Power-on test analysis system based on copper wire processing

Abstract

The invention relates to the technical field of copper wire machining quality detection, in particular to a power-on test analysis system based on copper wire machining, and aims to solve the problems that in the prior art, high-precision dynamic prediction of a copper wire temperature field cannot be achieved, physical modeling and deep learning cannot be fused, and the power-on test accuracy is poor. And a reliable temperature basis cannot be provided for structure evaluation and life prediction. Physical modeling and deep learning are fused through the heat effect analysis module, high-precision dynamic prediction of the copper wire temperature field is achieved through electro-thermal coupling simulation and the physical constraint neural network, multivariable input and strong nonlinear modeling capacity are achieved, and a reliable temperature basis is provided for structure evaluation and service life prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of copper wire processing quality detection, and more particularly to an electric test and analysis system based on copper wire processing. Background Art

[0002] Copper wire is a core material in the fields of power transmission, electronic equipment and communications, and its quality directly determines product performance and safety. However, existing testing technologies have three major flaws: conductivity testing relies on manual operation, resulting in low efficiency and easy misjudgment; mechanical strength testing focuses only on a single tensile index, making it difficult to comprehensively evaluate comprehensive mechanical properties; surface defect recognition capabilities are weak and minor flaws are easily missed; in addition, each testing link operates independently and data cannot be analyzed in a linked manner, making it difficult to support process optimization and closed-loop quality management, seriously restricting the industry's production efficiency and product quality improvement.

[0003] The patent application with reference publication number CN108469546A discloses a wire impedance testing method and system, comprising: obtaining wire parameters of the wire to be tested, voltages across the wire to be tested, and voltages across a test resistor connected in series with the wire to be tested after receiving a test instruction; calculating the connector impedance of the wire to be tested based on the obtained wire parameters of the wire to be tested, voltages across the wire to be tested, and voltages across the test resistor; comparing the calculated connector impedance with a standard connector impedance, and generating a qualified test result for the wire to be tested if the connector impedance does not exceed the standard connector impedance; otherwise, generating a failed test result for the wire to be tested. The present application can accurately calculate the connector impedance of the wire to be tested, and compare the calculated connector impedance of the wire to be tested with the industry standard connector impedance to determine whether the wire to be tested is qualified. The error range is small, the accuracy is high, and the production process is simplified, production efficiency is improved, and the operator is no longer required to have high professional knowledge. It is suitable for batch testing.

[0004] However, the above-mentioned reference patent calculates the connector impedance by collecting wire parameters and voltage data, and compares it with the standard value to determine its eligibility, thereby achieving high-precision automated detection with small error, high efficiency, and no need for professional operation. It is suitable for batch testing, but it cannot achieve high-precision dynamic prediction of the copper wire temperature field, cannot integrate physical modeling and deep learning, and cannot provide a reliable temperature basis for structural evaluation and life prediction; at the same time, it cannot use the finite element method to simulate structural deformation and stress evolution, cannot combine implicit solution with multi-physical field collaborative analysis, and cannot achieve safety warning and reliability evaluation of copper wire structures.

[0005] To this end, we propose an electric test and analysis system based on copper wire processing to address the above problems. Summary of the Invention

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

[0007] The purpose of the present invention is achieved through the following technical solutions:

[0008] A power-on test analysis system based on copper wire processing, applied to a power-on test management platform, includes:

[0009] The electrical data acquisition module is used to collect the key electrical parameters of the copper wire under different power-on conditions in real time and perform pre-processing operations on the collected key electrical parameters;

[0010] The thermal effect analysis module builds an electrical-thermal coupling model of the copper wire during the power-on process based on pre-processed key electrical parameters and uses a physical constraint neural network to simulate the temperature distribution;

[0011] The defect detection module extracts current and resistance perturbation signals based on preprocessed key electrical parameters. It uses multi-scale reconstruction and pattern recognition technology, combined with pulse excitation and defect fingerprint library comparison, to detect and locate submillimeter-level internal defects.

