A mobile phone data cable detection and analysis system

Abstract

The present invention discloses a mobile phone data cable detection and analysis system, which relates to the technical field of data cable detection. Through a test environment construction module, a suitable high-speed transmission test environment is created for the data cable, and equipment such as oscilloscopes and signal analyzers are used to monitor the signal quality; the data monitoring module detects the increase index of the signal transmission error rate and the delay fluctuation index in real time in an environment with or without electromagnetic interference; the data analysis module converts the detected data into a comprehensive feature vector and inputs it into a machine learning model to generate a signal quality index; the comparative analysis module compares the signal quality index with a set threshold to classify the quality of the data cable; the optimization module predicts the degree of abnormality of low-quality data cables, and if the abnormality is serious, optimization measures are taken, effectively improving the anti-interference performance of the data cable, significantly reducing the error rate and delay fluctuation during data transmission, and ensuring reliable communication and data integrity between devices.

Description

Technical Field

[0001] The present invention relates to the technical field of data cable detection, and particularly to a mobile phone data cable detection and analysis system. Background Art

[0002] The detection and analysis of mobile phone data cables refer to the process of testing and evaluating the functions, performance, and quality of mobile phone data cables. Through this detection and analysis, it can be determined whether the data cable can transmit data normally, whether the charging is stable, whether the connection is reliable, and the durability and anti-interference ability of the data cable can be evaluated. These tests usually cover electrical performance tests, signal quality tests, mechanical strength tests, etc., to ensure that the data cable can provide a good user experience in daily use. In addition, the detection and analysis of mobile phone data cables may also involve the evaluation of their design, materials, and manufacturing processes. For example, checking whether the cable insulation layer of the data cable is durable enough and whether the connector meets the standards to avoid damage caused by frequent plugging and unplugging or external forces. Through this comprehensive detection, it can help manufacturers optimize the design and improve the product quality, and also help consumers select more reliable mobile phone data cables.

[0003] The prior art has the following deficiencies:

[0004] In some unqualified connector designs, there may be no effective electromagnetic interference (EMI) shielding, especially during high-speed data transmission. In this case, when the data cable transmits data, the magnetic field in the external environment or the interference generated by the device may affect the signal quality, resulting in data loss, transmission errors, or even system crashes of the device. At the same time, this signal interference may lead to data transmission failures, file corruption, or serious communication errors between electronic devices, affecting the normal operation of the device, and in severe cases, may cause system crashes or the device to require repair. Summary of the Invention

[0005] The purpose of the present invention is to provide a mobile phone data cable detection and analysis system to solve the deficiencies in the background art.

[0006] To achieve the above purpose, the present invention provides the following technical solution: A mobile phone data cable detection and analysis system, including a test environment construction module, a data monitoring module, a data analysis module, a comparative analysis module, and an optimization module;

[0007] The test environment construction module is used to construct a test environment for data transmission of the mobile phone data cable and select test equipment capable of high-speed data transmission and equipped with corresponding interference detection functions. The test equipment includes an oscilloscope, a signal analyzer, and a data transmission test platform;

[0008] A data monitoring module is used to conduct tests separately under an external electromagnetic interference source and a non-interference environment, connect a data line to a computer and perform high-speed data transmission, and use an oscilloscope and a signal analyzer to monitor signal quality data in real time. The signal quality data includes a signal transmission bit error rate increase index and a signal transmission delay fluctuation index;

[0009] A data analysis module is used to convert the signal transmission bit error rate increase index and the signal transmission delay fluctuation index into a comprehensive feature vector, which is used as an input item of a machine learning model. After training the model, a signal quality index of the data line during high-speed data transmission is obtained;

[0010] A comparative analysis module is used to compare and analyze the signal quality index of the data line obtained during high-speed data transmission with a pre-set quality threshold, and classify the data line into a high-quality signal data line and a low-quality signal data line according to the analysis result;

[0011] An optimization module recommends continued use for high-quality signal data lines; for low-quality signal data lines, it further predicts the degree of signal quality abnormality during high-speed data transmission. If the abnormality degree is high, corresponding optimization measures are taken immediately.

[0012] Preferably, in the data monitoring module, the signal quality data includes a signal transmission bit error rate increase index. The method for obtaining the signal transmission bit error rate increase index is as follows:

[0013] In signal transmission, the relationship between the bit error rate BER and the signal-to-noise ratio SNR is: where B is a function term and M is the order of the modulation method. A bit error rate sequence is constructed, and the bit error rate sequence is expressed as {BER i}; where i represents the bit error rate value of the i-th measurement. For the calculation of the bit error rate increase index, the likelihood function is expressed as: where P(BER i |θ) represents the probability of the bit error rate BER i obtained from the i-th experiment under the given parameter θ. For the sake of simplifying the calculation, the logarithmic likelihood function lnL(θ) is used, and the expression:

[0014] The core task of maximum likelihood estimation is to find the optimal parameter θ* to maximize the logarithmic likelihood function: After obtaining the optimal parameter, calculate the bit error rate BER0 under the non-interference condition, calculate the bit error rate BER1 under the interference condition, that is, the bit error rate measured in an environment containing electromagnetic interference and other factors, and calculate the signal transmission bit error rate increase index. The expression is: In the formula, MK is the signal transmission bit error rate increase index.

