A method and system for testing the performance of a car wire harness parameter

Abstract

The application discloses a kind of automobile wire harness parameter performance test method and system, it is related to electrical equipment intelligent monitoring and predictive protection field, including, based on high-precision micro-resistance change rate data stream, the trend intensity of resistance change, fluctuation characteristics and abnormal frequency are extracted, and three-dimensional characteristic electric pulse signal is fused and generated;According to three-dimensional characteristic electric pulse signal, the decision result is converted into control instruction to drive actuator to execute safety diagnosis control, and generate equipment operation log;Based on equipment operation log, combine brillouin scattering principle to locate overheated area, measure the change of dielectric loss of insulating material, and generate diagnosis verification report;According to diagnosis verification report and decision result, dynamically optimize neural pathway connection weight, and generate wire harness performance report;By adaptive adjustment threshold control parameter, the intelligent extraction and compression coding of resistance change characteristics under complex working conditions are realized, and three-level response decision is driven to complete fault grading judgment and execution in millisecond time window.

Description

Technical Field

[0001] This invention relates to the field of multi-parameter testing and protection decision-making technology for automotive wiring harnesses, and in particular to a method and system for testing the performance parameters of automotive wiring harnesses. Background Technology

[0002] In the field of automotive electronics reliability engineering, real-time monitoring of wiring harness connection status has long relied on discrete detection schemes involving contact resistance sampling and temperature sensing. Current mainstream technologies fall into two main categories: one is based on the four-wire Kelvin bridge principle, obtaining static resistance values ​​through constant current source excitation; the other employs distributed fiber optic temperature sensing technology, utilizing Raman scattering effects to construct a temperature field model.

[0003] Firstly, at the level of detection principle, conventional resistance sampling method is affected by the potential barrier effect of oxide film on contact surface, and its ohmic contact model deviates significantly from the actual electronic transport behavior at the micro-nano scale; secondly, in terms of data processing architecture, the fixed threshold alarm mechanism is difficult to adapt to complex working conditions, especially when mechanical vibration and temperature change are coupled with multiple physical fields, it is impossible to dynamically adjust the abnormal contact resistance evaluation criteria. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method for testing the performance parameters of automotive wiring harnesses to solve the problems of missed detection of micro-ohmic resistance jump events caused by the contact interface barrier effect and delayed response to insulation failure warnings under complex operating conditions.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for testing the performance parameters of automotive wiring harnesses, which includes calculating the contact resistance value based on the principle of quantum tunneling effect and continuously generating a high-precision micro-resistance change rate data stream.

[0008] Based on high-precision micro-resistance change rate data stream, the trend intensity, fluctuation characteristics and abnormal frequency of resistance change are extracted and fused to generate a three-dimensional characteristic electrical pulse signal.

[0009] Based on the three-dimensional characteristic electrical pulse signal, the decision results are converted into control commands to drive the actuator to perform safety diagnosis and control, and generate equipment operation logs;

[0010] Based on the equipment operation log, the overheated area is located by combining the Brillouin scattering principle, the change in dielectric loss of the insulating material is measured, and a diagnostic verification report is generated.

[0011] Based on the diagnostic verification report and decision results, the neural pathway connection weights are dynamically optimized, and a bundle performance report is generated.

[0012] As a preferred embodiment of the automotive wiring harness parameter performance testing method of the present invention, the steps of calculating the contact resistance value based on the quantum tunneling effect principle and continuously generating a high-precision micro-resistance change rate data stream are as follows.

[0013] A nanoscale insulating dielectric layer was constructed, and the electron tunneling current response curve was captured in real time to generate raw voltage and current datasets.

[0014] Based on the principle of quantum tunneling effect, the original voltage and current dataset is analyzed, environmental interference is dynamically compensated, the instantaneous value of contact electricity is calculated, and a time series resistance array is generated.

[0015] A sliding window differentiation process is performed on the time-series resistor array to eliminate high-frequency noise interference and generate a high-precision micro-resistance change rate data stream.

[0016] As a preferred embodiment of the automotive wiring harness parameter performance testing method of the present invention, the steps for extracting the trend intensity, fluctuation characteristics, and anomaly frequency of resistance changes based on a high-precision micro-resistance change rate data stream are as follows:

[0017] Based on high-precision micro-resistance change rate data stream, the data conversion of resistance change to action potential is performed to generate pulse sequence and real-time updated threshold control parameters.

[0018] Based on the pulse sequence and real-time updated threshold control parameters, the system performs trend intensity analysis of resistance changes, captures fluctuation characteristics, and statistically analyzes abnormal frequencies, integrating these to generate three characteristic indicators.

