Quality detection method and device for copper strip for cable

Abstract

The invention provides a quality detection method and device for a copper strip for a cable, and relates to the field of coaxial cable detection. The method comprises the following steps: obtaining thickness data of a target copper coil strip through a thickness gauge in response to a quality detection operation for the target copper coil strip; according to the thickness data, constructing a frequency spectrum law diagram corresponding to the target copper coil strip; obtaining a quality detection characteristic corresponding to the target copper coil strip through the frequency spectrum law diagram; and outputting a quality detection result corresponding to the target copper coil strip according to the quality detection characteristics through a copper coil strip peak value statistical model. The problem that whether the production quality of the copper strip is qualified or not cannot be accurately judged is solved.

Description

Technical Field

[0001] The present application relates to the field of coaxial cable detection, and in particular to a method and device for detecting the quality of copper strips used in cables. Background Art

[0002] Coaxial cable is sometimes called radio frequency cable. It is the most commonly used structure. Because its inner and outer conductors are in concentric positions, the electromagnetic energy is confined to the medium between the inner and outer conductors. Therefore, it has significant advantages such as low attenuation, high shielding performance, wide frequency band and stable performance.

[0003] The copper strip used to manufacture cables has its own variable parameters such as chemical composition, hardness, external dimensions and grain size, which are all controllable and detectable. At present, the production quality of copper strips is also tested by these indicators. However, in the production of coaxial cables, due to various factors, periodic impedance unevenness will always occur on the cables. When the signal is transmitted in the cable, the internal reflection will produce in-phase superposition, which will lead to a large reflection coefficient peak. Especially when producing small-sized cables, the above-mentioned influence will be more obvious. At this time, it is impossible to accurately judge whether the production quality of copper strips is qualified by only testing the above-mentioned quality indicators.

[0004] Therefore, there is an urgent need for a quality detection method and device for copper strips for cables. Summary of the invention

[0005] The present application provides a method and device for detecting the quality of copper strips for cables, which solves the problem of being unable to accurately determine whether the production quality of the copper strips is qualified.

[0006] In a first aspect of the present application, a quality inspection method for copper tape for cables is provided, the method comprising: in response to a quality inspection operation on a target copper tape, obtaining thickness data of the target copper tape through a thickness gauge; constructing a frequency spectrum regularity diagram corresponding to the target copper tape based on the thickness data; obtaining quality inspection features corresponding to the target copper tape through the frequency spectrum regularity diagram, the quality inspection features including frequency spectrum main frequency features, frequency spectrum harmonic component features, frequency spectrum bandwidth features and peak value time domain position distribution features; outputting quality inspection results corresponding to the target copper tape based on the quality inspection features through a copper tape peak statistical model.

[0007] Optionally, the thickness data of the target copper coil is obtained by a thickness gauge, specifically including: within a preset time period, obtaining a thickness fluctuation signal corresponding to the target copper coil by a high-precision thickness gauge; converting the thickness fluctuation signal into a digital information number by digital-to-analog conversion, and filtering the digital information number; and using the filtered digital information number as the thickness data of the target copper coil.

[0008] Optionally, based on the thickness data, a frequency spectrum regularity diagram corresponding to the target copper coil is constructed, specifically including: performing a fast Fourier transform on the thickness data to convert the thickness data from time domain data to frequency domain data; based on the transformed thickness data, a preset resolution is used to construct a frequency spectrum regularity diagram corresponding to the target copper coil.

[0009] Optionally, the thickness data is subjected to a fast Fourier transform to convert the thickness data from time domain data to frequency domain data, specifically comprising: performing a fast Fourier transform according to the following formula:

[0010]

[0011] Where X(k) is the kth frequency component in the frequency domain data, x(n) is the nth sampling point value in the time domain data, N is the total number of sampling points, k is the frequency index, and k = 0, 1, 2, 3, ..., N-1, j is the imaginary unit, and j satisfies 2 =1.

[0012] Optionally, the quality inspection result corresponding to the target copper tape is output according to the quality inspection characteristics through the copper tape peak statistical model, specifically including: calculating the reflection coefficient peak value corresponding to the target copper tape through the copper tape peak statistical model according to the quality inspection characteristics; outputting the quality inspection result corresponding to the target copper tape according to the reflection coefficient peak value.