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

[0013] As a preferred embodiment of the present invention, the process of the thermal effect analysis module constructing the electric-thermal coupling model during the copper wire power-on process includes:

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

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

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

[0017] The numerical method is used to solve the electro-thermal coupling model. 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 value at the current moment to calculate the heat generation power;

[0020] S3: Substitute the heat generation power as the 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 value of each node of the copper wire, and recalculate the resistance according to the new temperature value;

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

[0023] As a preferred embodiment of the present invention, the process of simulating temperature distribution using a physical constraint 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. Through weighted connections and activation function operations, the potential relationship between input variables and copper wire temperature is mined;

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

[0028] The loss function is divided into two parts: data term and physical term;

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

[0030] The total loss function is composed of data terms and physical terms through weighted summation, where each term is multiplied by a fixed value set before training begins as its corresponding weight coefficient, and then added together;

[0031] The physical constraint neural network training steps are as follows:

[0032] T1: Initialize neural network parameters;

[0033] T2: input the time, space and electrical parameters of the training sample;

[0034] T3: forward propagation to calculate output temperature;

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

[0036] T5: Back propagation to adjust network parameters;

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

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

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

[0040] Structural changes in electrical systems 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 time derivative of the impedance:

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

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

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

[0045] Perform wavelet decomposition on the current perturbation signal and the impedance perturbation signal respectively;

[0046] Reconstruct the signal of a specific frequency band as needed, the low-frequency part is used to extract trend features, and the high-frequency part is used to capture mutation features;

[0047] Extract the root mean square value, average amplitude and peak factor characteristics of the reconstructed signal;

[0048] Perform Fourier transform on the signal to obtain the spectrum diagram, extract the spectrum peak, power spectrum density and energy concentration frequency band characteristics;

[0049] Through multi-scale reconstruction and feature extraction, the perturbation signal is fully 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: crest factor, root mean square value, variance, peak-to-peak interval; extract frequency domain features: spectrum peak, power spectrum density, main frequency band energy;

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

[0052] The classifier is trained using a supervised learning algorithm, with the input being the feature vector and the output being the defect category;

[0053] The classifier outputs the defect type and characteristic parameters, and the defect location is determined by combining pulse excitation analysis.

[0054] As a preferred embodiment of the present invention, the process of the defect detection module combining pulse excitation and defect fingerprint library comparison to perform submillimeter level defect detection and positioning includes:

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

[0056] A signal generator is used to generate a pulse signal with specified parameters. The injected signal covers multiple frequency bands. The signal propagates into the copper wire and interacts with the material.

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

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

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

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

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

[0062] Calculate the cosine similarity between the current signal and the signal in the fingerprint library to find the most matching defect type;

[0063] 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.

[0064] As a preferred embodiment of the present invention, the process of the mechanical stability assessment module establishing a thermal-mechanical response model of the copper wire under the action of electricity includes:

[0065] Obtain the key electrical parameters and temperature distribution data after preprocessing. The temperature distribution data is the temperature field T(x, t), that is, the temperature of the copper wire at different positions and time points;

[0066] When copper wire is energized, it generates Joule heat, causing non-uniform temperature rise, which in turn leads to thermal expansion and stress evolution. The thermal-mechanical response model consists of three basic processes: heat conduction process, thermal strain-stress relationship, and mechanical equilibrium condition.

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

[0068] Relationship between thermal strain and stress: Temperature increase causes thermal expansion, resulting in thermal strain. Thermal strain is the difference between the current temperature and the reference temperature multiplied by the thermal expansion coefficient. Under small deformation linear elastic conditions, thermal stress is the material's Young's modulus 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 jointly with the boundary conditions to obtain the deformation distribution of the material.