[0019] In a preferred embodiment of the automotive wiring harness parameter performance testing method of the present invention, the steps for fusing and generating three-dimensional characteristic electrical pulse signals are as follows:

[0020] The three characteristic indicators are converted into electrical pulses and fused to generate a three-dimensional characteristic electrical pulse signal.

[0021] As a preferred embodiment of the automotive wiring harness parameter performance testing method of the present invention, the steps of converting the decision result into control commands based on the three-dimensional characteristic electrical pulse signal to drive the actuator to perform safety diagnostic control and generate an equipment operation log are as follows:

[0022] Based on the three-dimensional characteristic electrical pulse signal, the analytical circuit synchronously extracts the physical characteristics of the pulse in three dimensions and generates a three-dimensional feature vector.

[0023] Real-time pattern matching is performed in the decision rule base based on three-dimensional feature vectors, and three-level response decision results are dynamically generated based on three indicators: trend strength, fluctuation characteristics and anomaly frequency.

[0024] Based on the three-level response decision results, control commands are dynamically generated to drive the actuators to perform safety diagnostics and adjustments, and equipment operation logs are generated simultaneously.

[0025] As a preferred embodiment of the automotive wiring harness parameter performance testing method of the present invention, the steps of locating overheated areas based on equipment operation logs and combining the Brillouin scattering principle, measuring changes in dielectric loss of the insulation material, and generating a diagnostic verification report are as follows.

[0026] Based on the abnormal time points and location markers recorded in the equipment operation log, a scan is performed along the target harness path to generate a thermal map of the temperature field distribution.

[0027] Based on the temperature field distribution thermogram, the overheated area is located, and the dielectric polarization response delay time of the insulating material is measured simultaneously in the overheated area to generate the three-dimensional coordinates of the overheated area and the dielectric loss change index.

[0028] By integrating the three-dimensional coordinates of the overheated area, dielectric loss change indicators, and abnormal event records in the operation log, a diagnostic verification report is generated.

[0029] In a preferred embodiment of the automotive wiring harness parameter performance testing method of the present invention, the steps of dynamically optimizing neural pathway connection weights and generating a wiring harness performance report based on diagnostic verification reports and decision results are as follows:

[0030] Based on the diagnostic verification report and the three-level response decision results, perform spatiotemporal alignment of the data to generate a fusion analysis dataset;

[0031] Based on the fusion analysis dataset, the neural pathway connection weights are adjusted, and the sensitivity threshold is dynamically adjusted according to the dielectric loss change rate to generate an updated neural pathway weight mapping table.

[0032] By integrating the neural pathway weight mapping table and diagnostic verification report, a performance topology map is constructed, the spatial structure data of the performance topology map is extracted, and a harness performance report is generated.

[0033] Secondly, the present invention provides an automotive wiring harness parameter performance testing system, comprising,

[0034] The resistance change rate generation module is used to calculate the contact resistance value based on the principle of quantum tunneling effect and continuously generate a high-precision micro resistance change rate data stream.

[0035] The electrical pulse signal extraction module is used to extract the trend intensity, fluctuation characteristics and abnormal frequency of resistance change based on the high-precision micro-resistance change rate data stream, and fuse them to generate a three-dimensional feature electrical pulse signal.

[0036] The control command conversion module is used to convert decision results into control commands based on three-dimensional characteristic electrical pulse signals to drive the actuator to perform safety diagnosis and control, and generate equipment operation logs;

[0037] The dielectric loss measurement module is used to locate overheated areas based on equipment operation logs and the Brillouin scattering principle, measure changes in dielectric loss of insulating materials, and generate diagnostic verification reports.

[0038] The neural pathway weight optimization module is used to dynamically optimize the neural pathway connection weights based on diagnostic verification reports and decision results, and generate a bundle performance report.

[0039] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein the computer program, when executed by the processor, implements any step of the automotive wiring harness parameter performance testing method as described in the first aspect of the present invention.

[0040] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the automotive wiring harness parameter performance testing method as described in the first aspect of the present invention.

[0041] The beneficial effects of this invention are as follows: a quantum tunneling response model is established by depositing a nano-insulating dielectric layer on the surface of the wire harness terminal atomic layer, and the instantaneous value of the contact current is accurately analyzed by combining a dynamic dielectric compensation algorithm, thereby achieving nanosecond-level capture of micro-ohm-level resistance jumps; furthermore, a dynamic threshold mechanism of spiking neurons is adopted to achieve intelligent extraction and compressed encoding of resistance change characteristics under complex working conditions by adaptively adjusting the threshold control parameters, driving the three-level response decision to complete fault classification judgment and execution within a millisecond-level time window. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart of a method for testing the performance parameters of automotive wiring harnesses.