[0013] Optionally, according to the quality detection characteristics, the reflection coefficient peak value corresponding to the target copper tape is calculated by using the copper tape peak statistical model, specifically including: calculating the reflection coefficient peak value according to the following formula:

[0014]

[0015] Among them, R max is the peak value of the reflection coefficient, N is the total number of quality detection features, f i is the i-th quality detection feature, g i (f i ) is the nonlinear weighting function corresponding to the i-th quality detection feature, α i is the weight factor corresponding to the i-th quality detection feature, f p and f q are any two different types of features in the quality detection features, h j (f p ,f q ) represents the jth interaction function, which is used to represent f p and f q The interaction relationship between j is the weight factor corresponding to the jth interaction function, γ is the global adjustment coefficient of the nonlinear combination, ψ k (fk ) is the nonlinear transformation function corresponding to a single quality detection feature, and K is the total number of quality detection features involved in the nonlinear transformation calculation.

[0016] Optionally, the quality inspection result corresponding to the target copper coil is output, specifically including: judging whether the reflection coefficient peak value is greater than or equal to a first preset threshold value; if the reflection coefficient peak value is greater than or equal to the first preset threshold value, confirming that the quality inspection result is qualified; judging whether the reflection coefficient peak value is greater than or equal to a second preset threshold value and less than the first preset threshold value; if the reflection coefficient peak value is greater than or equal to the second preset threshold value and less than the first preset threshold value, confirming that the quality inspection result is quality to be optimized; judging that the reflection coefficient peak value is less than the second preset threshold value; if the reflection coefficient peak value is less than the second preset threshold value, confirming that the quality inspection result is unqualified.

[0017] In a second aspect of the present application, a quality detection device for copper tape for cables is provided, the device comprising an acquisition module and a processing module, wherein:

[0018] The acquisition module is used to respond to the quality inspection operation on the target copper coil and obtain the thickness data of the target copper coil through the thickness gauge; through the spectrum regularity diagram, the quality inspection characteristics corresponding to the target copper coil are obtained, and the quality inspection characteristics include the main frequency characteristics of the spectrum, the harmonic component characteristics of the spectrum, the bandwidth characteristics of the spectrum and the time domain position distribution characteristics of the peak.

[0019] The processing module is used to construct a frequency spectrum diagram corresponding to the target copper coil according to the thickness data; through the copper coil peak statistical model, according to the quality detection characteristics, the quality detection result corresponding to the target copper coil is output.

[0020] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods described above.

[0021] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to perform any of the above methods.

[0022] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0023] 1. When performing quality inspection on the target copper coil, the thickness data of the target copper coil is obtained through the thickness gauge, and the frequency spectrum diagram corresponding to the target copper coil is constructed according to the thickness data, so as to obtain the quality inspection features corresponding to the target copper coil through the frequency spectrum diagram, and output the quality inspection results corresponding to the target copper coil according to the quality inspection features through the peak statistical model of the copper coil, and then accurately obtain the thickness data of the target copper coil through the thickness gauge, and construct the frequency spectrum diagram based on the thickness data, extract the quality inspection features, and use the peak statistical model to analyze the feature data, so as to realize the accurate judgment of the overall quality trend of the copper coil, and greatly improve the accuracy of the production quality judgment of the copper coil.

[0024] 2. Obtain the thickness fluctuation signal corresponding to the target copper coil through a high-precision thickness gauge, convert the thickness fluctuation signal into a digital information number through digital-to-analog conversion, and filter the digital information number, so that the filtered digital information number is used as the thickness data of the target copper coil, thereby reducing the noise and random errors in the thickness data, providing high-precision data support and a reliable analysis basis for subsequent quality inspection and production process optimization.

[0025] 3. Based on multiple different types of quality detection features, the reflection coefficient peak is calculated through the comprehensive influence of basic feature items, interactive feature items and nonlinear combination items, and the quality detection result corresponding to the target copper coil is output based on the reflection coefficient peak. On the one hand, the calculation accuracy of the reflection coefficient peak is greatly improved, and on the other hand, the real-time efficiency of quality detection is also improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a quality detection method for a copper strip for a cable provided in an embodiment of the present application;

[0027] Figure 2 It is a module schematic diagram of a quality detection device for copper strips for cables provided in an embodiment of the present application;

[0028] Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0029] Explanation of the reference numerals: 21, acquisition module; 22, processing module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory; 306, rolling mill thickness gauge signal interface. DETAILED DESCRIPTION

[0030] In order to enable technicians in this field 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 in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0031] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more listed items.

[0032] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0033] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0034] Please refer to Figure 1 , which shows a flow chart of a quality detection method for copper strips for cables provided in an embodiment of the present application, the flow chart mainly includes the following steps: S101 to S104.