[0015] Preferably, the signal quality data includes a signal transmission delay fluctuation index, and the acquisition method of the signal transmission delay fluctuation index is as follows:

[0016] Collect the delay data during signal transmission. The delay data refers to the transmission delay L(t) of the signal at each time point t, and the collected delay data is {(t1, L1), (t2, L2),..., (t n , L n )}, where t n is the time point and L n is the signal delay corresponding to the time point; in Gaussian process regression, the kernel function determines the similarity between input data points, and the kernel function is used to measure the similarity of delay data between different time points t i and t j . According to the selected kernel function, calculate the covariance matrix K between delay data points: where k(t n , t n ) is the covariance value calculated by the kernel function. If the signal delay data is affected by noise, a noise term needs to be added, and the expression is: where I is the identity matrix. Through Gaussian process regression, obtain the predicted values of the delay data, predict for new time points to obtain their corresponding delay values, input X = {t1, t2,..., t n}, and output y = {L1, L2,..., L n}; predicted data: prediction point t * ; calculate the covariance matrix: K(X, X) is the covariance matrix between training data, K(X, t * ) is the covariance matrix between training data and prediction points, K(t * , t * ) is the covariance matrix between prediction points, and the predicted mean μ * is: μ * = K(t * , X)K(X, X) -1 y; predicted variance is: Extract the standard deviation of the signal from the predicted variance, and perform weighted average calculation on the standard deviations of delay fluctuations calculated at several time points to obtain the signal transmission delay fluctuation index.

[0017] Preferably, in the data analysis module, the signal transmission error rate increase index and the signal transmission delay fluctuation index are converted into a comprehensive feature vector, and the comprehensive feature vector is used as the input of the machine learning model. The machine learning model takes predicting the signal quality index label of the data line during high-speed data transmission for each group of comprehensive feature vectors as the prediction target, and minimizing the sum of the prediction errors of the signal quality index labels of all data lines during high-speed data transmission as the training target. The machine learning model is trained until the sum of the prediction errors reaches convergence and then the model training is stopped. The signal quality index of the data line during high-speed data transmission is determined according to the model output result, where the machine learning model is a polynomial regression model.

[0018] Preferably, in the comparative analysis module, the signal quality index of the data line obtained during high-speed data transmission is compared and analyzed with a pre-set quality threshold. If the signal quality index of the data line during high-speed data transmission is greater than or equal to the pre-set quality threshold, it indicates that the signal quality of the data line during high-speed data transmission is high and the data transmission is stable. At this time, no warning signal is generated, and the corresponding data line is classified as a high-quality signal data line. If the signal quality index of the data line during high-speed data transmission is less than the pre-set quality threshold, it indicates that the signal quality of the data line during high-speed data transmission is low and the data transmission is unstable. At this time, a warning signal is generated, and the corresponding data line is classified as a low-quality signal data line.

[0019] Preferably, in the optimization module, the signal quality abnormality degree is marked as E, and the calculation expression is: When Q < T, E is a positive value, and the larger the value, the higher the signal quality abnormality degree. Set a high abnormality threshold Ecritical and a low abnormality threshold Elow, and predict and classify the signal quality abnormality degree E. If E < Elow, the abnormality degree is low, and no immediate optimization is required, but close monitoring is needed. If Elow ≤ E < Ecritical, the abnormality degree is medium, and optimization or replacement is carried out. If E ≥ Ecritical, the abnormality degree is high, and immediate optimization measures should be taken.

[0020] Preferably, in the case of a high abnormality degree, optimization measures are taken to improve the signal quality index Q by improving the signal transmission error rate increase index MK and the signal transmission delay fluctuation index LH. A new signal quality index Q′ is obtained through adjustment, and the expression is: Q′ = f(MK′, LH′); where MK′ = MK - ΔMK; MK′ is the optimized error rate increase index, and ΔMK represents the improvement amplitude; LH′ = LH - ΔLH; LH′ is the optimized delay fluctuation index, and ΔLH represents the improvement amplitude; f is the output function of the polynomial regression model. After optimization, calculate the new abnormality degree E′, and the expression is: If E′ < Elow, the optimization measure is effective and the data cable can continue to be used; if E ≥ Ecritical, further optimization or replacement of the data cable is required.

[0021] In the above technical solution, the technical effects and advantages provided by the present invention are as follows:

[0022] 1. Through the collaborative work of the test environment construction, data monitoring, data analysis, comparative analysis, and optimization modules, the present invention uses precise test equipment (such as oscilloscopes and signal analyzers) to monitor signal quality, generates the increase index of signal transmission bit error rate and the delay fluctuation index, and analyzes and predicts the signal quality index through a machine learning model, providing a scientific and reliable evaluation of the quality of the data cable.