[0044] Figure 2 A flowchart illustrating the workings of the dynamic thresholding mechanism of spiking neurons.

[0045] Figure 3 A flowchart for generating a three-level response decision.

[0046] Figure 4A flowchart for neural pathway optimization and harness performance reporting. Detailed Implementation

[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0048] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0049] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0050] Reference Figures 1-4 As one embodiment of the present invention, this embodiment provides a method for testing the performance parameters of automotive wiring harnesses, comprising the following steps:

[0051] S1: Calculates contact resistance value based on the principle of quantum tunneling effect, and continuously generates a high-precision micro-resistance change rate data stream;

[0052] S1.1: A nanoscale insulating dielectric layer is constructed using atomic layer deposition technology, and the electron tunneling current response curve is captured in real time to generate raw voltage and current datasets;

[0053] More specifically, an atomic layer deposition process is performed on the surface of the wire harness terminals, and precursor gas and reactant gas are alternately introduced layer by layer to generate a nanoscale insulating dielectric layer of specified composition. A continuously varying scanning voltage sequence is applied to both sides of the insulating dielectric layer, and the tunneling current response formed by electrons passing through the insulating dielectric layer under the corresponding scanning voltage is captured in real time by a high-sensitivity current sensor. The current measurement values ​​corresponding to the application of negative scanning voltage, zero scanning voltage, and positive scanning voltage are fully recorded, and finally, a raw dataset of voltage and current containing the scanning voltage sequence and tunneling current response is generated.

[0054] S1.2: Based on the principle of quantum tunneling effect, analyze the original voltage and current dataset, dynamically compensate for environmental interference through the dielectric properties of the dielectric layer, calculate the instantaneous value of contact electricity and generate a time series resistance array;

[0055] More specifically, the original voltage and current datasets are analyzed using the quantum tunneling effect principle to establish an exponential response relationship between the electron tunneling probability and the applied scanning voltage; the ambient temperature fluctuation amplitude is monitored in real time, and dynamic compensation coefficients are extracted through the frequency response function of the dielectric properties of the dielectric layer; the tunneling current response value is corrected using the compensation coefficients, and the instantaneous contact current value is separated from the corrected current response value; Ohm's law is used to convert the instantaneous contact current value into a dynamic resistance value, and a time-series resistance array is constructed according to the acquisition time sequence.

[0056] S1.3: Based on the time series resistor array, a sliding window differentiation process is performed, and high-frequency noise interference is eliminated through a digital filtering algorithm to generate a high-precision micro-resistance change rate data stream.

[0057] More specifically, sliding window differentiation is performed on the time-series resistor array: an analysis window of a specific time length is set and moves with a fixed step size; the central difference method is used to calculate the resistance difference coefficients point by point; the Butterworth low-pass filtering algorithm is applied to the difference coefficient sequence, the filter order is designed and the normalized cutoff frequency is set, and phase distortion is eliminated through bidirectional filtering; finally, a time-aligned high-precision micro-resistance change rate data stream is generated, strictly inheriting the timestamp system of the original time-series resistor array, and the differential value is measured in ohms / second, for example, the millisecond-level sampling interval corresponds to the millisecond-level micro-resistance change rate resolution.

[0058] S2: Based on high-precision micro-resistance change rate data stream, extract the trend intensity, fluctuation characteristics and abnormal frequency of resistance change, and fuse them to generate a three-dimensional characteristic electrical pulse signal;

[0059] S2.1: Based on high-precision micro-resistance change rate data stream, the data conversion of resistance change to action potential is performed through the dynamic threshold mechanism of spiking neurons, generating pulse sequences and real-time updated threshold control parameters;

[0060] Furthermore, based on a high-precision micro-resistance change rate data stream, the resistance change of adjacent timestamps is captured by differential operation; the change is input into the dynamic threshold mechanism of the spiking neuron, and the resistance change is accumulated through the leakage integrator to form a membrane potential. When the membrane potential exceeds the dynamic sensitivity threshold, a pulse is triggered and the integrator is reset; the triggering simultaneously calls the logarithmic function to dynamically update the dynamic sensitivity threshold; and the pulse sequence and the real-time updated threshold control parameters are output in real time.