[0035] Step S101 , in response to a quality inspection operation on a target copper coil, obtaining thickness data of the target copper coil by a thickness gauge.

[0036] Specifically, a high-precision thickness gauge installed on the copper strip rolling equipment is used, and the copper strip rolling equipment is calibrated during operation. The calibration process requires the use of copper samples of standard thickness, and the sensitivity and zero point of the thickness gauge are adjusted to ensure the accuracy and consistency of the measurement. During the rolling process of the target copper coil, the thickness gauge monitors the thickness data of the target copper coil in real time. Its measurement range covers the full width of the copper coil, and the thickness fluctuation is recorded through the thickness data.

[0037] In a possible implementation, step S101 further includes: obtaining a thickness fluctuation signal corresponding to the target copper coil by means of a high-precision thickness gauge within a preset time period; converting the thickness fluctuation signal into a digital information number by means of digital-to-analog conversion, and filtering the digital information number; and using the filtered digital information number as the thickness data of the target copper coil.

[0038] Specifically, after the quality inspection operation begins, within a continuous time period, that is, within a preset time period, the thickness fluctuation signal corresponding to the target copper coil is continuously obtained through a high-precision thickness gauge. The thickness fluctuation signal is output in analog form, including the thickness change information of the target copper coil and the noise signal introduced by possible equipment vibration or interference. Subsequently, the analog signal is sampled and converted into a digital signal by an analog-to-digital converter. The sampling frequency must meet the requirement of at least twice the highest frequency of the thickness fluctuation to avoid aliasing. The sampled digital signal is filtered to remove high-frequency noise, retain the main characteristics of the thickness fluctuation, and store it as the original time domain signal sequence in chronological order. At the same time, ensure that the sampling point density of the thickness gauge is high enough to cover the entire length of the copper coil. The sampling frequency can be dynamically adjusted according to the rolling speed. For example, when the rolling speed is 300m / min, the sampling frequency is set to 10kHz to reflect the details of the thickness fluctuation. In this process, real-time and robustness are particularly critical. The denoising process can effectively avoid erroneous quality judgments and ensure that the acquired thickness data provides a reliable basis for subsequent spectrum analysis and quality inspection. The digital information number that has been filtered is used as the thickness data of the target copper coil.

[0039] Step S102, constructing a frequency spectrum diagram corresponding to the target copper strip according to the thickness data.

[0040] Specifically, constructing the frequency spectrum law diagram corresponding to the target copper coil is mainly a process of frequency domain analysis. The thickness data is expressed as a raw signal that changes with time in the time domain, which may contain noise and interference information. In order to accurately capture the periodic fluctuations and characteristic frequencies in the signal, it is necessary to convert the time domain data into frequency domain data. That is, through frequency domain analysis, the main frequency, amplitude peak and energy distribution law of the thickness fluctuation can be clearly identified, thereby forming an intuitive frequency spectrum law diagram. In order to ensure the accuracy of the frequency spectrum law diagram, a sufficiently high resolution must be used in the construction process, combined with signal preprocessing and data screening steps to remove invalid data and random noise.

[0041] In a possible implementation, step S102 further includes: performing a fast Fourier transform on the thickness data to convert the thickness data from time domain data to frequency domain data; and constructing a frequency spectrum diagram corresponding to the target copper tape according to the transformed thickness data using a preset resolution.

[0042] Specifically, the acquired thickness data is subjected to fast Fourier transform to convert the time domain signal into a frequency domain signal. The Fourier transform formula is:

[0043]

[0044] Where X(k) is the kth frequency component in the frequency domain data, x(n) is the nth sampling point value in the time domain data, N is the total number of sampling points, k is the frequency index, and k = 0, 1, 2, 3, ..., N-1, j is the imaginary unit, and j satisfies 2 =1.

[0045] The core of the fast Fourier transform is to decompose the complex time domain signal into a series of sinusoidal wave components, so as to capture the main frequency of the thickness fluctuation and its corresponding amplitude in this way. Secondly, according to the transformed frequency domain data, the data is processed with a preset resolution to form a spectrum regularity diagram of the target copper coil. The choice of resolution needs to take into account the fineness of the analysis and the computational efficiency. Usually, a resolution of 4096 points or higher is used to ensure that the spectrum diagram can accurately reflect the fluctuation characteristics. When constructing the spectrum diagram, the position of the main frequency peak and its amplitude are marked, and the distribution of the spectrum energy is calculated to further support quality assessment. In the above formula, w(n)=0.5(1-cos(2πn / N-1)) is a weighted window function, including but not limited to Hanning window, Hamming window, Blackman window, etc. The weighted window function is used to reduce spectrum leakage. The spectrum regularity diagram can clearly show the thickness fluctuation characteristics of the target copper coil and reveal potential problems in processing. Combining information such as the main frequency, amplitude peak and energy distribution can provide a clear basis for subsequent quality detection and optimization.