[0070] As a preferred embodiment of the present invention, the process of simulating the internal stress evolution and structural deformation characteristics using the finite element method in the mechanical stability assessment module includes:

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

[0072] Geometric modeling and meshing: Build a 3D model based on the actual geometric shape of the copper wire and divide it into tetrahedral or hexahedral units;

[0073] Initial and boundary conditions are set: 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 boundaries;

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

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

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

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

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

[0080] Calculate thermal strain based on temperature and then calculate stress;

[0081] Substitute the stress into the equilibrium equation and solve for the displacement field;

[0082] Update the deformed geometry and thermal properties and enter the next time step;

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

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

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

[0086] After the simulation is completed, the temperature field, stress field and deformation field are comprehensively analyzed;

[0087] Thermal stress analysis: Compare the thermal stress distribution with the material yield strength to determine whether there are areas that exceed the limit;

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

[0089] Thermal fatigue analysis: Under periodic power-on conditions, analyze stress cycle changes and estimate thermal fatigue life using the Goodman or Coffin-Manson method;

[0090] Establishment of stability criterion: 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 the present invention are:

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

[0093] (2) In the present invention, the mechanical stability assessment module constructs a thermal-mechanical coupling model based on electrical and temperature data, uses the finite element method to simulate structural deformation and stress evolution, combines implicit solution with multi-physics field collaborative analysis to ensure calculation stability, evaluates thermal stress, deformation and fatigue damage through post-processing, establishes stability criteria, and realizes safety warning and reliability assessment of copper wire structures. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0095] Figure 2 A flowchart of the steps for solving the electro-thermal coupling model in the present invention;

[0096] Figure 3Flowchart of the training steps of the physical constraint neural network in the present invention. DETAILED DESCRIPTION

[0097] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making creative work shall fall within the scope of protection of the present invention.

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

[0099] The electrical data acquisition module is used to collect the key electrical parameters of the copper wire in real time under different power-on conditions (achieved through a multi-channel electrical sensor array) and perform preprocessing operations on the collected key electrical parameters. The preprocessing operations include data cleaning, calibration, anomaly detection, synchronization processing and data standardization;

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

[0101] The thermal effect analysis module builds an electrical-thermal coupling model of the copper wire during the power-on process based on pre-processed key electrical parameters and uses a physical constraint neural network to simulate the temperature distribution;

[0102] The thermal effect analysis module constructs an electro-thermal coupling model of the copper wire during power flow, including the following steps:

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

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

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

[0106] Where P represents the heat generation power 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, which is used to describe the temperature change inside the copper wire with time and space:

[0108] Where T represents the current temperature, α represents the thermal diffusivity, represents the spatial second derivative of temperature, ρ represents the copper wire density, and c represents the specific heat capacity;

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

[0110] R(T)=R0[1+β(T-T0)];

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

[0112] The numerical method is used to solve the electro-thermal coupling model. 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 value at the current moment to calculate the heat generation power;

[0115] S3: Substitute the heat generation power as the 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 value of each node of the copper wire, and recalculate the resistance according to the new temperature value;

[0117] S5: Return to step S2 and enter the next time step iteration. Through the above process, the temperature field of the copper wire during the power-on process is simulated step by step.

[0118] The thermal effect analysis module uses a physical constraint neural network to simulate temperature distribution, including the following process:

[0119] Physically constrained neural networks are a method for embedding partial differential equations into deep learning models. This method introduces the governing equations as hard constraints during neural network training, making the network output directly conform 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 and reaches the output layer after being processed by several hidden layers.

[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. The potential relationship between the input variables and the temperature of the copper wire is mined through weighted connections and activation function operations. The activation function uses a nonlinear function.