[0061] The dynamic sensitivity threshold, as a dynamic benchmark with real-time adaptive capabilities, precisely regulates pulse trigger sensitivity through a closed-loop feedback mechanism: During initialization, a predefined initialization threshold is loaded; during the operation of the spiking neuron dynamic threshold mechanism, when the accumulated voltage driven by the high-precision micro-resistance change rate data stream exceeds the current dynamic sensitivity threshold level, the threshold is immediately reduced exponentially according to the dynamic desensitization coefficient τ; if no trigger event is detected, the threshold level is continuously increased in threshold increments of 0.5mV / ms; simultaneously, a minimum protection threshold of 5mV is set to prevent the sensitivity from increasing indefinitely, and a maximum noise tolerance threshold of 500mV is set to suppress environmental interference overload. Its dynamic evolution is as follows: when environmental interference intensifies, the dynamic sensitivity threshold actively increases to suppress false triggers; when weak signals appear, the dynamic sensitivity threshold rapidly decreases to increase detection sensitivity; ultimately, the dynamic sensitivity threshold directly determines the pulse sequence density and is updated synchronously with the pulse event as a real-time dynamic indicator characterizing sensitivity and environmental noise levels.

[0062] S2.2: Based on the pulse sequence and the real-time updated threshold control parameters, perform trend intensity analysis of resistance changes, capture of fluctuation characteristics and statistics of abnormal frequency, and integrate to generate three characteristic indicators;

[0063] Furthermore, based on the pulse sequence and the real-time updated dynamic sensitivity threshold, the following steps are performed: trend intensity analysis is performed: the trend intensity value of resistance change is derived by analyzing the pulse density time evolution gradient; fluctuation feature capture is performed: the resistance fluctuation amplitude feature is extracted by calculating the pulse interval variation coefficient; anomaly frequency statistics are performed: the frequency of resistance anomalies is obtained by counting the number of high-density pulse events; finally, the three results of trend intensity value, fluctuation amplitude feature and anomaly frequency are integrated to generate three feature indicators.

[0064] The trend intensity analysis of resistance change is achieved by generating a pulse density time series based on the real-time statistical analysis of the number of pulses within a fixed time unit of the pulse sequence. The pulse density time series is then processed by first-order time axis differentiation to calculate the pulse density change gradient. Linear regression is used to fit the pulse density gradient sequence to extract the slope value. The real-time updated dynamic sensitivity threshold is used as a sensitivity correction factor to normalize and calibrate the slope value. Finally, a scalar trend intensity value quantifying the direction and rate of resistance change is output. Positive values ​​indicate a continuous increase in resistance, negative values ​​indicate a decrease in resistance, and fluctuations near zero are judged as a steady state.

[0065] The fluctuation feature capture process involves extracting the time intervals between adjacent pulses from the pulse sequence to form an ordered pulse interval sequence, calculating the arithmetic mean of the pulse interval sequence to represent the basic fluctuation frequency, obtaining the standard deviation of the interval sequence to quantify the absolute fluctuation amplitude, and generating the coefficient of variation as the normalized relative volatility. The dynamically updated dynamic sensitivity threshold is used as a dynamic weighting factor to correct the fluctuation amplitude. The final physical mapping rule for the fluctuation amplitude feature is as follows: the low value range corresponds to a stable contact state, the medium value range reflects normal operational fluctuations, and the high value range warns of connection anomaly risks.

[0066] The anomaly frequency statistics method involves identifying high-density pulse events in a pulse sequence through time window scanning. First, an upper limit for the pulse interval within an event and a lower limit for the number of consecutive pulses are set. When a continuous pulse group satisfies the condition that the maximum interval does not exceed the maximum interval for event judgment and contains at least the number of consecutive pulses below the lower limit, it is determined to be a valid event. Events are counted in fixed time units, and the anomaly occurrence frequency is dynamically corrected through a real-time updated dynamic sensitivity threshold. Finally, the event frequency per unit time is output as the anomaly occurrence frequency. A zero value indicates stable and no anomalies, a low frequency corresponds to acceptable transient interference, and a high frequency warns of the risk of continuous contact failure.

[0067] S2.3: The three characteristic indicators are converted into electrical pulses through a physical characteristic translation mechanism, and a memristor array is used to fuse and generate a three-dimensional characteristic electrical pulse signal.

