaches a homogenized state or the preset maximum holding time limit is reached. If the melt still has not reached a homogenized state after the maximum holding time limit is reached, the process control module triggers an emergency alarm, prompting the operator to stop the machine for inspection and to investigate factors such as raw material quality and melting process parameters that may lead to melt non-uniformity, avoiding energy waste and production delays caused by ineffective holding.

[0085] This application presents an online current monitoring method for melting furnaces. By continuously acquiring melting furnace current signals, it accurately determines the complete liquid phase holding stage, extracts low-frequency current fluctuation characteristics reflecting the melt state, and quantifies its decay process. Finally, based on the quantified parameters, it accurately determines the melt homogenization state. This method requires no additional detection equipment and can be directly deployed on existing production lines. It effectively solves the problem in existing technologies that rely on experience or fixed parameters to fail to identify latent non-uniformity in the melt, accurately distinguishing between the differences between "apparent melting" and "internal homogenization." This method significantly reduces quality risks such as thickness fluctuations and edge cracks in subsequent rolling caused by insufficient melt homogenization, providing reliable melt quality assurance for the manufacture of predetermined thicknesses, especially ultra-thin copper strips and foils. It also reduces ineffective holding time, improves production efficiency, and has good engineering applicability and industrial promotion value.

[0086] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of an online current monitoring system for a melting furnace provided in an embodiment of this application. Figure 4 As shown, the system 400 includes: a data acquisition module 401, used to continuously acquire the current signal of the power supply circuit during the operation of the melting furnace to form a current time series; a complete liquid phase heat preservation stage determination module 402, used to determine the complete liquid phase heat preservation stage based on the change characteristics of the current time series; a low-frequency current fluctuation feature extraction module 403, used to extract low-frequency current fluctuation features reflecting the homogenization state inside the melt from the current time series corresponding to the complete liquid phase heat preservation stage, wherein the low-frequency current fluctuation features include at least: a feature sequence of energy changes over time within a preset low-frequency range obtained from the spectral analysis of the current signal; a quantization parameter set acquisition module 404, used to acquire a quantization parameter set reflecting the attenuation state of the low-frequency current fluctuation based on the low-frequency current fluctuation features; and a judgment module 405, used to determine whether the melt has reached a homogenization state suitable for rolling a predetermined thickness of copper strip foil based on the comparison result of the quantization parameter set and preset judgment conditions.

[0087] Those skilled in the art will clearly understand that the technical solutions of the embodiments of this application can be implemented by means of software and / or hardware. In this specification, "unit" and "module" refer to software and / or hardware that can independently complete or cooperate with other components to complete a specific function, wherein the hardware may be, for example, a field-programmable gate array (FPGA), an integrated circuit (IC), etc.

[0088] Each processing unit and / or module in the embodiments of this application can be implemented by an analog circuit that implements the functions described in the embodiments of this application, or by software that executes the functions described in the embodiments of this application.

[0089] Please see Figure 5 It shows a schematic diagram of the structure of an electronic device according to an embodiment of this application, which can be used to implement... Figure 1 The method in the illustrated embodiment. (As shown) Figure 5 As shown, the electronic device 500 may include: at least one processor 501, at least one network interface 504, a user interface 503, a memory 505, and at least one communication bus 502. The communication bus 502 is used to enable connection and communication between the components. The user interface 503 may include buttons, and optionally include a standard wired or wireless interface. The network interface 504 may include, but is not limited to, a Bluetooth module, an NFC module, a Wi-Fi module, etc.

[0090] The processor 501 may include one or more processing cores and connect to various parts within the device 500 via various interfaces and lines. It implements the various functions and data processing of the device 500 by running or executing instructions, programs, code sets, or instruction sets stored in the memory 505, and by accessing data in the memory 505. Optionally, the processor 501 may be implemented using at least one hardware form of DSP, FPGA, or PLA. The processor 501 may also integrate one or more combinations of CPU, GPU, and modem. The CPU is mainly used to handle the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display on the screen; and the modem is used for wireless communication. It is understood that the modem may not be integrated into the processor 501, but may be implemented through a separate chip.

[0091] Memory 505 may include random access memory (RAM) or read-only memory (ROM). Optionally, memory 505 includes a non-transitory computer-readable medium for storing instructions, programs, code, code sets, or instruction sets. Memory 505 may be divided into a program storage area and a data storage area, wherein the program storage area may be used to store instructions for implementing an operating system, instructions for implementing at least one function (such as touch functionality, audio playback functionality, image playback functionality, etc.), and instructions for implementing the aforementioned method embodiments; the data storage area may be used to store data involved in the relevant method embodiments. Memory 505 may also be at least one storage device located remotely from processor 501. Figure 5 As shown, the memory 505, which serves as a computer storage medium, may contain an operating system, a network communication module, a user interface module, and program instructions.

[0092] In particular, the methods and / or embodiments in this application can be implemented as computer software programs. For example, the embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. When the computer program is executed by processor 501, it performs the functions defined in the methods of this application.

