A system for analyzing current test based on copper wire processing

Abstract

The present application relates to copper wire processing quality detection technical field, more specifically, it relates to a kind of power-on test analysis system based on copper wire processing, for solving the problem that prior art cannot realize the high-precision dynamic prediction of copper wire temperature field, cannot fuse physical modeling and deep learning, cannot provide reliable temperature basis for structure evaluation and life prediction;The present application fuses physical modeling and deep learning through thermal effect analysis module, realizes the high-precision dynamic prediction of copper wire temperature field through electric-thermal coupling simulation and physical constraint neural network, has multivariable input and strong nonlinear modeling capacity, provides reliable temperature basis for structure evaluation and life prediction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of copper wire processing quality detection, more particularly to a power-on test analysis system based on copper wire processing. BACKGROUND

[0002] Copper wire is the core material in the field of power transmission, electronic equipment and communication, and its quality directly determines the performance and safety of the product. However, the existing detection technology has three defects: the detection of electrical conductivity relies on manual operation, resulting in low efficiency and easy misjudgment; the mechanical strength test only focuses on a single tensile index and cannot comprehensively evaluate the comprehensive mechanical properties; the surface defect recognition ability is weak and easy to miss small defects; in addition, each detection link operates independently, and the data cannot be linked and analyzed, which makes it difficult to support process optimization and closed-loop quality management, and seriously restricts the production efficiency and product quality improvement of the industry.

[0003] The patent application with publication number CN108469546A discloses a wire impedance test method and system, which includes: after receiving a test instruction, obtaining the wire parameters of the wire to be tested, the voltage at both ends of the wire to be tested, and the voltage at both ends of the test resistor connected in series with the wire to be tested; calculating the joint impedance of the wire to be tested according to the obtained wire parameters of the wire to be tested, the voltage at both ends of the wire to be tested, and the voltage at both ends of the test resistor; comparing the calculated joint impedance with the standard joint impedance, if the joint impedance does not exceed the standard joint impedance, generating a test result that the wire to be tested is qualified, otherwise generating a test result that the wire to be tested is unqualified, the application can accurately calculate the joint impedance of the wire to be tested, compare the calculated joint impedance of the wire to be tested with the industry standard joint impedance to determine whether the wire to be tested is qualified, the error range is small, the accuracy is high, the production process is simplified, the production efficiency is improved, the operator does not need to have high professional knowledge, and the application is suitable for batch testing;

[0004] However, the above-mentioned reference patent calculates the joint impedance by collecting wire parameters and voltage data, and compares it with the standard value to determine the qualification, realizes high-precision automatic detection, has small error and high efficiency, does not need professional operation, is suitable for batch testing, but cannot realize high-precision dynamic prediction of the copper wire temperature field, cannot integrate physical modeling and deep learning, cannot provide reliable temperature basis for structure evaluation and life prediction; at the same time, cannot use the finite element method to simulate structure deformation and stress evolution, cannot combine implicit solution and multi-physical field collaborative analysis, and cannot realize safety warning and reliability evaluation of the copper wire structure.

[0005] Therefore, we propose a power-on test analysis system based on copper wire processing to solve the above problems. SUMMARY

[0006] The purpose of this invention is to provide an electrical testing and analysis system based on copper wire processing, which solves the problems of existing technologies that cannot achieve high-precision dynamic prediction of copper wire temperature field, cannot integrate physical modeling and deep learning, and cannot provide reliable temperature basis for structural assessment and life prediction; at the same time, they cannot use the finite element method to simulate structural deformation and stress evolution, cannot combine implicit solution and multi-physics field collaborative analysis, and cannot achieve safety early warning and reliability assessment of copper wire structures.

[0007] The objective of this invention is achieved through the following technical solution:

[0008] A current-conductivity testing and analysis system based on copper wire processing, applied to a current-conductivity testing management platform, includes:

[0009] The electrical data acquisition module is used to collect key electrical parameters of copper wires under different energizing conditions in real time and to perform preprocessing operations on the collected key electrical parameters.

[0010] The thermal effect analysis module constructs an electro-thermal coupling model of the copper wire during energization based on preprocessed key electrical parameters, and uses a physically constrained neural network to simulate the temperature distribution.

[0011] The defect detection module extracts current and resistance perturbation signals based on preprocessed key electrical parameters, and uses multi-scale reconstruction and pattern recognition technology, combined with pulse excitation and defect fingerprint database comparison, to complete the detection and location of sub-millimeter level internal defects.

[0012] The mechanical stability assessment module establishes a thermo-mechanical response model of the copper wire under energization based on preprocessed key electrical parameters and temperature distribution data, and uses the finite element method to simulate the evolution of internal stress and structural deformation characteristics.

[0013] In a preferred embodiment of the present invention, the process by which the thermal effect analysis module constructs an electro-thermal coupling model during the energization of the copper wire includes:

[0014] Obtain the key electrical parameters after preprocessing, including current, voltage, power factor and impedance;

[0015] The electro-thermal coupling model consists of two sub-models: an electrical model and a heat conduction model;

[0016] The electrical model is based on Ohm's law and Joule's law, the heat conduction model is based on the heat diffusion equation, and the resistance of the copper wire changes with temperature.