[0023] 2. The present invention can not only automatically distinguish high-quality and low-quality data cables, classify and judge the degree of signal abnormality of low-quality data cables, but also take optimization measures in a timely manner when the degree of abnormality is high, and improve the signal quality index by improving the increase index of bit error rate and the delay fluctuation index. After the system is optimized, the optimization effect is re-evaluated to ensure the stability and anti-interference ability of the data cable in actual applications. Through these precise analysis and optimization processes, the present invention significantly improves the signal stability and data integrity of the data cable in high-speed transmission, reduces the risk of equipment failure, and provides a more reliable transmission solution for users. Brief Description of the Drawings

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0025] Figure 1 It is a system module diagram of the present invention. Detailed Embodiments

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0027] Embodiment, please refer to Figure 1 As shown, a mobile phone data cable detection and analysis system in this embodiment includes a test environment construction module, a data monitoring module, a data analysis module, a comparative analysis module, and an optimization module;

[0028] A test environment construction module, which is used to construct a test environment for data transmission of mobile phone data cables, and select test equipment that can perform high-speed data transmission and is equipped with corresponding interference detection functions. The test equipment includes an oscilloscope, a signal analyzer, and a data transmission test platform;

[0029] A data monitoring module, which is used to conduct tests respectively in an external electromagnetic interference source and a non-interference environment. Connect the data cable to a computer and perform high-speed data transmission, and use an oscilloscope and a signal analyzer to monitor signal quality data in real time. The signal quality data includes the signal transmission error rate increase index and the signal transmission delay fluctuation index;

[0030] A data analysis module, which is used to convert the signal transmission error rate increase index and the signal transmission delay fluctuation index into a comprehensive feature vector, which is used as an input item of a machine learning model. After training the model, the signal quality index of the data cable during high-speed data transmission is obtained;

[0031] A comparative analysis module, which is used to compare and analyze the signal quality index of the obtained data cable during high-speed data transmission with a pre-set quality threshold, and classify the data cable into a high-quality signal data cable and a low-quality signal data cable according to the analysis result;

[0032] An optimization module. For high-quality signal data cables, it is recommended to continue using them; for low-quality signal data cables, further predict the degree of signal quality abnormality during high-speed data transmission. If the abnormality degree is high, corresponding optimization measures should be taken immediately.

[0033] In the test environment construction module, the main goal of constructing the test environment is to provide a test environment that can simulate the real usage scenario for mobile phone data cables during high-speed data transmission, and be equipped with appropriate test equipment. These test equipment will be used to detect signal quality problems that may occur during data transmission, including the impact of external electromagnetic interference (EMI) and other environmental factors on the signal.

[0034] Select a laboratory environment with good electromagnetic compatibility (EMC) control to reduce unnecessary external electromagnetic interference and ensure the normal operation of the equipment. For the comprehensiveness of the test, ensure that the test environment can simulate electromagnetic interference with different intensities and directions. This means that in the test, not only the ideal situation without interference should be considered, but also interference factors that may be encountered in the actual usage environment (such as: household appliances, wireless devices, mobile phones, etc.) should be introduced.

[0035] To test the transmission quality of data cables in different interference environments, the test environment needs to include devices or systems that can cause electromagnetic interference. For example: High-frequency signal generator: Used to simulate interference signal sources, such as the signal frequencies of Wi-Fi routers or mobile phones. Electromagnetic field generator: Simulates the impact of external strong magnetic fields on signal transmission to help evaluate the performance of data cables in real life. Controllable noise source: Such as wireless devices, LED lighting, etc., which can generate electromagnetic noise at specific frequencies and affect the data transmission process.

[0036] High-speed data transmission platform: To test the performance of data cables during high-speed data transmission, a suitable data transmission protocol and platform need to be selected. For example: USB 3.0 / 3.1: Supports high-speed data transmission and is widely used for data transmission between smartphones and other devices. Thunderbolt 3 or 4: For even higher-speed data transmission, especially in cases where large bandwidth is required (such as video stream transmission, large file transmission). PCIe (high-speed bus): For special application scenarios, PCIe can be used as the high-speed data transmission standard. Selecting a data transmission platform that meets the test requirements can ensure the authenticity and comprehensiveness of the test, simulating different transmission rates and workloads.

[0037] The devices in the test environment need to be able to detect the performance of data cables during high-speed transmission, including key parameters such as electromagnetic interference, bit error rate, signal attenuation, and latency. Oscilloscope: Used to monitor and analyze the voltage waveforms during the data cable transmission process to check the signal integrity. Signal quality monitoring: Observe the voltage waveforms during data transmission in real time through an oscilloscope to check if the signal is distorted or attenuated. Waveform analysis: Monitor the changes in the signal under external electromagnetic interference and analyze the distortion caused by the interference. Bit error rate analysis: Capture the waveforms during data transmission and combine with bit error rate analysis to help identify bit errors or data loss in data transmission.

[0038] Signal analyzer: Can analyze the spectrum of the signal, especially suitable for detecting noise or interference in high-frequency signals. Spectrum analysis: Monitor the possible frequency interference in the signal and identify the impact of external noise sources on the signal. Signal integrity analysis: Analyze the time-domain and frequency-domain characteristics of the signal to ensure that the signal transmission is not affected by unnecessary interference. EMI (electromagnetic interference) test: Detect the electromagnetic emissions during the data cable transmission and determine if they meet the standards to avoid electromagnetic pollution or distortion.