[0046] Step S103, obtaining quality detection features corresponding to the target copper coil through the spectrum regularity diagram, the quality detection features including spectrum main frequency features, spectrum harmonic component features, spectrum bandwidth features and peak time domain position distribution features.

[0047] Specifically, the core of this step is to extract the quality detection features of the target copper coil through the spectrum regularity diagram. The quality detection features include but are not limited to the main frequency features of the spectrum, the harmonic component features of the spectrum, the spectrum bandwidth features, the time domain position distribution features of the peak value and other related calculation features. The quality detection features can comprehensively reflect the thickness fluctuation law of the copper coil from different angles of the frequency domain and time domain, thereby supporting the identification and evaluation of quality problems.

[0048] The main frequency feature of the spectrum is the main periodic manifestation of the thickness fluctuation of the target copper coil, corresponding to the main frequency component with the largest amplitude in the spectrum regularity diagram. When extracting this feature, it is necessary to search for the frequency value of the highest amplitude point in the frequency domain and record its amplitude. This feature directly reflects the main mode of thickness fluctuation and is an important basis for determining the stability of rolling quality.

[0049] The spectrum harmonic component characteristics describe the frequency and amplitude distribution of multiple harmonic components outside the main frequency. These harmonic components may be introduced by the mechanical vibration of the rolling equipment or the periodic error of the copper strip processing technology. By setting a certain range near the main frequency (such as 2 or 3 times the main frequency), extracting the frequency and amplitude of the significant harmonic components in the spectrum, and calculating the ratio of the harmonic to the main frequency amplitude, the influence of harmonics on the overall thickness fluctuation is quantified.

[0050] The spectrum bandwidth characteristic reflects the frequency energy distribution range of the thickness fluctuation signal. By calculating the bandwidth within a certain threshold range near the main frequency, the concentration and diffusion of the fluctuation can be evaluated. A wider bandwidth usually means that the thickness fluctuation signal contains more high-frequency components, which may lead to unstable processing.

[0051] The time domain position distribution characteristics of the peak value provide the distribution law of thickness fluctuation in the time dimension. In the time domain signal before the spectrum regularity diagram is constructed, identifying the occurrence position and amplitude of the fluctuation peak can reflect the transient abnormal behavior in the rolling process. By statistically analyzing the position distribution and amplitude changes of these peak values, the dynamic characteristics of the fluctuation can be further quantified.

[0052] In actual operation, the process of extracting these features includes: removing noise and interference components from the spectrum regularity diagram to ensure the accuracy of feature extraction; calculating the frequency and amplitude of the main frequency and harmonic components, the frequency range of the bandwidth, and the time distribution of the time domain peak to form a complete quality detection feature set. Through this process, the extracted quality detection features can fully reflect the quality status of the target copper coil from both the frequency domain and the time domain, providing a key basis for subsequent quality inspection and optimization.

[0053] Step S104: outputting the quality inspection result corresponding to the target copper tape according to the quality inspection feature through the copper tape peak statistical model.

[0054] Specifically, according to the quality detection characteristics, the reflection coefficient peak value corresponding to the target copper coil is calculated by the copper coil peak statistical model; and according to the reflection coefficient peak value, the quality detection result corresponding to the target copper coil is output. The construction process of the copper coil peak statistical model is as follows: a large number of thickness fluctuation data of different batches of copper coils and their corresponding actual reflection coefficient peak historical data are collected. The thickness fluctuation data is obtained through the frequency spectrum diagram extracted by steps S101 and S102, and the reflection coefficient peak is measured by experiment or simulation. In order to improve the robustness of the model, data samples covering different process conditions, equipment status and product specifications are required. After the historical data is collected, preprocessing is performed, including denoising, normalization and outlier detection, to ensure the integrity and quality of the data. Through historical data, the corresponding historical quality detection features are obtained, including but not limited to the main frequency features of the historical spectrum, the harmonic component features of the historical spectrum, the bandwidth features of the historical spectrum, the time domain position distribution features of the historical peak, etc. The historical data is divided into a training set, a validation set, and a test set. The training set is used to optimize the model parameters so that the model can minimize the error in predicting the reflection coefficient peak, and the hyperparameters of the model are adjusted through grid search or Bayesian optimization to further improve the model performance. The validation set is used to evaluate the performance indicators of the model, such as the mean square error (MSE), the coefficient of determination (R 2 ) to ensure that the model has high accuracy and robustness. If necessary, cross-validation can be used to further evaluate the performance of the model under different data distributions. For models with poor performance, analyze the source of errors, such as uncaptured high-order nonlinear relationships or insufficient features, iteratively optimize the feature set or adjust the model structure. Finally, deploy the tested copper coil peak statistical model for use in the production environment, and output the reflection coefficient peak based on the quality detection features collected in real time.