[0017] The electro-thermal coupling model is solved using a numerical method, and the specific steps are as follows:

[0018] S1: Initialize the geometry, boundary conditions, and initial temperature distribution of the copper wire;

[0019] S2: Input the current and resistance values ​​at the current moment to calculate the heat generation power;

[0020] S3: Substitute the heat generation power as a heat source term into the heat conduction equation, and discretize the heat conduction equation using the finite difference method or the finite element method.

[0021] S4: Solve the temperature field, update the temperature values ​​of each node of the copper wire, and recalculate the resistance based on the new temperature values;

[0022] S5: Return to step S2 and proceed to the next time step iteration.

[0023] In a preferred embodiment of the present invention, the process of simulating temperature distribution using a physically constrained neural network in the thermal effect analysis module includes:

[0024] The physical constraint neural network adopts a feedforward neural network structure, which includes an input layer, several hidden layers, and an output layer;

[0025] The input layer includes the following input variables: time variable t, spatial coordinate x, current value I(t), voltage value V(t), power factor PF(t), and impedance value Z(t);

[0026] Each hidden layer consists of multiple neurons, which transmit information through weighted connections. By using weighted connections and activation function operations, the potential relationship between input variables and copper wire temperature is explored.

[0027] The output layer is a temperature value T(x, t), which represents the temperature of the copper wire at a specified time and spatial location;

[0028] The loss function consists of two parts: data terms and physical terms.

[0029] The data item is used to calculate the squared error between the network output and the reference data;

[0030] The total loss function is constructed by weighted summation of data terms and physical terms, where each term is multiplied by a fixed value set before training begins as its corresponding weighting coefficient, and then the terms are summed.

[0031] The training steps for a physically constrained neural network are as follows:

[0032] T1: Initialize neural network parameters;

[0033] T2: Input the time, space, and electrical parameters from the training samples;

[0034] T3: Forward propagation calculation output temperature;

[0035] T4: Calculate the loss function based on the current output;

[0036] T5: Backpropagation adjusts network parameters;

[0037] T6: Repeat steps S3-S5 until the loss function converges.

[0038] In a preferred embodiment of the present invention, the process of the defect detection module extracting current and resistance perturbation signals includes:

[0039] Obtain the normalized current, voltage, power factor, and impedance time series of the preprocessed output;

[0040] Structural changes in an electrical system can cause local disturbances in current and impedance. The current perturbation signal is obtained from the time derivative of the current.

[0041] The impedance perturbation signal is obtained from the impedance time derivative:

[0042] The extracted perturbation signal is processed by multi-level wavelet transform or local least squares method. Wavelet transform is used to reconstruct the signal at multiple scales.

[0043] In a preferred embodiment of the present invention, the process by which the defect detection module analyzes the perturbation signal using multi-scale reconstruction and pattern recognition technology includes:

[0044] The perturbation signal contains multiple frequency components. Discrete wavelet transform is used for multi-scale reconstruction. The specific steps are as follows:

[0045] Wavelet decomposition was performed on the current perturbation signal and the impedance perturbation signal, respectively.

[0046] The signal of a specific frequency band is reconstructed as needed, with the low-frequency part used to extract trend features and the high-frequency part used to capture abrupt changes.

[0047] The root mean square value, average amplitude, and peak factor features are extracted from the reconstructed signal.

[0048] Perform a Fourier transform on the signal to obtain a spectrum, and extract the spectral peaks, power spectral density, and energy concentration band features;

[0049] Through multi-scale reconstruction and feature extraction, the perturbation signal is comprehensively characterized. Based on the extracted signal features, pattern recognition technology is used to classify defects. The implementation steps are as follows:

[0050] Extract time-domain features from the reconstructed signal: peak factor, root mean square value, variance, peak-to-peak interval; extract frequency-domain features: spectral peak value, power spectral density, and main frequency band energy.

[0051] Use principal component analysis or linear discriminant analysis to reduce the dimensionality of the feature space;

[0052] A supervised learning algorithm is used to train a classifier, with the input being a feature vector and the output being the defect category;

[0053] The classifier outputs the defect type and characteristic parameters, which are then combined with pulse excitation analysis to determine the defect location.

[0054] In a preferred embodiment of the present invention, the process of sub-millimeter-level defect detection and localization by the defect detection module combining pulse excitation and defect fingerprint database comparison includes:

[0055] The reflection and transmission characteristics of a copper wire are obtained by injecting a broadband high-frequency pulse signal to excite its response. The specific steps are as follows:

[0056] A pulse signal with specified parameters is generated using a signal generator. The injected signal covers multiple frequency bands and propagates into the interior of the copper wire, interacting with the material.

[0057] Record the time delay and amplitude changes of the reflected signal;

[0058] The location of the defect is calculated using the relationship between the arrival time of the reflected signal and the speed of signal propagation.

[0059] Improve temporal resolution by increasing the signal sampling rate;

[0060] Establish a defect fingerprint database to store typical signal characteristics of different types of defects. The fingerprint database includes characteristic data of current and impedance perturbation signals, reflection waveforms under pulse excitation, and time delay and amplitude changes corresponding to defects.