[0068] Furthermore, electrical pulse conversion and fusion are performed based on three characteristic indicators: First, the trend intensity value is converted into an amplitude-dimensional electrical pulse signal through a physical characteristic translation mechanism, the fluctuation amplitude characteristic is converted into a frequency-dimensional pulse sequence, and the frequency of anomaly occurrence triggers phase-dimensional modulation; the electrical pulse signals converted from the three indicators are synchronously input into the three independent ports of the memristor array, and the conductance value weighted fusion is performed at the intersection of the memristor array, and the synthesized waveform is output through charge integration effect; finally, a three-dimensional characteristic electrical pulse signal carrying amplitude, frequency and phase dimensions is generated at the output of the memristor array, and the waveform characteristics strictly follow the physical meaning of the input indicators.

[0069] S3: Based on the three-dimensional characteristic electrical pulse signal, the decision result is converted into control commands to drive the actuator to perform safety diagnosis and control, and generate equipment operation logs;

[0070] S3.1: Based on the three-dimensional characteristic electrical pulse signal, the physical features of the pulse in three dimensions are extracted simultaneously through the pulse physical characteristic analysis circuit of the behavior analysis engine to generate a three-dimensional feature vector;

[0071] More specifically, based on the pulse physical characteristic analysis circuit of the three-dimensional feature electric pulse signal input behavior analysis engine, amplitude dimension processing is performed: a sliding window differential operation is applied to the amplitude dimension electric pulse signal to eliminate DC drift, and the amplitude dimension refined signal is output; the refined signal triggers frequency dimension feature extraction: the fast Fourier transform algorithm is used to calculate the energy distribution of the main frequency spectrum of the electric pulse signal, capture the peak frequency component intensity, and generate frequency dimension features; the frequency dimension features and the original phase dimension signal are synchronously input into phase dimension processing: the phase deviation angle is calculated by measuring the time difference of the rising edge of adjacent pulses through a zero-crossing detection circuit, and the phase dimension features are output; finally, the mean of the amplitude dimension refined signal, the frequency dimension features, and the phase dimension features are packaged according to the timestamp to generate a three-dimensional feature vector data package in the format of [amplitude feature, frequency feature, phase feature].

[0072] The three dimensions of physical characteristics include amplitude, frequency, and phase.

[0073] The amplitude dimension physical feature characterizes the voltage intensity change characteristics of the electric pulse signal. The processing is as follows: the amplitude component of the original three-dimensional characteristic electric pulse signal is input, and the baseline drift component is subtracted in real time through the sliding window differential algorithm. The peak value of the output signal is stabilized within the preset voltage range. The amplitude dimension physical feature directly quantifies the transient contact voltage fluctuation amplitude between conductors. Physically, the larger the amplitude dimension physical feature, the higher the energy of the contact resistance change event. The final output is a scalar value in millivolts. For example, the amplitude dimension physical feature maintains a low millivolt range during steady-state contact and jumps to a high millivolt level when a contact arc occurs.

[0074] It should be noted that the preset voltage range is set based on the physical signal characteristics and system protection requirements. The lower limit covers the minimum measurable voltage fluctuation of the conductor steady-state contact, and the upper limit accommodates the peak value of the contact arc. The typical range is set to -10V to +10V. Through a real-time reference correction mechanism and hardware protection, it ensures that the steady-state condition achieves millivolt-level resolution, the fault state fully captures volt-level jumps, and the range boundary strictly matches the conductor-insulation layer breakdown physical model.

[0075] The frequency dimension physical feature analysis analyzes the spectral energy distribution characteristics of the electrical pulse signal. The processing steps are as follows: receiving the refined signal after amplitude dimension processing, performing a fast Fourier transform to calculate the frequency domain energy distribution, and extracting the energy proportion of the main frequency band; specifically, extracting the energy integral value of the example percentage bandwidth around the peak frequency point, and outputting the relative energy percentage; the frequency dimension physical feature directly maps the duration characteristics of the discharge event, physically manifested as a higher frequency dimension physical feature representing a shorter pulse event time scale; the output is a pure numerical value ranging from 0-100%, for example, during corona discharge, the frequency dimension physical feature is concentrated in the example mid-frequency band, while during arc discharge, it shifts to the example high-frequency band.

[0076] The phase dimension physical feature describes the timing deviation characteristics of the electrical pulse signal. The processing is as follows: a voltage comparator circuit is deployed at the zero-crossing point of the three-dimensional characteristic electrical pulse signal to measure the time difference between the rising edges of adjacent pulses and map the phase deviation angle using the time-angle mathematical relationship. The phase dimension physical feature reflects the degradation of the synchronization of multi-contact collaborative work in real time. Physically, the larger the phase dimension physical feature, the more detailed the timing disorder of the contact action. The output unit is a continuous value in radians (range from 0 to 2π). For example, the phase dimension physical feature is ≈0 radians when ideal synchronization is achieved. When mechanical wear causes timing loss, the phase dimension physical feature increases to > the example critical radian value.