[0055] The peak value of the reflection coefficient is calculated using the copper tape peak statistical model according to the following formula:

[0056]

[0057] Among them, R max is the peak value of the reflection coefficient, N is the total number of quality detection features, f i is the i-th quality detection feature, g i (f i ) is the nonlinear weighting function corresponding to the i-th quality detection feature, α i is the weight factor corresponding to the i-th quality detection feature, f p and f q are any two different types of features in the quality detection features, h j (f p ,f q ) represents the jth interaction function, which is used to represent f p and fq The interaction relationship between j is the weight factor corresponding to the jth interaction function, γ is the global adjustment coefficient of the nonlinear combination, ψ k (f k ) is the nonlinear transformation function corresponding to a single quality detection feature, and K is the total number of quality detection features involved in the nonlinear transformation calculation.

[0058] The above calculation formula consists of three parts, namely the basic feature term, the interactive feature term and the nonlinear combination term. is the basic feature item, which includes all the quality detection features f i The contributions of are calculated separately and given different weights, reflecting the direct impact of individual quality detection features on the reflection coefficient peak; It is an interactive feature item. By describing the interaction relationship between features, the interaction relationship is a synergistic or conflicting effect, thereby enhancing the expressive power of the copper strip peak statistical model. The basic feature items alone cannot capture these interactive relationships, that is, many features of copper strip quality detection are not completely independent. The interactive item can capture the potential correlation between the main frequency of thickness fluctuation and the harmonic amplitude and spectrum bandwidth, providing supplementary information for the accurate prediction of the reflection coefficient peak. It is a nonlinear combination term. By introducing a global nonlinear transformation, multiple features are integrated into a whole to reflect the joint effect of complex features. That is, the nonlinear combination term is suitable for capturing the complex and high-order dependencies between quality detection features, and makes up for the deficiencies of basic feature terms and interactive feature terms in modeling high-dimensional spatial relationships, thereby improving the accuracy of reflection coefficient peak prediction.

[0059] To avoid overfitting of the model, you can also add a regularization term, which is Among them, λ is the regularization strength coefficient, which is used to control the constraint strength of the regularization term on the copper tape peak statistical model. The regularization strength coefficient can use the training set and the validation set. By trying different λ values, the performance of the copper tape peak statistical model on the validation set is compared, so as to select the setting that can achieve the best balanced performance on the validation set. Therefore, if Q is the final corrected reflection coefficient peak, then Q=R max -R.

[0060] In a possible implementation, step S104 also includes: determining whether the reflection coefficient peak value is greater than or equal to a first preset threshold value; if the reflection coefficient peak value is greater than or equal to the first preset threshold value, confirming that the quality inspection result is qualified; determining whether the reflection coefficient peak value is greater than or equal to a second preset threshold value and less than the first preset threshold value; if the reflection coefficient peak value is greater than or equal to the second preset threshold value and less than the first preset threshold value, confirming that the quality inspection result is quality to be optimized; determining that the reflection coefficient peak value is less than the second preset threshold value; if the reflection coefficient peak value is less than the second preset threshold value, confirming that the quality inspection result is unqualified.