[0061] During real-time detection, the following comparison operations are performed:

[0062] Calculate the cosine similarity between the current signal and the signals in the fingerprint database to find the best matching defect type;

[0063] A similarity threshold is set. If the similarity between the current signal and a certain type of defect is higher than the threshold, it is determined to be that type of defect.

[0064] In a preferred embodiment of the present invention, the process by which the mechanical stability assessment module establishes a thermo-mechanical response model of the copper wire under energization includes:

[0065] The key electrical parameters and temperature distribution data after preprocessing are obtained. The temperature distribution data is the temperature field T(x,t), which is the temperature of the copper wire at different locations and time points.

[0066] Copper wire generates Joule heat during the energization process, causing non-uniform temperature rise, which in turn leads to thermal expansion and stress evolution. The thermo-mechanical response model consists of three basic processes: heat conduction process, thermal strain-stress relationship, and mechanical equilibrium conditions.

[0067] Heat conduction process: Temperature diffuses within a material through heat conduction. This process involves material density, specific heat capacity, thermal conductivity, and the Joule heat source term, which is calculated from current and resistance.

[0068] Relationship between thermal strain and stress: Temperature rise causes thermal expansion, which generates thermal strain. Thermal strain is the difference between the current temperature and the reference temperature multiplied by the coefficient of thermal expansion. Under small deformation linear elastic conditions, thermal stress is the Young's modulus of the material multiplied by the difference between the total strain and the thermal strain.

[0069] Mechanical equilibrium condition: The stress state inside the material must satisfy the mechanical equilibrium condition, which is represented by the sum of the divergence of the stress tensor and the volume force density being equal to zero. This relationship needs to be solved in conjunction with the boundary conditions to obtain the deformation distribution of the material.

[0070] In a preferred embodiment of the present invention, the mechanical stability assessment module uses the finite element method to simulate the evolution of internal stress and structural deformation characteristics, including the following process:

[0071] The finite element method is a numerical method for solving systems of partial differential equations. The entire simulation process includes modeling, mesh generation, boundary condition setting, time integration scheme, and coupled solution.

[0072] Geometric modeling and mesh generation: A three-dimensional model is established based on the actual geometry of the copper wire, and then divided into tetrahedral or hexahedral elements;

[0073] Initial and boundary conditions are set as follows: the initial temperature is set to ambient temperature, the initial stress and strain are zero, and the boundary conditions include:

[0074] Thermal boundary conditions: Set convective heat transfer or adiabatic boundary conditions;

[0075] Force boundary conditions: Set fixed ends, free ends, or other constraints;

[0076] Heat source application: Distribute the heat source calculated from the current and resistance to each unit;

[0077] Thermo-mechanical coupling calculation process: A multiphysics coupling method is used for simultaneous solution. The following operations are performed at each time step:

[0078] Calculate the heat source based on current and resistance;

[0079] Solve the heat conduction equation to update the temperature;

[0080] Calculate the thermal strain based on temperature, and then calculate the stress.

[0081] Substitute the stress into the equilibrium equations to solve for the displacement field;

[0082] Update the deformed geometric and thermal properties, and proceed to the next time step;

[0083] Repeat the above process until both temperature and stress converge;

[0084] Time integration and solution method: Implicit time integration is used for time progression, and the linear equations are solved using the sparse matrix iterative solution method.

[0085] In a preferred embodiment of the present invention, the process of the mechanical stability assessment module analyzing and evaluating the finite element simulation results includes:

[0086] After the simulation is completed, a comprehensive analysis is performed on the temperature field, stress field, and deformation field.

[0087] Temperature stress analysis: Comparing the thermal stress distribution with the material's yield strength to determine if there are regions that exceed the limit;

[0088] Deformation analysis: Calculate the maximum deformation and deformation gradient of the copper wire throughout the entire process, and analyze whether there is stress concentration or structural interference;

[0089] Thermal fatigue analysis: Under periodic energization, stress cyclic changes are analyzed, and thermal fatigue life is estimated using the Goodman or Coffin-Manson method;

[0090] Stability criterion establishment: A stability criterion is established based on the maximum temperature gradient, maximum equivalent stress, and maximum displacement to determine whether the copper wire is in a stable state.

[0091] Compared with the prior art, the advantages of this invention are:

[0092] (1) In this invention, the thermal effect analysis module integrates physical modeling and deep learning. Through electro-thermal coupling simulation and physical constraint neural network, it realizes high-precision dynamic prediction of the temperature field of copper wire. It has multi-variable input and strong nonlinear modeling capabilities, providing reliable temperature basis for structural evaluation and life prediction.

[0093] (2) In this invention, the mechanical stability assessment module constructs a thermo-mechanical coupling model based on electrical and temperature data, uses the finite element method to simulate structural deformation and stress evolution, and combines implicit solution and multi-physics field collaborative analysis to ensure calculation stability. Through post-processing, thermal stress, deformation and fatigue damage are evaluated, stability criteria are established, and safety early warning and reliability assessment of copper wire structure are realized. Attached Figure Description

[0094] Figure 1 This is a system block diagram of the present invention;

[0095] Figure 2 This is a flowchart of the steps for solving the electro-thermal coupling model in this invention;

[0096] Figure 3This is a flowchart of the training steps for the physical constraint neural network in this invention. Detailed Implementation

[0097] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0098] Example 1: As Figure 1 , Figure 2 and Figure 3 As shown, the present invention proposes a current-conductivity testing and analysis system based on copper wire processing, which is applied to a current-conductivity testing management platform, comprising:

[0099] The electrical data acquisition module is used to acquire key electrical parameters of copper wires under different energizing conditions in real time (achieved through a multi-channel electrical sensor array). The acquired key electrical parameters are preprocessed, including data cleaning, calibration, anomaly detection, synchronous processing, and data standardization.