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

[0124] The loss function is divided into two parts: data term and physical term;

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

[0126]

[0127] Where T yc (x i , t i ) is the temperature of the network output, T ck (x i ,t i ) is the reference data, N is the number of samples;

[0128] The physics term is used to calculate how much the network output deviates from the heat conduction equation:

[0129]

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

[0131] The total loss function is composed of data terms and physical terms through weighted summation, where each term is multiplied by a fixed value set before training begins as its corresponding weight coefficient, and then added together;

[0132] The physical constraint neural network training steps are as follows:

[0133] T1: Initialize neural network parameters;

[0134] T2: input the time, space and electrical parameters of the training sample;

[0135] T3: forward propagation to calculate output temperature;

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

[0137] T5: Back propagation to adjust network parameters;

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

[0139] During the entire training process, the network parameters are updated according to the gradient direction so that the output results conform to the form of the reference data and the heat conduction equation;

[0140] The thermal effect analysis module combines the advantages of numerical modeling based on physical laws and data fitting of deep learning, and can accurately reflect the temperature evolution process of copper wire under different power-on conditions; through the iterative solution of electrical models and heat conduction models, it realizes the dynamic simulation of heat generation and heat transfer processes; at the same time, it adopts physical constraint neural networks and introduces heat conduction equations as hard constraints in modeling, so that the network output strictly conforms to physical laws, improving the reliability and generalization ability of the prediction results; the module supports multi-variable input and has good nonlinear modeling capabilities. It is suitable for high-precision estimation of temperature distribution under complex boundary conditions, providing a solid foundation for subsequent structural stability assessment and life prediction.

[0141] The defect detection module extracts current and resistance perturbation signals based on preprocessed key electrical parameters. It uses multi-scale reconstruction and pattern recognition technology, combined with pulse excitation and defect fingerprint library comparison, to detect and locate submillimeter-level internal defects.

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

[0143] Obtain the standardized current, voltage, power factor, and impedance time series output after preprocessing. The resistance change information can be obtained by jointly analyzing the impedance signal and the current signal.

[0144] Structural changes in electrical systems 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 time derivative of the impedance:

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

[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 the perturbation signal, including the following process:

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

[0150] The current perturbation signal and the impedance perturbation signal are respectively subjected to wavelet decomposition, and the decomposition results include low-frequency approximate coefficients and high-frequency detail coefficients. The wavelet basis function is a fixed type, such as Haar wavelet or Daubechies wavelet;

[0151] Reconstruct the signal of a specific frequency band as needed, the low-frequency part is used to extract trend features, and the high-frequency part is used to capture mutation features;

[0152] Extract the root mean square value, average amplitude and peak factor characteristics of the reconstructed signal;

[0153] Perform Fourier transform on the signal to obtain the spectrum diagram, extract the spectrum peak, power spectrum density and energy concentration frequency band characteristics;

[0154] Through multi-scale reconstruction and feature extraction, the perturbation signal is fully 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: crest factor, root mean square value, variance, peak-to-peak interval; extract frequency domain features: spectrum peak, power spectrum density, main frequency band energy;

[0156] Use principal component analysis or linear discriminant analysis to reduce the dimension of the feature space, retain the eigenvectors with high discrimination, and reduce redundant information and computational complexity;

[0157] Use supervised learning algorithms to train classifiers, with feature vectors as input and defect categories as output. Optional classifiers include support vector machines, neural networks, and decision trees.

[0158] The classifier outputs the defect type and characteristic parameters, and combines them with pulse excitation analysis to determine the defect location;

[0159] The defect detection module combines pulse excitation and defect fingerprint library comparison to perform submillimeter-level defect detection and location. The process includes:

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

[0161] A signal generator is used to generate a pulse signal with specified parameters. The injected signal covers multiple frequency bands. The signal propagates into the copper wire and interacts with the material.

[0162] Record the time delay and amplitude change of the reflected signal. The change of the reflected signal reflects the abnormality of the internal structure of the copper wire;

[0163] The defect location is calculated using the relationship between the arrival time of the reflected signal and the signal propagation speed. 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, nanosecond time measurement is achieved, and the positioning accuracy reaches the sub-millimeter level;

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

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

[0167] Calculate the cosine similarity between the current signal and the signal in the fingerprint library to find the most 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, combines wavelet transform and feature engineering to achieve multi-scale signal reconstruction, 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 library comparison, high-precision positioning and type identification of submillimeter defects are achieved; the overall solution has the characteristics of high sensitivity, accurate positioning, and strong adaptability, and is suitable for internal defect detection of copper wires under complex working conditions.