[0061] Specifically, the calculated reflection coefficient peak value is compared with the first preset threshold and the second preset threshold value. Among them, the first preset threshold value represents the upper limit standard of the qualified quality of the copper coil, and the second preset threshold value represents the lower limit standard of the quality to be optimized, and the first preset threshold value is greater than the second preset threshold value. If the reflection coefficient peak value is greater than or equal to the first preset threshold value, it means that the thickness fluctuation characteristics, spectrum regularity, etc. of the copper coil have met the design and use requirements, its processing technology is stable, and the performance is within the standard range. At this time, the test result is confirmed to be qualified, and the test information is recorded for subsequent tracking and verification. If the reflection coefficient peak value is greater than or equal to the second preset threshold value and less than the first preset threshold value, it means that the reflection coefficient peak value of the copper coil has not yet reached the optimal standard, but is still within an acceptable range. There may be slight thickness fluctuations or harmonic effects, but they have not seriously affected the performance of the cable. At this time, it is confirmed that the test result is quality to be optimized, and it is recommended to optimize the production process, including but not limited to adjusting the roller pressure, grinding the roller, adjusting the rolling speed, etc. If the reflection coefficient peak value is less than the second preset threshold value, it means that the thickness fluctuation of the copper coil is too large or there is a significant spectrum anomaly, which may cause the reflection coefficient peak value to significantly exceed the use requirements and affect the performance of the coaxial cable. At this point, confirm that the test result is unqualified, and mark the problematic copper coil to avoid entering the subsequent process. At the same time, output a diagnostic report to locate the possible source of the quality problem. Through the above judgment logic, complex quality inspection results can be divided into clear levels, intuitively reflecting the quality status of the copper coil, and providing a scientific basis for subsequent process optimization. It should be noted that the preset threshold needs to be determined through a large amount of experimental data and industry standards. It is usually set according to different copper coil specifications and application scenarios. In addition, the number of preset thresholds can be increased or decreased according to the actual needs of the scenario. For example, in a simplified scenario, only one preset threshold can be set. When the reflection coefficient peak is greater than or equal to the preset threshold, the quality inspection result is qualified, otherwise the quality inspection result is unqualified.

[0062] For example, in order to verify the practicality of the copper coil peak statistical model in the embodiment of the present application, please refer to Table 1, which describes the statistical law of the reflection coefficient peak value calculated by the copper coil peak statistical model when the finishing mill roll box and the bearing box are used in combination.

[0063] [Table 1]

[0064]

[0065] By analyzing and optimizing the rules in Table 1, the processing quality of the copper coil can be significantly improved and the fluctuation amplitude of the reflection coefficient peak can be reduced, thereby verifying the practicality of the copper coil peak statistical model in the embodiment of the present application.

[0066] The present application adopts the above method. When performing quality inspection operation on the target copper coil, the thickness data of the target copper coil is obtained by a thickness gauge, and a frequency spectrum regularity diagram corresponding to the target copper coil is constructed based on the thickness data, so that the quality inspection feature corresponding to the target copper coil is obtained through the frequency spectrum regularity diagram, and the quality inspection result corresponding to the target copper coil is output according to the quality inspection feature through the copper coil peak statistical model, and then the thickness data of the target copper coil is accurately obtained through the thickness gauge, and a frequency spectrum regularity diagram is constructed based on the thickness data, the quality inspection feature is extracted, and the peak statistical model is used to analyze the feature data, thereby realizing accurate judgment of the overall quality trend of the copper coil, and greatly improving the accuracy of judgment of the production quality of the copper coil.

[0067] Please refer to Figure 2 , which shows a module schematic diagram of a quality detection device for a copper strip for a cable provided in an embodiment of the present application, the device includes an acquisition module 21 and a processing module 22, wherein:

[0068] The acquisition module 21 is used to respond to the quality inspection operation on the target copper coil and obtain the thickness data of the target copper coil through the thickness gauge; through the spectrum regularity diagram, the quality inspection characteristics corresponding to the target copper coil are obtained, and the quality inspection characteristics include the main frequency characteristics of the spectrum, the harmonic component characteristics of the spectrum, the bandwidth characteristics of the spectrum and the time domain position distribution characteristics of the peak.

[0069] The processing module 22 is used to construct a frequency spectrum diagram corresponding to the target copper coil according to the thickness data; and output the quality detection result corresponding to the target copper coil according to the quality detection characteristics through the copper coil peak statistical model.

[0070] In a possible embodiment, the acquisition module 21 is used to obtain the thickness data of the target copper coil through a thickness gauge, specifically including: within a preset time period, obtaining a thickness fluctuation signal corresponding to the target copper coil through a high-precision thickness gauge; converting the thickness fluctuation signal into a digital information number through digital-to-analog conversion, and filtering the digital information number; and using the filtered digital information number as the thickness data of the target copper coil.

[0071] In one possible implementation, the processing module 22 is used to construct a frequency spectrum diagram corresponding to the target copper coil according to the thickness data, specifically including: performing a fast Fourier transform on the thickness data to convert the thickness data from time domain data to frequency domain data; and constructing a frequency spectrum diagram corresponding to the target copper coil according to the transformed thickness data using a preset resolution.