[0039] The data transmission test platform is used to simulate high-speed data transmission, providing the same data volume and transmission speed as the actual usage environment to analyze the performance of the data cable under actual working conditions. High-speed data transmission simulation: By simulating large file transfers, video stream transfers, or other high-bandwidth transmission tasks, test whether the data cable is stable during high-speed transmission. Load testing: Test the performance of the data cable under different loads (such as different file sizes, different data rates, etc.) to check whether it can continuously and stably transmit data. Environment simulation: The test platform can be adjusted according to needs to simulate different environmental conditions, such as different signal frequencies, transmission rates, etc.

[0040] The data monitoring module is used to conduct tests separately in the presence of external electromagnetic interference sources and non-interference environments. Connect the data cable to a computer and perform high-speed data transmission. Use an oscilloscope and a signal analyzer to monitor the signal quality data in real time. The signal quality data includes the signal transmission error rate increase index and the signal transmission delay fluctuation index.

[0041] In the absence of external electromagnetic interference, conduct a benchmark test. Use an oscilloscope to monitor the waveform of data transmission in real time to ensure that the signal is clear and distortion-free. Use a signal analyzer to check the spectrum of the signal to ensure that the signal frequency is stable and there is no additional noise.

[0042] Add interference sources such as high-frequency signal generators and electromagnetic field generators to the test environment. Measure data such as the increase in error rate and delay fluctuation during data transmission to evaluate the impact of interference on signal quality. Conduct interference analysis on the spectrum of the signal through a signal analyzer to identify the interference sources generated by external devices or the environment.

[0043] Expose the data cable to electromagnetic interference sources in different directions and evaluate the impact of interference in different directions on signal transmission. Combine the oscilloscope and the signal analyzer to analyze indicators such as signal attenuation and delay fluctuation to simulate different interference situations that the device may encounter in actual use.

[0044] Use devices such as an oscilloscope and a signal analyzer to record all test data in real time. In all test environments, organize and summarize indicators such as the signal quality, error rate, and delay of the data cable for subsequent analysis. Evaluate the test results according to the preset quality threshold to determine the signal quality of the data cable in different environments and determine whether it meets the standards.

[0045] Connect the data cable to the computer and start the high-speed data transfer task. Ensure that the test environment can simulate the actual usage scenario, and the transferred data can represent normal workloads, such as large file transfer, high-speed data stream, etc. Connect one end of the data cable of the mobile phone to be tested to the USB interface of the computer or other applicable high-speed data ports (such as Thunderbolt interface), and the other end to the device (such as a smartphone, external hard drive, etc.) for data transfer. Ensure that the computer has an interface that supports high-speed data transfer (such as USB 3.0 / 3.1, Thunderbolt, etc.). If specific types of data need to be transferred (such as high-definition video, compressed files, operating system images, etc.), prepare the relevant test files in advance. If a dedicated test platform is used (for example, a USB data transfer test platform), ensure that the device is correctly configured and can handle the requirements of high-bandwidth data transfer.

[0046] Start the file transfer. The data can be high-load and large-file data (such as high-definition video files, program packages, etc.) to simulate the real transfer scenario. At this time, the data cable will undertake a large amount of data exchange tasks between devices. Confirm the relevant parameters of the data transfer, such as transfer rate, file size, transfer time, etc., for subsequent evaluation of signal quality.

[0047] During the data transfer process, use an oscilloscope and a signal analyzer to monitor the signal quality in real time, and detect whether there are problems such as signal attenuation, noise interference, bit error rate, etc., to evaluate the performance of the data cable in high-speed transmission.

[0048] The main function of the oscilloscope is to monitor the voltage waveform in real time during the transfer process, capture the signal changes, and be able to identify problems such as signal distortion and noise. The steps of the oscilloscope are as follows:

[0049] Set the oscilloscope parameters: Time base: Set an appropriate time base (for example, in ns or μs) to ensure that the details of the data transfer process can be captured. Trigger setting: Ensure that the waveform is stably displayed at the start of the data transfer through the trigger setting, avoiding missing signal fluctuations at critical moments. Voltage range: Set an appropriate voltage range to ensure that the signal waveform does not exceed the measurement range of the oscilloscope.

[0050] Capture the signal waveform: Observe the signal waveform during the data transfer process to ensure that the signal waveform is clear and has no serious distortion. Compare the waveform during data transfer with the expected ideal waveform to detect whether there is significant noise, waveform distortion, or attenuation.

[0051] Signal distortion analysis: Detect the degree of signal distortion through the oscilloscope and observe whether the waveform has excessive offsets or noise. For example, if the shielding in the data cable is poor, it may cause signal interference and waveform distortion.

[0052] Bit error rate detection: Use an oscilloscope to observe the voltage waveform during transmission and find the incorrect transmission moments on the waveform (for example, when a data packet fails to reach the destination correctly). Calculate the bit error rate by comparing with the data transmission results at the computer end.