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

[0171] like Figure 1 As shown in the figure, the mechanical stability assessment module establishes a thermal-mechanical response model of the copper wire under the action of electricity based on the pre-processed key electrical parameters and temperature distribution data, and uses the finite element method to simulate the internal stress evolution and structural deformation characteristics;

[0172] The process of establishing the thermal-mechanical response model of copper wire under the action of electricity in the mechanical stability assessment module includes:

[0173] Obtain the key electrical parameters and temperature distribution data after preprocessing. The temperature distribution data is the temperature field T(x, t), that is, the temperature of the copper wire at different positions and time points;

[0174] When copper wire is energized, it generates Joule heat, causing non-uniform temperature rise, which in turn leads to thermal expansion and stress evolution. The thermal-mechanical response model consists of three basic processes: heat conduction process, thermal strain-stress relationship, and mechanical equilibrium condition.

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

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

[0177] Mechanical equilibrium condition: The stress state within 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 must 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 internal stress evolution and structural deformation characteristics. The process includes:

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

[0180] Geometric modeling and meshing: A three-dimensional model is built based on the actual geometric shape of the copper wire and divided into tetrahedral or hexahedral units. Hot spots are locally encrypted to improve calculation accuracy.

[0181] Initial and boundary conditions are set: 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 boundaries;

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

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

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

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

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

[0188] Calculate thermal strain based on temperature and then calculate stress;

[0189] Substitute the stress into the equilibrium equation and solve for the displacement field;

[0190] Update the deformed geometry and thermal properties and enter the next time step;

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

[0192] Time integration and solution method: implicit time integration method is used for time advancement, and sparse matrix iterative solution method is used to solve the linear equations;

[0193] The process of analyzing and evaluating the finite element simulation results in the mechanical stability assessment module includes:

[0194] After the simulation is completed, the temperature field, stress field and deformation field are comprehensively analyzed;

[0195] Thermal stress analysis: Compare the thermal stress distribution with the material yield strength to determine whether there are areas that exceed the limit;

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

[0197] Thermal fatigue analysis: Under periodic power-on conditions, analyze stress cycle changes and estimate thermal fatigue life using the Goodman or Coffin-Manson method;

[0198] Establishment of stability criterion: Establishment of stability criterion based on 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 pre-processed electrical parameters and temperature field data to establish a high-precision thermal-mechanical coupling model, and realizes dynamic simulation of internal stress evolution and structural deformation through the finite element method; it adopts implicit time integration and multi-physics field coupling solution strategy to ensure computational stability and convergence; it combines material constitutive relations and boundary conditions to accurately capture thermal expansion, stress concentration and deformation trends; the post-processing link comprehensively analyzes temperature stress, maximum deformation and fatigue damage, establishes quantitative stability criteria, and supports scientific assessment and early warning of the safety status of copper wire structures, with the advantages of precise modeling, reliable calculation and comprehensive assessment.

[0200] The above are only preferred specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes based on the technical solutions and improved concepts of the present invention within the technical scope disclosed by the present invention, and they should be covered by the scope of protection of the present invention.

Claims

1. A power-on test analysis system based on copper wire processing, applied to a power-on test management platform, characterized in that: include: The electrical data acquisition module is used to collect the key electrical parameters of the copper wire under different power-on conditions in real time and perform pre-processing operations on the collected key electrical parameters; The thermal effect analysis module builds an electrical-thermal coupling model of the copper wire during the power-on process based on pre-processed key electrical parameters and uses a physical constraint neural network to simulate the temperature distribution; The defect detection module extracts current and resistance perturbation signals based on preprocessed key electrical parameters. It uses multi-scale reconstruction and pattern recognition technology, combined with pulse excitation and defect fingerprint library comparison, to detect and locate submillimeter-level internal defects. The mechanical stability assessment module establishes a thermal-mechanical response model of copper wire under electrical action based on pre-processed key electrical parameters and temperature distribution data, and uses the finite element method to simulate the internal stress evolution and structural deformation characteristics.