[0072] In a possible implementation, the processing module 22 is used to perform a fast Fourier transform on the thickness data to convert the thickness data from time domain data to frequency domain data, specifically including: performing a fast Fourier transform using the following formula:

[0073]

[0074] Where X(k) is the kth frequency component in the frequency domain data, x(n) is the nth sampling point value in the time domain data, N is the total number of sampling points, k is the frequency index, and k = 0, 1, 2, 3, ..., N-1, j is the imaginary unit, and j satisfies 2 =1.

[0075] In a possible implementation, the processing module 22 is used to output the quality inspection result corresponding to the target copper tape according to the quality inspection characteristics through the copper tape peak statistical model, specifically including: calculating the reflection coefficient peak value corresponding to the target copper tape through the copper tape peak statistical model according to the quality inspection characteristics; outputting the quality inspection result corresponding to the target copper tape according to the reflection coefficient peak value.

[0076] In a possible implementation, the processing module 22 is used to calculate the reflection coefficient peak value corresponding to the target copper tape according to the quality detection characteristics through the copper tape peak statistical model, specifically including: calculating the reflection coefficient peak value according to the following formula:

[0077]

[0078] Among them, R max is the peak value of the reflection coefficient, N is the total number of quality detection features, f i is the i-th quality detection feature, g i (f i ) is the nonlinear weighting function corresponding to the i-th quality detection feature, α i is the weight factor corresponding to the i-th quality detection feature, f p and f q are any two different types of features in the quality detection features, h j (f p ,f q ) represents the jth interaction function, which is used to represent f p and f q The interaction relationship betweenj is the weight factor corresponding to the jth interaction function, γ is the global adjustment coefficient of the nonlinear combination, ψ k (f k ) is the nonlinear transformation function corresponding to a single quality detection feature, and K is the total number of quality detection features involved in the nonlinear transformation calculation.

[0079] In one possible embodiment, the processing module 22 is used to output the quality inspection result corresponding to the target copper coil, specifically including: determining whether the reflection coefficient peak value is greater than or equal to a first preset threshold value; if the reflection coefficient peak value is greater than or equal to the first preset threshold value, confirming that the quality inspection result is qualified; determining whether the reflection coefficient peak value is greater than or equal to a second preset threshold value and less than the first preset threshold value; if the reflection coefficient peak value is greater than or equal to the second preset threshold value and less than the first preset threshold value, confirming that the quality inspection result is quality to be optimized; determining that the reflection coefficient peak value is less than the second preset threshold value; if the reflection coefficient peak value is less than the second preset threshold value, confirming that the quality inspection result is unqualified.

[0080] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0081] The present application also provides an electronic device. Figure 3 , Figure 3 30 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include: at least one processor 301, at least one communication bus 302, a user interface 303, at least one network interface 304, a memory 305, and a rolling mill thickness gauge signal interface 306.

[0082] The communication bus 302 is used to realize the connection and communication between these components.

[0083] The user interface 303 may include a display screen (Display), and the optional user interface 303 may also include a standard wired interface and a wireless interface.

[0084] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0085] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or more combinations of a central processing unit (Central Processing Unit, CPU), and a graphics processing unit (Graphics Processing Unit, GPU). Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content that needs to be displayed on the display screen; and the modem is used to handle wireless communication.

[0086] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and a quality detection application program for cable copper tape.

[0087] exist Figure 3In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the quality detection application program for copper tape for cables stored in the memory 305. When executed by one or more processors 301, the electronic device executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0088] The present application also provides a computer-readable storage medium, which stores instructions. When executed by one or more processors, the electronic device executes one or more of the methods described in the above embodiments.

[0089] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0090] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0091] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0092] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0093] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0094] The above are only exemplary embodiments disclosed in this application, and the scope of the disclosure of this application cannot be limited by this. That is, any equivalent changes and modifications made according to the teachings disclosed in this application are still within the scope of the disclosure of this application. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other implementation schemes disclosed in this application.

[0095] This application is intended to cover any modifications, uses or adaptive changes disclosed in this application, which follow the general principles disclosed in this application and include common knowledge or customary technical means in the technical field not recorded in this application.