[0053] The signal analyzer is mainly used to analyze the frequency characteristics of signals, identify noise, interference, and other problems in the signals. The steps are as follows:

[0054] Use the signal analyzer to perform a spectrum analysis on the transmitted signal and view the frequency distribution of the signal. Monitor whether the signal is affected by external electromagnetic interference (EMI) and detect the spurious frequencies that may appear in the detected spectrum. By observing the noise in different frequency bands, the impact of electromagnetic interference on the signal quality can be found.

[0055] If external electromagnetic interference sources (such as wireless devices, household appliances, etc.) may affect signal transmission, the signal analyzer can help confirm the location and frequency of the signal interference source. During the test, additional interference frequencies may appear, resulting in the distortion of the signal spectrum or the degradation of the signal quality.

[0056] The signal analyzer can help evaluate the integrity of the signal and determine whether the signal is severely affected by attenuation or noise during transmission. In an environment with interference, the signal analyzer can show the anti-interference ability of the data line, such as excessive signal loss or frequency distortion.

[0057] Use an oscilloscope and a signal analyzer to capture signal quality data and record key indicators such as bit error rate, signal distortion, and interference frequency. Collect performance indicators during transmission, such as transmission rate, latency, and throughput, through a data transmission test platform or a computer system. Compare the actually measured signal quality data with the data under the ideal transmission state to evaluate the performance of the data line under different conditions. By analyzing these signal quality data (such as bit error rate, signal strength, noise ratio, etc.), potential performance bottlenecks can be identified.

[0058] The signal quality data includes the signal transmission bit error rate increase index, and the method for obtaining the signal transmission bit error rate increase index is:

[0059] The bit error rate is a function based on the signal-to-noise ratio and other factors (such as electromagnetic interference). In signal transmission, the relationship between the bit error rate BER and the signal-to-noise ratio SNR is: where B is the function term and M is the order of the modulation method (such as BPSK, QPSK, etc.). Construct a bit error rate sequence, and the bit error rate sequence is expressed as: {BER i}; where i represents the bit error rate value of the i-th measurement. For the calculation of the bit error rate increase index, the likelihood function can be expressed as: where P(BERi $BER(i|\theta)$ represents the bit error rate obtained in the $i$-th experiment under the given parameter $\theta$. i The parameter $\theta$ usually involves factors such as the SNR of the signal and the interference intensity. To simplify the calculation, the log-likelihood function $\ln L(\theta)$ is used, with the expression: Through logarithmic transformation, the likelihood function becomes easier to handle, especially when dealing with multiple experiment data.

[0060] The core task of maximum likelihood estimation is to find the optimal parameter $\theta^*$ that maximizes the log-likelihood function: After obtaining the optimal parameter, calculate the bit error rate $BER_0$ under the condition of no interference, and calculate the bit error rate $BER_1$ under the condition of interference, that is, the bit error rate measured in an environment containing electromagnetic interference and other factors. Calculate the signal transmission bit error rate increase index, with the expression: In the formula, $MK$ is the signal transmission bit error rate increase index.

[0061] The larger the signal transmission bit error rate increase index, the worse the signal quality of the data line during high-speed data transmission. When the bit error rate increase index is large, it means that after being affected by interference or noise, the transmission quality of the data line drops significantly, and the proportion of error bits increases. This usually indicates that the data line cannot effectively maintain data integrity in the face of factors such as electromagnetic interference (EMI) and signal attenuation. In high-speed data transmission, a large signal transmission bit error rate increase index may lead to frequent transmission errors, packet loss, or serious delays, ultimately affecting the communication stability between devices. Therefore, a large signal transmission bit error rate increase index is a sign of poor signal quality, low transmission efficiency, and high error occurrence rate.

[0062] The smaller the signal transmission bit error rate increase index, the better the signal quality of the data line during high-speed data transmission. On the contrary, a smaller bit error rate increase index indicates that the data line can maintain higher stability and lower error rate during transmission. Even in the presence of external interference or noise, the loss of signal quality is relatively small, and the increase in bit error rate is limited. This usually means that the data line has strong anti-interference ability and can maintain high transmission efficiency and accuracy during high-speed data transmission. For high-frequency and high-speed data transmission tasks, a smaller signal transmission bit error rate increase index helps to reduce data loss, improve system response speed, and reduce the probability of device failures or repairs. Therefore, when the signal transmission bit error rate increase index is small, the signal quality of the data line is better, and the transmission process is more stable and reliable.