2. The power-on test and analysis system based on copper wire processing according to claim 1, characterized in that: The process of constructing the electric-thermal coupling model of the copper wire during the power-on process by the thermal effect analysis module includes: Obtain key electrical parameters after preprocessing, including current, voltage, power factor and impedance; The electro-thermal coupling model consists of two sub-models: the electrical model and the thermal conduction model; The electrical model is based on Ohm's law and Joule's law, and the heat conduction model is based on the heat diffusion equation. The resistance of the copper wire changes with temperature. The numerical method is used to solve the electro-thermal coupling model. 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 the 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 value of each node of the copper wire, and recalculate the resistance according to the new temperature value; S5: Return to step S2 and enter the next time step iteration.

3. The power-on test and analysis system based on copper wire processing according to claim 2, characterized in that: The process of simulating temperature distribution using a physical constraint neural network in the thermal effect analysis module 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. Through weighted connections and activation function operations, the potential relationship between input variables and copper wire temperature is mined; The output layer is the temperature value T(x, t), which represents the temperature of the copper wire at the specified time and spatial position; The loss function is divided into two parts: data term and physical term; The data term is used to calculate the squared error between the network output and the reference data; The total loss function is composed of data terms and physical terms through weighted summation, where each term is multiplied by a fixed value set before training begins as its corresponding weight coefficient, and then added together; The physical constraint neural network training steps are as follows: T1: Initialize neural network parameters; T2: input the time, space and electrical parameters of the training sample; T3: forward propagation to calculate output temperature; T4: Calculate the loss function based on the current output; T5: Back propagation to adjust network parameters; T6: Repeat steps S3-S5 until the loss function converges.

4. The power-on test and analysis system based on copper wire processing according to claim 1, characterized in that: The process of extracting current and resistance perturbation signals by the defect detection module includes: Obtain the normalized current, voltage, power factor and impedance time series output after preprocessing; Structural changes in electrical systems 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 time derivative of the impedance: The extracted perturbation signal is processed by multi-level wavelet transform or local least square method, and the wavelet transform is used to reconstruct the signal at multiple scales.

5. The power-on test and analysis system based on copper wire processing according to claim 4, characterized in that: The process of analyzing the perturbation signal using multi-scale reconstruction and pattern recognition technology in the defect detection module includes: The perturbation signal contains multiple frequency components, and discrete wavelet transform is used for multi-scale reconstruction. The specific steps are as follows: Perform wavelet decomposition on the current perturbation signal and the impedance perturbation signal respectively; Reconstruct the signal of a specific frequency band as needed, the low-frequency part is used to extract trend features, and the high-frequency part is used to capture mutation features; Extract the root mean square value, average amplitude and peak factor characteristics of the reconstructed signal; Perform Fourier transform on the signal to obtain the spectrum diagram, extract the spectrum peak, power spectrum density and energy concentration frequency band characteristics; Through multi-scale reconstruction and feature extraction, the perturbation signal is fully 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: crest factor, root mean square value, variance, peak-to-peak interval; extract frequency domain features: spectrum peak, power spectrum density, main frequency band energy; Use principal component analysis or linear discriminant analysis to reduce the dimensionality of the feature space; The classifier is trained using a supervised learning algorithm, with the input being the feature vector and the output being the defect category; The classifier outputs the defect type and characteristic parameters, and the defect location is determined by combining pulse excitation analysis.