[0077] S3.2: Real-time pattern matching is performed in the decision rule base based on three-dimensional feature vectors, and three-level response decision results are dynamically generated according to three indicators: trend strength, fluctuation characteristics and anomaly frequency.

[0078] More specifically, real-time pattern matching is performed in the decision rule base based on three-dimensional feature vectors. The basic quadrant partition is determined by the trend intensity value sign, and the layers are nested according to the range of the fluctuation amplitude feature value. Finally, a three-level response decision is triggered by the frequency of anomaly occurrence: when the trend intensity value is in the steady state range, the fluctuation amplitude feature is in the low value range, and the anomaly occurrence frequency is 0 times, the first-level response "steady state maintenance" is output; when the trend intensity value is in the negative decay range, the fluctuation amplitude feature is in the middle value range, and the anomaly occurrence frequency is ≤3 times / minute, the second-level response "instantaneous interference recording" is output; when the trend intensity value is in the positive growth range, the fluctuation amplitude feature is in the high value range, and the anomaly occurrence frequency is >5 times / minute, the third-level response "continuous contact failure warning" is output; the matching period is ≤2 milliseconds, and the decision results are refreshed 200 times per second.

[0079] The decision rule base is built based on thousands of hours of historical data samples of electrical contact conditions. It includes rule entries consisting of trend intensity value interval division, fluctuation amplitude feature layering, and anomaly occurrence frequency classification. Each rule clearly defines the mapping relationship between the three-dimensional condition combination and the three-level response decision, and embeds dynamic correction parameters. The matching process scans all entries in the database in real time to perform parallel condition judgment.

[0080] S3.3: Based on the three-level response decision results, dynamically generate control commands to drive the actuators to perform safety diagnostics and adjustments, and simultaneously generate equipment operation logs.

[0081] More specifically, control commands are dynamically generated based on the three-level response decision results: when the decision rule base outputs a level one early warning response, a control command to activate the yellow warning light is generated; when a level two operation response is triggered, a command to proportionally adjust the load current is generated; when a level three alarm response is triggered, a circuit breaker tripping command and an audible and visual alarm drive signal are generated; all commands are transmitted to the actuator in real time via the industrial bus protocol; and equipment operation logs are generated simultaneously: recording timestamps, response level codes, execution action details, and copies of command parameters, with log entries stored in minute segments.

[0082] S4: Based on the equipment operation log, the overheated area is located by combining the Brillouin scattering principle, the change in dielectric loss of the insulating material is measured, and a diagnostic verification report is generated.

[0083] S4.1: Based on the abnormal time nodes and location identifiers recorded in the equipment operation log, a scan is performed along the target beam path through a photonic crystal fiber sensor network to generate a thermal map of the temperature field distribution.

[0084] Furthermore, based on the abnormal time nodes and location identifiers recorded in the equipment operation log, the photonic crystal fiber sensor network synchronously emits laser pulses towards the target beam path, and collects the backscattered Brillouin signal intensity and frequency shift in real time; demodulation points are deployed along the path with millimeter spatial resolution, and the frequency shift is converted into temperature values ​​through the Brillouin frequency shift-temperature mapping relationship, and the temperature dataset is organized according to the physical coordinates of the beam; abnormal areas are located according to the location identifiers, and oversampling is performed on the abnormal areas to reconstruct the temperature gradient, finally outputting a thermal map of the temperature field distribution.

[0085] S4.2: Based on the temperature field distribution thermogram, the overheated region is located using the Brillouin scattering principle. Simultaneously, the dielectric polarization response delay time of the insulating material is measured in the overheated region to generate the three-dimensional coordinates of the overheated region and the dielectric loss change index.

[0086] Furthermore, overheated regions are identified based on the thermal map of the temperature field distribution, and the center point of the overheated region is located based on the physical mechanism of Brillouin scattering: a continuous probe laser beam is emitted towards the overheated region, and the precise three-dimensional coordinates of the overheated region are calculated by capturing the change in Brillouin frequency shift; an alternating electric field is simultaneously applied at the three-dimensional coordinates, and the dielectric polarization response delay time of the insulating material is measured by the response current of the high-speed sampling electrode; the dielectric loss change index is calculated by the delay time-dielectric loss factor mapping relationship, and finally a diagnostic dataset containing the three-dimensional coordinates of the overheated region and the corresponding dielectric loss change index is generated.