[0100] The electrical data acquisition module acquires key parameters such as current, voltage, power factor, and impedance of copper wires in real time under different energizing conditions through a multi-channel sensor array. It also performs preprocessing such as cleaning, calibration, anomaly detection, synchronization, and standardization to ensure the accuracy, consistency, and usability of the data. The module has high precision, high stability, and strong adaptability, which can effectively support subsequent modeling analysis, fault early warning, and intelligent diagnosis, providing a reliable data foundation for copper wire performance evaluation and condition monitoring.

[0101] The thermal effect analysis module constructs an electro-thermal coupling model of the copper wire during energization based on preprocessed key electrical parameters, and uses a physically constrained neural network to simulate the temperature distribution.

[0102] The process of constructing the electro-thermal coupling model of the copper wire during energization in the thermal effect analysis module includes:

[0103] Obtain the key electrical parameters after preprocessing, including current, voltage, power factor and impedance;

[0104] The electro-thermal coupling model consists of two sub-models: an electrical model and a heat conduction model;

[0105] The electrical model is based on Ohm's law and Joule's law and is used to calculate the heat power generated per unit volume: P = I 2 R(T);

[0106] Where P represents the heat power generated per unit volume, I represents the current flowing through the copper wire, and R(T) represents the resistance of the copper wire at the current temperature;

[0107] The heat conduction model is based on the heat diffusion equation and is used to describe the temperature variation within the copper wire over time and space.

[0108] Where T represents the current temperature and α represents the thermal diffusivity. The spatial second derivative of temperature is represented by ρ, copper linear density is represented by c, and specific heat capacity is represented by c.

[0109] The resistance of copper wire changes with temperature, and this relationship can be expressed by the following function:

[0110] R(T) = R0[1 + β(T - T0)];

[0111] Where R0 represents the initial resistance at reference temperature T0, and β represents the temperature coefficient of resistance of copper material. This function is used to update the resistance value at each time step.

[0112] The electro-thermal coupling model is solved using a numerical method, and the specific steps are as follows:

[0113] S1: Initialize the geometry, boundary conditions, and initial temperature distribution of the copper wire;

[0114] S2: Input the current and resistance values ​​at the current moment to calculate the heat generation power;

[0115] S3: Substitute the heat generation power as a heat source term into the heat conduction equation, and discretize the heat conduction equation using the finite difference method or the finite element method.

[0116] S4: Solve the temperature field, update the temperature values ​​of each node of the copper wire, and recalculate the resistance based on the new temperature values;

[0117] S5: Return to step S2 and proceed to the next time step iteration. Through the above process, the time-step simulation of the temperature field during the energization of the copper wire is realized.

[0118] The thermal effect analysis module uses a physically constrained neural network to simulate the temperature distribution process, which includes:

[0119] Physically constrained neural networks are a method that embeds partial differential equations into deep learning models. This method introduces control equations as hard constraints during the training process of the neural network, so that the network output directly conforms to the mathematical form of the target problem.

[0120] The physical constraint neural network adopts a feedforward neural network structure, which includes an input layer, several hidden layers and an output layer. Information in the network is transmitted unidirectionally from the input layer, processed by several hidden layers and then reaches the output layer.

[0121] The input layer includes the following input variables: time variable t, spatial coordinate x, current value I(t), voltage value V(t), power factor PF(t), and impedance value Z(t);

[0122] Each hidden layer consists of multiple neurons, which transmit information through weighted connections. By using weighted connections and activation function operations, the potential relationship between input variables and copper wire temperature is explored. The activation function is a non-linear function.

[0123] The output layer is a temperature value T(x, t), which represents the temperature of the copper wire at a specified time and spatial location;

[0124] The loss function consists of two parts: data terms and physical terms.

[0125] The data item is used to calculate the squared error between the network output and the reference data:

[0126]

[0127] Where T yc (x i , t i (T) is the temperature output by the network. ck (x i ,t i () is the reference data, and N is the sample size;

[0128] The physics term is used to calculate the degree of deviation of the network output from the heat conduction equation:

[0129]

[0130] in It is the first derivative of temperature with respect to time, and M is the number of sampling points;

[0131] The total loss function is constructed by weighted summation of data terms and physical terms, where each term is multiplied by a fixed value set before training begins as its corresponding weighting coefficient, and then the terms are summed.

[0132] The training steps for a physically constrained neural network are as follows:

[0133] T1: Initialize neural network parameters;

[0134] T2: Input the time, space, and electrical parameters from the training samples;

[0135] T3: Forward propagation calculation output temperature;

[0136] T4: Calculate the loss function based on the current output;

[0137] T5: Backpropagation adjusts network parameters;

[0138] T6: Repeat steps S3-S5 until the loss function converges;

[0139] Throughout the training process, the network parameters are updated according to the gradient direction to ensure that the output conforms to the form of the reference data and the heat conduction equation.