[0063] The signal quality data includes the signal transmission delay fluctuation index. The method for obtaining the signal transmission delay fluctuation index is:

[0064] Collect the delay data during the signal transmission process. The delay data refers to the transmission delay L(t) of the signal at each time point t, which is monitored in real time by speed measurement tools (such as oscilloscopes, signal analyzers). The collected delay data is {(t1,L1),(t2,L2),...,(t n ,L n )}, where t n is the time point and L n is the signal delay corresponding to the time point; in Gaussian process regression, the kernel function determines the similarity between input data points, and the kernel function is used to measure the similarity of delay data between different time points t i and t j . Commonly used kernel functions include: squared exponential kernel and Matérn kernel; after selecting the appropriate kernel function, it will be used to calculate the covariance matrix between input data points. According to the selected kernel function, calculate the covariance matrix K between delay data points: where k(t n ,t n ) is the covariance value calculated by the kernel function. If the signal delay data is affected by noise, a noise term is required, and the expression is: where I is the identity matrix. Through Gaussian process regression, the predicted value of the delay data is obtained. For a new time point prediction, its corresponding delay value is obtained. The input X = {t1,t2,...,t n}, and the output y = {L1,L2,...,L n}; Prediction data: Prediction point t * . Calculate the covariance matrix: K(X,X) is the covariance matrix between training data. K(X,t * ) is the covariance matrix between training data and prediction points. K(t * ,t * ) is the covariance matrix between prediction points. The predicted mean μ * is: μ * = K(t * ,X)K(X,X) -1 y; The predicted variance is: Extract the standard deviation of the signal from the predicted variance, and perform a weighted average calculation on the standard deviations of the delay fluctuations calculated at several time points to obtain the signal transmission delay fluctuation index.

[0065] The larger the signal transmission delay fluctuation index, the worse the signal quality of the data line during high-speed data transmission. When the delay fluctuation index is relatively large, it indicates that the signal has experienced significant delay fluctuations during transmission, meaning that there are unstable delay variations during data transmission. Such fluctuations may be caused by factors such as electromagnetic interference, insufficient device performance, network congestion, or signal attenuation. A high fluctuation index usually leads to out-of-order packets, transmission errors, or packet losses, and may trigger communication failures between devices. As the delay fluctuation increases, the transmission stability of the data deteriorates, resulting in untimely system responses or unpredictable delays, which is particularly dangerous for data transmission systems with high frequency and high bandwidth requirements and may lead to system crashes, data corruption, or even device damage.

[0066] The smaller the signal transmission delay fluctuation index, the better the signal quality of the data line during high-speed data transmission. A smaller delay fluctuation index indicates that the change in delay during signal transmission is smaller, and the transmission process is relatively stable. This means that the data line can maintain stable delay characteristics during high-speed data transmission, and the data can be transmitted at a relatively uniform rate, reducing signal distortion or errors caused by delay fluctuations. A low fluctuation index usually reflects high-quality signal transmission, which can improve the reliability and response speed of the system. Especially in application scenarios that require high-precision data transmission, a smaller delay fluctuation can significantly enhance the user experience and the overall performance of the device.

[0067] A data analysis module is used to convert the signal transmission bit error rate increase index and the signal transmission delay fluctuation index into a comprehensive feature vector, which is used as an input item for a machine learning model. After training the model, the signal quality index of the data line during high-speed data transmission is obtained.

[0068] Convert the signal transmission bit error rate increase index and the signal transmission delay fluctuation index into a comprehensive feature vector. Use the comprehensive feature vector as the input of a machine learning model. The machine learning model takes predicting the signal quality index label of the data line during high-speed data transmission for each group of comprehensive feature vectors as the prediction target, and takes minimizing the sum of the prediction errors of the signal quality index labels of all data lines during high-speed data transmission as the training target. Train the machine learning model until the sum of the prediction errors reaches convergence and then stop the model training. Determine the signal quality index of the data line during high-speed data transmission according to the model output result. Among them, the machine learning model is a polynomial regression model.

[0069] The method for obtaining the signal quality index of a data line during high-speed data transmission is as follows: From the comprehensive feature vector training data of a trained machine learning model, obtain the corresponding function expression: Q = F(MK, LH); where F is the output function of the model, MK is the signal transmission bit error rate increase index, LH is the signal transmission delay fluctuation index, and Q is the signal quality index of the data line during high-speed data transmission.

[0070] A comparison and analysis module is used to compare and analyze the signal quality index of the obtained data line during high-speed data transmission with a pre-set quality threshold, and classify the data line into a high-quality signal data line and a low-quality signal data line according to the analysis result.

[0071] Compare and analyze the signal quality index of the obtained data line during high-speed data transmission with a pre-set quality threshold. If the signal quality index of the data line during high-speed data transmission is greater than or equal to the pre-set quality threshold, it indicates that the signal quality of the data line during high-speed data transmission is high and the data transmission is stable. At this time, no warning signal is generated, and the corresponding data line is classified as a high-quality signal data line; if the signal quality index of the data line during high-speed data transmission is less than the pre-set quality threshold, it indicates that the signal quality of the data line during high-speed data transmission is low and the data transmission is unstable. At this time, a warning signal is generated, and the corresponding data line is classified as a low-quality signal data line.

[0072] An optimization module recommends continuing to use high-quality signal data lines; for low-quality signal data lines, further predict the degree of signal quality abnormality during high-speed data transmission. If the degree of abnormality is high, corresponding optimization measures are taken immediately.