[0087] The formula for calculating the dielectric loss variation index is:

[0088]

[0089] Where I represents the dielectric loss change index; Δtanδ represents the change in dielectric loss tangent; t1 represents the current polarization response delay time; and t2 represents the initial polarization response delay time.

[0090] S4.3: Integrate the three-dimensional coordinates of the overheated area, dielectric loss change indicators, and abnormal event records in the operation log, and generate a diagnostic verification report through spatiotemporal correlation analysis.

[0091] Furthermore, a spatiotemporal correlation analysis is performed by integrating the three-dimensional coordinates of the overheated area, the dielectric loss change index, and the abnormal event records in the operation log: First, the timestamps are aligned, and the three-dimensional coordinates of the overheated area are mapped to the harness position markers marked in the operation log; then, the spatial overlap between the dielectric loss change index exceeding the limit event and the response level decision results in the operation log is verified; finally, a diagnostic verification report is generated, which includes four parts: 1. Spatiotemporal matching table of abnormal events; 2. Thermal-electric correlation analysis conclusions; 3. Failure root cause determination; 4. Verification status marking.

[0092] The abnormal event record is a structured data entry in the operation log that accurately marks the trigger time of the three-level response decision, including the timestamp, location identifier, response level code, execution action code, dynamic criterion set of response decision, and snapshot of load and environment parameters, as well as the verification status updated by spatiotemporal correlation analysis. Its millisecond-level timestamp is serialized with photon scanning and the location identifier is mapped to the physical topology.

[0093] S5: Based on the diagnostic verification report and decision results, dynamically optimize the neural pathway connection weights and generate a bundle performance report.

[0094] S5.1: Based on the diagnostic verification report and the three-level response decision results, perform spatiotemporal alignment of data through the neural pathway optimization engine to generate a fusion analysis dataset;

[0095] Furthermore, based on the spatiotemporal matching table in the diagnostic verification report and the execution action records of the three-level response decision results, the neural pathway optimization engine initiates data spatiotemporal alignment: first, a millisecond-level timestamp alignment channel is established to synchronously correct the spatial mapping relationship; multidimensional data is fused through a dynamic priority weighting algorithm, and the dynamic criterion set of the response decision of the three-level response decision results and the verification conclusion of the diagnostic verification report generate cross-validation fields; finally, a fusion analysis dataset is output, which includes a unified timestamp, spatial topology encoding, weighted dielectric loss change index, response action encoding, decision threshold and measured value deviation fusion field, and the dataset is stored as a matrix table in ascending order of timestamp.

[0096] S5.2: Based on the fusion analysis dataset, the neural pathway connection weights are adjusted, and the sensitivity threshold is dynamically adjusted according to the rate of change of dielectric loss to generate an updated neural pathway weight mapping table.

[0097] Furthermore, based on the time series of weighted dielectric loss change indicators in the fusion analysis dataset, the neural pathway optimization engine performs Hebbe rule weight adjustment: selects the corresponding neural pathway connection according to the spatial encoding of the target location, calculates the gradient value of the weighted dielectric loss change indicator at adjacent time points, and applies incremental adjustment based on the gradient sign and magnitude; simultaneously, it dynamically adjusts the sensitivity threshold according to the instantaneous change rate of the weighted dielectric loss change indicator; finally, it generates an updated neural pathway weight mapping table, which contains three fields: location encoding, updated connection weight, and sensitivity threshold.

[0098] The sensitivity threshold is a critical parameter for dynamically regulating the triggering conditions of neural pathways. Its dimension is the physical unit of the derivative of the weighted dielectric loss change index, and its initial value is set according to the material safety specification benchmark.

[0099] Dynamic adjustment follows the formula:

[0100]

[0101] Among them, S ' Indicates the updated sensitivity threshold; S min S1 represents the minimum guaranteed safety sensitivity value; S2 represents the current sensitivity threshold (0.1~0.8). represents the adaptive gain coefficient; i represents the instantaneous rate of change of the weighted dielectric loss index; Indicates the rate of change of the reference.

[0102] S5.3: Integrate the neural pathway weight mapping table with the diagnostic verification report, construct a performance topology map through three-dimensional performance modeling, extract the spatial structure data of the performance topology map, and generate a harness performance report.