[0140] The thermal effect analysis module integrates the advantages of numerical modeling based on physical laws and data fitting through deep learning, accurately reflecting the temperature evolution of copper wires under different energizing conditions. Through iterative solutions of electrical and thermal conduction models, it achieves dynamic simulation of heat generation and transfer processes. Simultaneously, it employs a physically constrained neural network, introducing the thermal conduction equation as a hard constraint in the modeling process, ensuring that the network output strictly conforms to physical laws, thus improving the reliability and generalization ability of the prediction results. The module supports multivariate inputs and possesses excellent nonlinear modeling capabilities, making it suitable for high-precision estimation of temperature distribution under complex boundary conditions, providing a solid foundation for subsequent structural stability assessment and lifetime prediction.

[0141] The defect detection module extracts current and resistance perturbation signals based on preprocessed key electrical parameters, and uses multi-scale reconstruction and pattern recognition technology, combined with pulse excitation and defect fingerprint database comparison, to complete the detection and location of sub-millimeter level internal defects.

[0142] The process by which the defect detection module extracts current and resistance perturbation signals includes:

[0143] The standardized current, voltage, power factor, and impedance time series of the preprocessed output are obtained. The resistance change information can be obtained through joint analysis of the impedance signal and the current signal.

[0144] Structural changes in an electrical system can cause local disturbances in current and impedance. The current perturbation signal is obtained from the time derivative of the current.

[0145] The impedance perturbation signal is obtained from the impedance time derivative:

[0146] The extracted perturbation signal is processed by multi-level wavelet transform or local least squares method. Wavelet transform is used to reconstruct the signal at multiple scales, and soft thresholding method is used to remove noise components.

[0147] The above method is used to obtain high-resolution current and impedance perturbation signals, which serve as the basic input for subsequent defect analysis.

[0148] The defect detection module uses multi-scale reconstruction and pattern recognition technology to analyze perturbation signals. The process includes:

[0149] The perturbation signal contains multiple frequency components. Discrete wavelet transform is used for multi-scale reconstruction. The specific steps are as follows:

[0150] Wavelet decomposition is performed on the current perturbation signal and the impedance perturbation signal respectively. The decomposition results include low-frequency approximation coefficients and high-frequency detail coefficients. The wavelet basis functions are of a fixed type, such as Haar wavelet or Daubechies wavelet.

[0151] The signal of a specific frequency band is reconstructed as needed, with the low-frequency part used to extract trend features and the high-frequency part used to capture abrupt changes.

[0152] The root mean square value, average amplitude, and peak factor features are extracted from the reconstructed signal.

[0153] Perform a Fourier transform on the signal to obtain a spectrum, and extract the spectral peaks, power spectral density, and energy concentration band features;

[0154] Through multi-scale reconstruction and feature extraction, the perturbation signal is comprehensively characterized. Based on the extracted signal features, pattern recognition technology is used to classify defects. The implementation steps are as follows:

[0155] Extract time-domain features from the reconstructed signal: peak factor, root mean square value, variance, peak-to-peak interval; extract frequency-domain features: spectral peak value, power spectral density, and main frequency band energy.

[0156] Principal component analysis or linear discriminant analysis can be used to reduce the dimensionality of the feature space, retaining feature vectors with high discriminative power and reducing redundant information and computational complexity.

[0157] The classifier is trained using a supervised learning algorithm. The input is a feature vector, and the output is the defect category. Optional classifiers include support vector machines, neural networks, decision trees, etc.

[0158] The classifier outputs the defect type and characteristic parameters, which are then combined with pulse excitation analysis to determine the defect location.

[0159] The defect detection module performs sub-millimeter-level defect detection and localization by combining pulse excitation and defect fingerprint database comparison.

[0160] The reflection and transmission characteristics of a copper wire are obtained by injecting a broadband high-frequency pulse signal to excite its response. The specific steps are as follows:

[0161] A pulse signal with specified parameters is generated using a signal generator. The injected signal covers multiple frequency bands and propagates into the interior of the copper wire, interacting with the material.

[0162] Record the time delay and amplitude changes of the reflected signal; the changes in the reflected signal reflect the abnormality of the internal structure of the copper wire.

[0163] The location of the defect is calculated by using the relationship between the arrival time of the reflected signal and the signal propagation speed, where the propagation speed is a known constant and the time delay is proportional to the distance.

[0164] By increasing the signal sampling rate, the time resolution is improved, enabling nanosecond-level time measurement and achieving sub-millimeter-level positioning accuracy;

[0165] Establish a defect fingerprint database to store typical signal characteristics of different types of defects. The fingerprint database includes characteristic data of current and impedance perturbation signals, reflection waveforms under pulse excitation, and time delay and amplitude changes corresponding to defects.

[0166] During real-time detection, the following comparison operations are performed:

[0167] Calculate the cosine similarity between the current signal and the signals in the fingerprint database to find the best matching defect type;

[0168] Set a similarity threshold. If the similarity between the current signal and a certain type of defect is higher than the threshold, it is determined to be that type of defect.