[0073] The main reason for recommending continuing to use high-quality signal data lines is their stability and reliability during high-speed data transmission. High-quality signal data lines can maintain a low bit error rate and small delay fluctuation during high-speed transmission, indicating that they have a strong ability to protect signals and a high anti-interference ability against external factors such as electromagnetic interference (EMI). The stable signal transmission performance ensures the integrity of data and the continuity of transmission, reducing the risk of data packet loss and transmission errors. Therefore, continuing to use high-quality signal data lines helps to ensure the stability of data transmission between devices, especially in application scenarios with high precision requirements (such as video transmission, data backup).

[0074] For low-quality signal data lines, further predict the degree of signal quality abnormality during high-speed data transmission, specifically:

[0075] Mark the degree of signal quality abnormality as E, and the calculation expression is: When Q < T, E is positive, and the larger the value, the higher the degree of signal quality abnormality. Set a high abnormal threshold Ecritical and a low abnormal threshold Elow to predict and classify the degree of signal quality abnormality E. Further divide the degree of abnormality into "low", "medium", and "high" levels to take corresponding optimization measures. If E < Elow, the degree of abnormality is low, usually no immediate optimization is required, but close monitoring is needed. If Elow ≤ E < Ecritical, the degree of abnormality is medium, and optimization or replacement can be considered. If E ≥ Ecritical, the degree of abnormality is high, and immediate optimization measures should be taken.

[0076] In the case of a relatively high degree of abnormality, take optimization measures to improve the signal quality index Q by improving the bit error rate increase index MK and the signal transmission delay fluctuation index LH of signal transmission. Obtain a new signal quality index Q′ through adjustment. The expression is: Q′ = f(MK′, LH′); where MK′ = MK - ΔMK; MK′ is the optimized bit error rate increase index, and ΔMK represents the improvement amplitude; LH′ = LH - ΔLH; LH′ is the optimized delay fluctuation index, and ΔLH represents the improvement amplitude; f is the output function of the polynomial regression model. After optimization, calculate the new degree of abnormality E′, and the expression is: If E′ reaches the expected improvement goal (such as E′ < Elow), it is considered that the optimization measure is effective, and the data cable can continue to be used. If E′ is still higher than the set threshold, further optimization or replacement of the data cable may be required.

[0077] In this embodiment, first, a test environment suitable for high-speed data transmission of mobile phone data cables is created through a test environment construction module, equipped with devices such as an oscilloscope, a signal analyzer, and a data transmission test platform to detect signal quality. On this basis, the data monitoring module connects the data cable to the computer and performs high-speed transmission under the conditions of external electromagnetic interference and no interference respectively, and real-time monitors key data such as the bit error rate increase index and the signal transmission delay fluctuation index of signal transmission. Then, the data analysis module converts the collected signal features into a comprehensive feature vector, which is used as an input item of the machine learning model, and generates a signal quality index after training the model. Subsequently, the comparison and analysis module compares the signal quality index of the data cable with a preset quality threshold, and divides the data cable into two categories: high quality and low quality. Finally, the optimization module recommends continuing to use the high-quality data cable, while further predicting the degree of abnormality of the low-quality data cable. If the degree of abnormality is high, immediate optimization measures are taken to improve its signal quality.

[0078] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0079] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more sets of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0080] As described above, the above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application.

Claims

1. A mobile phone data line detection and analysis system, characterized in that: It includes test environment construction module, data monitoring module, data analysis module, comparative analysis module and optimization module; The test environment construction module is used to build a test environment for data transmission over a mobile phone data line and select test equipment that can perform high-speed data transmission and is equipped with corresponding interference detection functions. The test equipment includes an oscilloscope, a signal analyzer, and a data transmission test platform. The data monitoring module is used to conduct tests under external electromagnetic interference sources and non-interference environments respectively, connect the data line to the computer and perform high-speed data transmission, and use an oscilloscope and a signal analyzer to monitor the signal quality data in real time. The signal quality data includes the signal transmission bit error rate increase index and the signal transmission delay fluctuation index; A data analysis module is used to convert the signal transmission bit error rate increase index and the signal transmission delay fluctuation index into a comprehensive feature vector as an input item of the machine learning model, and obtain the signal quality index of the data line during high-speed data transmission after training the model; A comparison and analysis module, used to compare and analyze the signal quality index of the data line obtained during the high-speed data transmission process with a preset quality threshold, and divide the data line into a high-quality signal data line and a low-quality signal data line according to the analysis result; The optimization module recommends that high-quality signal data lines continue to be used; for low-quality signal data lines, the abnormality of their signal quality during high-speed data transmission is further predicted. If the abnormality is high, corresponding optimization measures are taken immediately.