[0093] Another embodiment of this application provides a computer-readable storage medium having computer program instructions stored thereon, which can be executed by a processor to implement the methods and / or technical solutions of any one or more embodiments of this application described above.

[0094] The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0095] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

Claims

1. A method for online monitoring of current in a melting furnace, characterized in that, include: S1: Continuously collect the current signal of the power supply circuit during the operation of the melting furnace to form a current time series; S2: Determine the complete liquid phase heat preservation stage based on the variation characteristics of the current time series; S3: Extract low-frequency current fluctuation characteristics reflecting the homogenization state inside the melt from the current time series corresponding to the complete liquid phase heat preservation stage. The low-frequency current fluctuation characteristics include at least: a feature sequence of energy change over time within a preset low-frequency range obtained from the spectrum analysis of the current signal. S4: Based on the low-frequency current fluctuation characteristics, obtain a set of quantitative parameters reflecting the attenuation state of the low-frequency current fluctuation; S5: Based on the comparison results between the set of quantitative parameters and the preset judgment conditions, determine whether the melt has reached a homogenization state suitable for rolling a copper strip foil of a predetermined thickness.

2. The method for online monitoring of melting furnace current according to claim 1, characterized in that, The step of determining the complete liquid phase heat preservation stage based on the variation characteristics of the current time series includes: preprocessing the current time series to remove abnormal data and perform smoothing; calculating the current change rate, current fluctuation amplitude, and current fluctuation coefficient within a continuous time window based on the preprocessed current time series; and comparing the calculated current change rate, current fluctuation amplitude, and current fluctuation coefficient with a preset melting stage determination threshold to determine the complete liquid phase heat preservation stage.

3. The method for online monitoring of melting furnace current according to claim 1, characterized in that, Extracting low-frequency current fluctuation features that reflect the homogenization state inside the melt includes: performing frequency domain transformation on the current time series corresponding to the complete liquid phase heat preservation stage to obtain the spectrum of the current signal; calculating the energy value in the spectrum within a preset low-frequency range; and using a sliding window to traverse the current time series of the complete liquid phase heat preservation stage to obtain the energy value corresponding to each window, forming a feature sequence of energy changing with time within the low-frequency range.

4. The method for online monitoring of melting furnace current according to claim 3, characterized in that, Extracting low-frequency current fluctuation characteristics that reflect the homogenization state inside the melt also includes: detecting peak points and valley points in the current time series during the fully liquid phase heat preservation stage; calculating the time interval between adjacent peaks and valleys based on the time information of the detected peak points and valley points; calculating statistical characteristic parameters of the time interval, including the coefficient of variation of the time interval, and forming a characteristic sequence of the coefficient of variation changing over time.

5. The method for online monitoring of melting furnace current according to claim 4, characterized in that, The set of quantization parameters includes at least one of the instantaneous decay rate, average decay rate, and cumulative decay amount calculated based on the energy characteristic sequence within the low frequency range; and / or at least one of the rate of change and average rate of change calculated based on the coefficient of variation characteristic sequence of the time interval.

6. The method for online monitoring of melting furnace current according to claim 1, characterized in that, The preset determination conditions include: Condition 1: the energy values ​​in the low-frequency range corresponding to a preset number of consecutive sliding windows all fall into a preset stable energy range; Condition 2: the absolute value of the low-frequency energy decay rate corresponding to the preset number of consecutive sliding windows is less than a preset stable decay rate threshold; Condition 3: the coefficient of variation of the time interval corresponding to the preset number of consecutive sliding windows is less than a preset stability threshold; when the set of quantization parameters simultaneously satisfies Condition 1, Condition 2 and Condition 3, it is determined that the melt has reached the homogenization state.

7. The method according to claim 1, characterized in that, After step S5, Including: S6: When it is determined that the melt has reached the homogenization state, output an instruction to allow the melt to enter the next process; when it is determined that the melt has not reached the homogenization state, continue to execute steps S3 to S5 until the homogenization state is reached or the preset maximum heat preservation time is reached.

8. A melting furnace current online monitoring system, characterized in that, include: The acquisition module is used to continuously acquire the current signal of the power supply circuit during the operation of the melting furnace and form a current time series; the complete liquid phase heat preservation stage determination module is used to determine the complete liquid phase heat preservation stage based on the changing characteristics of the current time series. A low-frequency current fluctuation feature extraction module is used to extract low-frequency current fluctuation features reflecting the homogenization state inside the melt from the current time series corresponding to the complete liquid phase heat preservation stage. The low-frequency current fluctuation features include at least: a feature sequence of energy changes over time within a preset low-frequency range obtained from the spectral analysis of the current signal; a quantization parameter set acquisition module is used to acquire a quantization parameter set reflecting the attenuation state of the low-frequency current fluctuation based on the low-frequency current fluctuation features; and a judgment module is used to determine whether the melt has reached a homogenization state suitable for rolling a predetermined thickness of copper strip foil based on the comparison result between the quantization parameter set and a preset judgment condition.

9. An electronic device, characterized in that, include: At least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, The computer program instructions can be executed by a processor to implement the method as described in any one of claims 1-7.

Images