[0169] The defect detection module extracts high-resolution current and impedance perturbation signals based on standardized electrical parameters, and combines wavelet transform and feature engineering to achieve multi-scale signal reconstruction. It uses pattern recognition technology to classify defects and introduces principal component analysis to reduce feature redundancy and improve classification efficiency. Through broadband pulse excitation and reflection signal analysis, combined with defect fingerprint database comparison, it achieves high-precision positioning and type identification of sub-millimeter defects. The overall solution has the characteristics of high sensitivity, accurate positioning, and strong adaptability, and is suitable for detecting internal defects in copper wires under complex working conditions.

[0170] Example 2: The technical solution of this embodiment of the invention differs from that of Example 1 in that:

[0171] like Figure 1 As shown, the mechanical stability assessment module establishes a thermo-mechanical response model of the copper wire under energization based on the preprocessed key electrical parameters and temperature distribution data, and uses the finite element method to simulate the evolution of internal stress and structural deformation characteristics.

[0172] The process of establishing a thermo-mechanical response model of a copper wire under energization in the mechanical stability assessment module includes:

[0173] The key electrical parameters and temperature distribution data after preprocessing are obtained. The temperature distribution data is the temperature field T(x,t), which is the temperature of the copper wire at different locations and time points.

[0174] Copper wire generates Joule heat during the energization process, causing non-uniform temperature rise, which in turn leads to thermal expansion and stress evolution. The thermo-mechanical response model consists of three basic processes: heat conduction process, thermal strain-stress relationship, and mechanical equilibrium conditions.

[0175] Heat conduction process: Temperature diffuses within a material through heat conduction. This process involves material density, specific heat capacity, thermal conductivity, and the Joule heat source term, which is calculated from current and resistance.

[0176] Relationship between thermal strain and stress: Temperature rise causes thermal expansion, which generates thermal strain. Thermal strain is the difference between the current temperature and the reference temperature multiplied by the coefficient of thermal expansion. Under small deformation linear elastic conditions, thermal stress is the Young's modulus of the material multiplied by the difference between the total strain and the thermal strain.

[0177] Mechanical equilibrium condition: The stress state inside the material must satisfy the mechanical equilibrium condition, which is represented by the sum of the divergence of the stress tensor and the volume force density being equal to zero. This relationship needs to be solved in conjunction with the boundary conditions to obtain the deformation distribution of the material.

[0178] The mechanical stability assessment module uses the finite element method to simulate the evolution of internal stress and structural deformation characteristics, including:

[0179] The finite element method is a numerical method for solving systems of partial differential equations. The entire simulation process includes modeling, mesh generation, boundary condition setting, time integration scheme, and coupled solution.

[0180] Geometric modeling and mesh generation: A three-dimensional model is established based on the actual geometry of the copper wire and divided into tetrahedral or hexahedral elements. Hot spots are locally meshed to improve calculation accuracy.

[0181] Initial and boundary conditions are set as follows: the initial temperature is set to ambient temperature, the initial stress and strain are zero, and the boundary conditions include:

[0182] Thermal boundary conditions: Set convective heat transfer or adiabatic boundary conditions;

[0183] Force boundary conditions: Set fixed ends, free ends, or other constraints;

[0184] Heat source application: Distribute the heat source calculated from the current and resistance to each unit;

[0185] Thermo-mechanical coupling calculation process: A multiphysics coupling method is used for simultaneous solution. The following operations are performed at each time step:

[0186] Calculate the heat source based on current and resistance;

[0187] Solve the heat conduction equation to update the temperature;

[0188] Calculate the thermal strain based on temperature, and then calculate the stress.

[0189] Substitute the stress into the equilibrium equations to solve for the displacement field;

[0190] Update the deformed geometric and thermal properties, and proceed to the next time step;

[0191] Repeat the above process until both temperature and stress converge;

[0192] Time integration and solution method: Implicit time integration is used for time progression, and the linear equations are solved using the sparse matrix iterative solution method;

[0193] The mechanical stability assessment module analyzes and evaluates the finite element simulation results, including the following processes:

[0194] After the simulation is completed, a comprehensive analysis is performed on the temperature field, stress field, and deformation field.

[0195] Temperature stress analysis: Comparing the thermal stress distribution with the material's yield strength to determine if there are regions that exceed the limit;

[0196] Deformation analysis: Calculate the maximum deformation and deformation gradient of the copper wire throughout the entire process, and analyze whether there is stress concentration or structural interference;

[0197] Thermal fatigue analysis: Under periodic energization, stress cyclic changes are analyzed, and thermal fatigue life is estimated using the Goodman or Coffin-Manson method;

[0198] Stability criterion establishment: A stability criterion is established based on the maximum temperature gradient, maximum equivalent stress, and maximum displacement to determine whether the copper wire is in a stable state;

[0199] The mechanical stability assessment module integrates preprocessed electrical parameters and temperature field data to establish a high-precision thermo-mechanical coupling model. It uses the finite element method to achieve dynamic simulation of internal stress evolution and structural deformation. It adopts implicit time integration and multi-physics coupling solution strategies to ensure computational stability and convergence. It accurately captures thermal expansion, stress concentration, and deformation trends by combining material constitutive relations and boundary conditions. The post-processing stage comprehensively analyzes temperature stress, maximum deformation, and fatigue damage to establish quantitative stability criteria. It supports the scientific assessment and early warning of the safety status of copper wire structures and has the advantages of precise modeling, reliable calculation, and comprehensive assessment.