Claims

1. A method for detecting the quality of copper strips for cables, characterized in that: The method comprises: In response to a quality inspection operation on a target copper coil, obtaining thickness data of the target copper coil by a thickness gauge; According to the thickness data, construct a frequency spectrum regularity diagram corresponding to the target copper strip; The quality detection features corresponding to the target copper coil are obtained through the spectrum regularity diagram, wherein the quality detection features include the main frequency features of the spectrum, the harmonic component features of the spectrum, the bandwidth features of the spectrum, and the time domain position distribution features of the peak values; Through the copper coil peak statistical model, according to the quality detection characteristics, the quality detection result corresponding to the target copper coil is output.

2. The method according to claim 1, characterized in that The step of obtaining the thickness data of the target copper coil by using a thickness gauge specifically includes: Within a preset time period, a thickness fluctuation signal corresponding to the target copper strip is obtained by a high-precision thickness gauge; Converting the thickness fluctuation signal into a digital information signal by digital-to-analog conversion, and filtering the digital information signal; The digital information number subjected to the filtering process is used as the thickness data of the target copper tape.

3. The method according to claim 1, characterized in that The constructing a frequency spectrum regularity diagram corresponding to the target copper strip according to the thickness data specifically includes: Performing a fast Fourier transform on the thickness data to convert the thickness data from time domain data to frequency domain data; According to the transformed thickness data, the frequency spectrum diagram corresponding to the target copper strip is constructed using a preset resolution.

4. The method according to claim 3, characterized in that The performing fast Fourier transform on the thickness data to convert the thickness data from time domain data into frequency domain data specifically includes: The fast Fourier transform is performed by the following formula: Wherein, X(k) is the kth frequency component in the frequency domain data, x(n) is the nth sampling point value in the time domain data, N is the total number of sampling points, k is the frequency index, and k=0,1,2,3,…,N-1, j is an imaginary unit, and j satisfies 2 =1.

5. The method according to claim 1, characterized in that The copper coil peak statistical model is used to output the quality detection result corresponding to the target copper coil according to the quality detection feature, specifically including: According to the quality detection feature, the reflection coefficient peak value corresponding to the target copper tape is calculated by using the copper tape peak statistical model; According to the reflection coefficient peak value, the quality detection result corresponding to the target copper tape is output.

6. The method according to claim 5, characterized in that The step of calculating the reflection coefficient peak value corresponding to the target copper tape by using the copper tape peak statistical model according to the quality detection feature specifically includes: The reflection coefficient peak is calculated according to the following formula: Among them, R max is the reflection coefficient peak value, N is the total number of quality detection features, and f i is the i-th quality detection feature, g i (f i ) is the nonlinear weighting function corresponding to the i-th quality detection feature, α i is the weight factor corresponding to the i-th quality detection feature, f p and f q are any two different types of features in the quality detection features, h j (f p ,f q ) represents the jth interaction function, which is used to represent f p and f q The interaction relationship between j is the weight factor corresponding to the jth interaction function, γ is the global adjustment coefficient of the nonlinear combination, ψ k (f k ) is the nonlinear transformation function corresponding to a single quality detection feature, and K is the total number of quality detection features involved in the nonlinear transformation calculation.

7. The method according to claim 5, characterized in that The outputting of the quality inspection result corresponding to the target copper coil specifically includes: Determining whether the reflection coefficient peak value is greater than or equal to a first preset threshold; If the reflection coefficient peak value is greater than or equal to the first preset threshold, confirming that the quality detection result is qualified; Determine whether the reflection coefficient peak value is greater than or equal to a second preset threshold and less than the first preset threshold; If the reflection coefficient peak value is greater than or equal to the second preset threshold value and less than the first preset threshold value, confirming that the quality detection result is quality to be optimized; Determining that the reflection coefficient peak value is less than the second preset threshold; If the reflection coefficient peak value is less than the second preset threshold, the quality detection result is confirmed to be unqualified.

8. A quality detection device for copper strips for cables, characterized in that: The device comprises an acquisition module and a processing module, wherein: The acquisition module is used to respond to the quality detection operation on the target copper coil and obtain the thickness data of the target copper coil through the thickness gauge; obtain the quality detection features corresponding to the target copper coil through the spectrum regularity diagram, and the quality detection features include the main frequency feature of the spectrum, the harmonic component feature of the spectrum, the bandwidth feature of the spectrum and the time domain position distribution feature of the peak value; The processing module is used to construct the frequency spectrum diagram corresponding to the target copper tape according to the thickness data; and output the quality detection result corresponding to the target copper tape according to the quality detection characteristics through the copper tape peak statistical model.

9. An electronic device, characterized in that: It includes a processor, a communication bus, a user interface, a network interface and a memory, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

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