2. A mobile phone data line detection and analysis system according to claim 1, characterized in that: In the data monitoring module, the signal quality data includes the signal transmission bit error rate increase index, and the method for obtaining the signal transmission bit error rate increase index is: In signal transmission, the relationship between the bit error rate BER and the signal-to-noise ratio SNR is: Among them, B is the function term, M is the order of the modulation method, and a bit error rate sequence is constructed. The bit error rate sequence is expressed as {BER i }; where i represents the bit error rate value measured for the i-th time. For the calculation of the bit error rate increase index, the likelihood function is expressed as: Among them, P(BER i |θ) represents the bit error rate BER obtained in the i-th experiment under the given parameter θ i The probability of , in order to simplify the calculation, use the log-likelihood function lnL(θ), the expression: The core task of maximum likelihood estimation is to find the optimal parameter θ* to maximize the log-likelihood function: After obtaining the optimal parameters, the bit error rate BER0 under interference-free conditions and the bit error rate BER1 under interference conditions are calculated, that is, the bit error rate measured in an environment containing electromagnetic interference factors, and the signal transmission bit error rate increase index is calculated. The expression is: Where MK is the signal transmission bit error rate increase index.

3. A mobile phone data line detection and analysis system according to claim 2, characterized in that: The signal quality data includes a signal transmission delay fluctuation index, and the method for obtaining the signal transmission delay fluctuation index is as follows: Collect delay data during signal transmission. Delay data refers to the transmission delay L(t) of the signal at each time point t. The collected delay data is {(t1,L1),(t2,L2),...,(t n ,L n )}, where t n It's time, L n is the signal delay at the corresponding time point; in Gaussian process regression, the kernel function determines the similarity between input data points. The kernel function is used to measure the similarity between different time points t i and t j Based on the selected kernel function, the covariance matrix K between the delayed data points is calculated: Among them, k(t n ,t n ) is the covariance value calculated by the kernel function. If the signal delay data is affected by noise, the noise term needs to be added The expression is: Where I is the unit matrix. Through Gaussian process regression, the predicted value of the delay data is obtained. The new time point is predicted to obtain its corresponding delay value. Input X = {t1, t2, ..., t n }, output y = {L1, L2, ..., L n }; Prediction data: prediction point t * ; Calculate the covariance matrix: K(X,X) is the covariance matrix between the training data, K(X,t * ) is the covariance matrix between the training data and the prediction points, K(t * ,t * ) is the covariance matrix between the prediction points, and the prediction mean μ * is: μ * =K(t * ,X)K(X,X) -1 y; prediction variance for: The standard deviation of the signal is extracted from the prediction variance, and the standard deviation of the delay fluctuation calculated at several time points is weighted averaged to obtain the signal transmission delay fluctuation index.

4. A mobile phone data line detection and analysis system according to claim 3, characterized in that: In the data analysis module, the signal transmission bit error rate increase index and the signal transmission delay fluctuation index are converted into comprehensive feature vectors, and the comprehensive feature vectors are used as the input of the machine learning model. The machine learning model uses each group of comprehensive feature vectors to predict the signal quality index label of the data line during high-speed data transmission as the prediction target, and takes minimizing the sum of prediction errors of the signal quality index labels of all data lines during high-speed data transmission as the training target. The machine learning model is trained until the sum of prediction errors converges and the model training is stopped. The signal quality index of the data line during high-speed data transmission is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

5. A mobile phone data line detection and analysis system according to claim 4, characterized in that: In the comparison and analysis module, the obtained signal quality index of the data line during the high-speed data transmission process is compared and analyzed with the preset quality threshold. If the signal quality index of the data line during the high-speed data transmission process is greater than or equal to the preset quality threshold, it means that the signal quality of the data line during the high-speed data transmission process is high and the data transmission is stable. At this time, no warning signal is generated, and the corresponding data line is divided into a high-quality signal data line; if the signal quality index of the data line during the high-speed data transmission process is less than the preset quality threshold, it means that the signal quality of the data line during the high-speed data transmission process is low and the data transmission is unstable. At this time, a warning signal is generated, and the corresponding data line is divided into a low-quality signal data line.

6. A mobile phone data line detection and analysis system according to claim 1, characterized in that: In the optimization module, the abnormal degree of signal quality is marked as E, and the calculation expression is: When Q < T, E is positive, and the larger the value, the higher the abnormal degree of signal quality. Set the high abnormal threshold Ecritical and the low abnormal threshold Elow to predict and classify the abnormal degree E of signal quality. If E < Elow, the abnormal degree is low, and no immediate optimization is required, but close monitoring is needed. If Elow ≤ E < Ecritical, the abnormal degree is medium, and optimization or replacement is carried out. If E ≥ Ecritical, the abnormal degree is high, and immediate optimization measures should be taken.

7. A mobile phone data line detection and analysis system according to claim 6, characterized in that: In the case of a high degree of abnormality, optimization measures are taken to improve the signal quality index Q by improving the error rate increase index MK of signal transmission and the delay fluctuation index LH of signal transmission. After adjustment, a new signal quality index Q' is obtained, and the expression is: Q' = f(MK', LH'); where MK' = MK - ΔMK; MK' is the optimized error rate increase index, and ΔMK represents the improvement amplitude; LH' = LH - ΔLH; LH' is the optimized delay fluctuation index, and ΔLH represents the improvement amplitude; f is the output function of the polynomial regression model; After optimization, the new degree of abnormality E' is calculated, and the expression is: If E' < Elow, the optimization measures are effective, and the data cable can continue to be used; if E ≥ Ecritical, further optimization or replacement of the data cable is required.

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