[0200] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and its improved concept, should be covered within the scope of protection of the present invention.

Claims

1. A current-conductivity testing and analysis system based on copper wire processing, applied to a current-conductivity testing management platform, characterized in that, include: The electrical data acquisition module is used to collect key electrical parameters of copper wires under different energizing conditions in real time and to perform preprocessing operations on the collected key electrical parameters. The thermal effect analysis module constructs an electro-thermal coupling model of the copper wire during energization based on preprocessed key electrical parameters, and uses a physically constrained neural network to simulate the temperature distribution. The process by which the thermal effect analysis module constructs the electro-thermal coupling model of the copper wire during energization includes: Obtain the key electrical parameters after preprocessing, including current, voltage, power factor and impedance; The electro-thermal coupling model consists of two sub-models: an electrical model and a heat conduction model; The electrical model is based on Ohm's law and Joule's law, the heat conduction model is based on the heat diffusion equation, and the resistance of the copper wire changes with temperature. The electro-thermal coupling model is solved using a numerical method, and the specific steps are as follows: S1: Initialize the geometry, boundary conditions, and initial temperature distribution of the copper wire; S2: Input the current and resistance values ​​at the current moment to calculate the heat generation power; S3: Substitute the heat generation power as a heat source term into the heat conduction equation, and discretize the heat conduction equation using the finite difference method or the finite element method. S4: Solve the temperature field, update the temperature values ​​of each node of the copper wire, and recalculate the resistance based on the new temperature values; S5: Return to step S2 and proceed to the next time step iteration; The defect detection module extracts current and resistance perturbation signals based on preprocessed key electrical parameters, and uses multi-scale reconstruction and pattern recognition technology, combined with pulse excitation and defect fingerprint database comparison, to complete the detection and location of sub-millimeter level internal defects. The process by which the defect detection module extracts current and resistance perturbation signals includes: Obtain the normalized current, voltage, power factor, and impedance time series of the preprocessed output; Structural changes in an electrical system can cause local disturbances in current and impedance. The current perturbation signal is obtained from the time derivative of the current. ; The impedance perturbation signal is obtained from the impedance time derivative: ; The extracted perturbation signal is processed by multi-level wavelet transform or local least squares method. Wavelet transform is used to reconstruct the signal at multiple scales. The mechanical stability assessment module establishes a thermo-mechanical response model of copper wire under energization based on preprocessed key electrical parameters and temperature distribution data, and uses the finite element method to simulate the evolution of internal stress and structural deformation characteristics. The process by which the mechanical stability assessment module establishes a thermo-mechanical response model of the copper wire under energization includes: The key electrical parameters and temperature distribution data after preprocessing are obtained. The temperature distribution data is the temperature field T(x,t), which is the temperature of the copper wire at different locations and time points. Copper wire generates Joule heat during the energization process, causing non-uniform temperature rise, which in turn leads to thermal expansion and stress evolution. The thermo-mechanical response model consists of three basic processes: heat conduction process, thermal strain-stress relationship, and mechanical equilibrium conditions. Heat conduction process: Temperature diffuses within a material through heat conduction. This process involves material density, specific heat capacity, thermal conductivity, and the Joule heat source term, which is calculated from current and resistance. Relationship between thermal strain and stress: Temperature rise causes thermal expansion, which generates thermal strain. Thermal strain is the difference between the current temperature and the reference temperature multiplied by the coefficient of thermal expansion. Under small deformation linear elastic conditions, thermal stress is the Young's modulus of the material multiplied by the difference between the total strain and the thermal strain. Mechanical equilibrium condition: The stress state inside the material must satisfy the mechanical equilibrium condition, which is represented by the sum of the divergence of the stress tensor and the volume force density being equal to zero. This relationship needs to be solved in conjunction with the boundary conditions to obtain the deformation distribution of the material.

2. The current-conductivity testing and analysis system based on copper wire processing according to claim 1, characterized in that, The thermal effect analysis module uses a physically constrained neural network to simulate the temperature distribution process, which includes: The physical constraint neural network adopts a feedforward neural network structure, which includes an input layer, several hidden layers, and an output layer; The input layer includes the following input variables: time variable t, spatial coordinate x, current value I(t), voltage value V(t), power factor PF(t), and impedance value Z(t); Each hidden layer consists of multiple neurons, which transmit information through weighted connections. By using weighted connections and activation function operations, the potential relationship between input variables and copper wire temperature is explored. The output layer is a temperature value T(x, t), which represents the temperature of the copper wire at a specified time and spatial location; The loss function consists of two parts: data terms and physical terms. The data item is used to calculate the squared error between the network output and the reference data; The total loss function is constructed by weighted summation of data terms and physical terms, where each term is multiplied by a fixed value set before training begins as its corresponding weighting coefficient, and then the terms are summed. The training steps for a physically constrained neural network are as follows: T1: Initialize neural network parameters; T2: Input the time, space, and electrical parameters from the training samples; T3: Forward propagation calculation output temperature; T4: Calculate the loss function based on the current output; T5: Backpropagation adjusts network parameters; T6: Repeat steps S3-S5 until the loss function converges.