6. The power-on test and analysis system based on copper wire processing according to claim 5, characterized in that: The process of the defect detection module combining pulse excitation and defect fingerprint library comparison to perform sub-millimeter level defect detection and positioning includes: By injecting a broadband high-frequency pulse signal to stimulate the response of the copper wire, its reflection and transmission characteristics are obtained. The specific steps are as follows: A signal generator is used to generate a pulse signal with specified parameters. The injected signal covers multiple frequency bands. The signal propagates into the copper wire and interacts with the material. Record the time delay and amplitude change of the reflected signal; The defect location is calculated using the relationship between the arrival time of the reflected signal and the signal propagation speed; Improve time resolution by increasing the signal sampling rate; Establish a defect fingerprint library to store typical signal characteristics of different types of defects. The fingerprint library includes characteristic data of current and impedance perturbation signals, reflection waveforms under pulse excitation, and time delay and amplitude changes corresponding to the defects. During real-time detection, the following comparison operations are performed: Calculate the cosine similarity between the current signal and the signal in the fingerprint library to find the most matching defect type; 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.

7. The power-on test and analysis system based on copper wire processing according to claim 1, characterized in that: The process of the mechanical stability assessment module establishing a thermal-mechanical response model of the copper wire under the action of electricity includes: Obtain the key electrical parameters and temperature distribution data after preprocessing. The temperature distribution data is the temperature field T(x, t), that is, the temperature of the copper wire at different positions and time points; When copper wire is energized, it generates Joule heat, causing non-uniform temperature rise, which in turn leads to thermal expansion and stress evolution. The thermal-mechanical response model consists of three basic processes: heat conduction process, thermal strain-stress relationship, and mechanical equilibrium condition. Heat conduction process: Temperature diffuses within the material through heat conduction. This process involves material density, specific heat capacity, thermal conductivity, and Joule heat source term, which is calculated from current and resistance. Relationship between thermal strain and stress: Temperature increase causes thermal expansion, resulting in thermal strain. Thermal strain is the difference between the current temperature and the reference temperature multiplied by the thermal expansion coefficient. Under small deformation linear elastic conditions, thermal stress is the material's Young's modulus 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 jointly with the boundary conditions to obtain the deformation distribution of the material.

8. The power-on test and analysis system based on copper wire processing according to claim 7, characterized in that: The mechanical stability assessment module uses the finite element method to simulate the internal stress evolution and structural deformation characteristics, including the following process: The finite element method is a numerical method for solving partial differential equations. The entire simulation process includes modeling, meshing, boundary condition setting, time integration scheme and coupled solution; Geometric modeling and meshing: Build a 3D model based on the actual geometric shape of the copper wire and divide it into tetrahedral or hexahedral units; Initial and boundary conditions are set: 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 boundaries; Force boundary conditions: set fixed ends, free ends or other constraints; Heat source application: distribute the heat source calculated by current and resistance to each unit; Thermal-mechanical coupling calculation process: A multi-physics coupling method is used for simultaneous solution. The following operations are performed at each time step: Calculate heat source based on current and resistance; Solve the heat conduction equation to update the temperature; Calculate thermal strain based on temperature and then calculate stress; Substitute the stress into the equilibrium equation and solve for the displacement field; Update the deformed geometry and thermal properties and enter the next time step; Repeat the above process until both temperature and stress converge; Time integration and solution method: The implicit time integration method is used for time advancement, and the linear equations are solved using the sparse matrix iterative solution method.

9. The power-on test and analysis system based on copper wire processing according to claim 8, characterized in that: The process of analyzing and evaluating the finite element simulation results by the mechanical stability assessment module includes: After the simulation is completed, the temperature field, stress field and deformation field are comprehensively analyzed; Thermal stress analysis: Compare the thermal stress distribution with the material yield strength to determine whether there are areas that exceed the limit; Deformation analysis: Calculate the maximum deformation and deformation gradient of the copper wire during the entire process, and analyze whether there is stress concentration or structural interference; Thermal fatigue analysis: Under periodic power-on conditions, analyze stress cycle changes and estimate thermal fatigue life using the Goodman or Coffin-Manson method; Establishment of stability criterion: 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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