3. The current-conductivity testing and analysis system based on copper wire processing according to claim 1, characterized in that, The defect detection module uses multi-scale reconstruction and pattern recognition technology to analyze perturbation signals, including the following process: The perturbation signal contains multiple frequency components. Discrete wavelet transform is used for multi-scale reconstruction. The specific steps are as follows: Wavelet decomposition was performed on the current perturbation signal and the impedance perturbation signal, respectively. The signal of a specific frequency band is reconstructed as needed, with the low-frequency part used to extract trend features and the high-frequency part used to capture abrupt changes. The root mean square value, average amplitude, and peak factor features are extracted from the reconstructed signal. Perform a Fourier transform on the signal to obtain a spectrum, and extract the spectral peaks, power spectral density, and energy concentration band features; Through multi-scale reconstruction and feature extraction, the perturbation signal is comprehensively characterized. Based on the extracted signal features, pattern recognition technology is used to classify defects. The implementation steps are as follows: Extract time-domain features from the reconstructed signal: peak factor, root mean square value, variance, peak-to-peak interval; extract frequency-domain features: spectral peak value, power spectral density, and main frequency band energy. Use principal component analysis or linear discriminant analysis to reduce the dimensionality of the feature space; A supervised learning algorithm is used to train the classifier, with the input being the feature vector and the output being the defect category; The classifier outputs the defect type and characteristic parameters, which are then combined with pulse excitation analysis to determine the defect location.

4. The current-conductivity testing and analysis system based on copper wire processing according to claim 3, characterized in that, The defect detection module performs sub-millimeter-level defect detection and localization by combining pulse excitation and defect fingerprint database comparison, including the following process: The reflection and transmission characteristics of a copper wire are obtained by injecting a broadband high-frequency pulse signal to excite its response. The specific steps are as follows: A pulse signal with specified parameters is generated using a signal generator. The injected signal covers multiple frequency bands and propagates into the interior of the copper wire, interacting with the material. Record the time delay and amplitude changes of the reflected signal; The location of the defect is calculated using the relationship between the arrival time of the reflected signal and the speed of signal propagation. Improve temporal resolution by increasing the signal sampling rate; Establish a defect fingerprint database to store typical signal characteristics of different types of defects. The fingerprint database includes characteristic data of current and impedance perturbation signals, reflection waveforms under pulse excitation, and time delay and amplitude changes corresponding to defects. During real-time detection, the following comparison operations are performed: Calculate the cosine similarity between the current signal and the signals in the fingerprint database to find the best matching defect type; A similarity threshold is set. If the similarity between the current signal and a certain type of defect is higher than the threshold, it is determined to be that type of defect.

5. The current-conductivity testing and analysis system based on copper wire processing according to claim 1, characterized in that, The mechanical stability assessment module uses the finite element method to simulate the evolution of internal stress and structural deformation characteristics, including the following processes: The finite element method is a numerical method for solving systems of partial differential equations. The entire simulation process includes modeling, mesh generation, boundary condition setting, time integration scheme, and coupled solution. Geometric modeling and mesh generation: A three-dimensional model is established based on the actual geometry of the copper wire, and then divided into tetrahedral or hexahedral elements; Initial and boundary conditions are set as follows: the initial temperature is set to ambient temperature, the initial stress and strain are zero, and the boundary conditions include: Thermal boundary conditions: Set convective heat transfer or adiabatic boundary conditions; Force boundary conditions: Set fixed ends, free ends, or other constraints; Heat source application: Distribute the heat source calculated from the current and resistance to each unit; Thermo-mechanical coupling calculation process: A multiphysics coupling method is used for simultaneous solution. The following operations are performed at each time step: Calculate the heat source based on current and resistance; Solve the heat conduction equation to update the temperature; Calculate the thermal strain based on temperature, and then calculate the stress. Substitute the stress into the equilibrium equations to solve for the displacement field; Update the deformed geometric and thermal properties, and proceed to the next time step; Repeat the above process until both temperature and stress converge; Time integration and solution method: Implicit time integration is used for time progression, and the linear equations are solved using the sparse matrix iterative solution method.

6. The current-conductivity testing and analysis system based on copper wire processing according to claim 5, characterized in that, The mechanical stability assessment module analyzes and evaluates the finite element simulation results, including the following processes: After the simulation is completed, a comprehensive analysis is performed on the temperature field, stress field, and deformation field. Temperature stress analysis: Comparing the thermal stress distribution with the material's yield strength to determine if there are regions that exceed the limit; Deformation analysis: Calculate the maximum deformation and deformation gradient of the copper wire throughout the entire process, and analyze whether there is stress concentration or structural interference; Thermal fatigue analysis: Under periodic energization, stress cyclic changes are analyzed, and thermal fatigue life is estimated using the Goodman or Coffin-Manson method; Stability criterion establishment: A stability criterion is established based on the maximum temperature gradient, maximum equivalent stress, and maximum displacement to determine whether the copper wire is in a stable state.

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