[0103] Furthermore, by integrating the location codes, updated connection weights, and verification conclusion fields from the diagnostic verification report in the neural pathway weight mapping table, three-dimensional performance modeling is performed: First, the location codes are converted into three-dimensional points in physical space, and the updated connection weight values ​​from the neural pathway weight mapping table are used as performance strength coefficients; then, the measured values ​​of dielectric loss change indicators from the diagnostic verification report are superimposed, and a material degradation coefficient distribution surface is established according to spatial coordinates; a weighted performance index is injected into the spatially segmented data field to construct a performance topology map; finally, four types of spatial structure data are extracted from the performance topology map to generate a harness performance report containing the following fields: location topology code, current performance index, performance degradation rate, historical reference difference, spatial critical marker, and associated action code.

[0104] The spatial structure data of the performance topology map contains four core elements: three-dimensional coordinates of the peak degradation region, performance gradient change vector, weight mutation node set, and spatial topology connectivity path. The four types of data strictly inherit the position encoding of the neural pathway weight mapping table and the coordinate accuracy of the diagnostic verification report. The peak coordinates are associated with the physical failure point of the bundle, the performance gradient vector indicates the direction of degradation diffusion, the weight mutation node corresponds to the mechanical stress concentration area, and the connectivity path quantifies the vulnerability.

[0105] This embodiment also provides an automotive wiring harness parameter performance testing system, including:

[0106] The resistance change rate generation module is used to calculate the contact resistance value based on the principle of quantum tunneling effect and continuously generate a high-precision micro resistance change rate data stream.

[0107] The electrical pulse signal extraction module is used to extract the trend intensity, fluctuation characteristics and abnormal frequency of resistance change based on the high-precision micro-resistance change rate data stream, and fuse them to generate a three-dimensional feature electrical pulse signal.

[0108] The control command conversion module is used to convert decision results into control commands based on three-dimensional characteristic electrical pulse signals to drive the actuator to perform safety diagnosis and control, and generate equipment operation logs;

[0109] The dielectric loss measurement module is used to locate overheated areas based on equipment operation logs and the Brillouin scattering principle, measure changes in dielectric loss of insulating materials, and generate diagnostic verification reports.

[0110] The neural pathway weight optimization module is used to dynamically optimize the neural pathway connection weights based on diagnostic verification reports and decision results, and generate a bundle performance report.

[0111] This embodiment also provides a computer device applicable to the automotive wiring harness parameter performance testing method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the automotive wiring harness parameter performance testing method proposed in the above embodiment.

[0112] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0113] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the method for testing the performance parameters of automotive wiring harnesses as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0114] In summary, this invention achieves nanosecond-level capture of micro-ohm-level resistance jumps by: establishing a quantum tunneling response model through atomic layer deposition of a nano-insulating dielectric layer on the surface of the wire harness terminal, and combining it with a dynamic dielectric compensation algorithm to accurately analyze the instantaneous value of the contact current; furthermore, it employs a spiking neuron dynamic threshold mechanism to achieve intelligent extraction and compressed encoding of resistance change characteristics under complex operating conditions by adaptively adjusting the threshold control parameters, driving the three-level response decision to complete fault classification judgment and execution within a millisecond-level time window.

[0115] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for testing the performance of a wire harness parameter of an automobile, characterized by: The application relates to a high-precision micro-resistance change rate data stream generation method based on quantum tunneling effect principle. According to the quantum tunneling effect principle, the contact resistance value is calculated, and a high-precision micro-resistance change rate data stream is continuously generated, and the specific steps are as follows, A nanoscale insulating medium layer is constructed, and an electron tunneling current response curve is captured in real time, and a voltage and current original data set is generated; Based on the quantum tunneling effect principle, the voltage and current original data set is analyzed, environmental interference is dynamically compensated, the contact electric instantaneous value is calculated, and a time series resistance array is generated; The time series resistance array is subjected to sliding window differential processing, high-frequency noise interference is eliminated, and a high-precision micro-resistance change rate data stream is generated; Based on the high-precision micro-resistance change rate data stream, the trend strength, fluctuation characteristics and abnormal frequency of resistance change are extracted, and a three-dimensional characteristic electric pulse signal is fused and generated; According to the three-dimensional characteristic electric pulse signal, the decision result is converted into a control instruction to drive an execution mechanism to execute safety diagnosis regulation and control, and a device operation log is generated; Based on the device operation log, a overheated area is located based on the Brillouin scattering principle, the dielectric loss change of insulating material is measured, and a diagnosis verification report is generated; According to the diagnosis verification report and the response decision result, the neural pathway connection weight is dynamically optimized, and a wire harness performance report is generated.

2. The method of claim 1, wherein: Based on the high-precision micro-resistance change rate data stream, the trend strength, fluctuation characteristics and abnormal frequency of resistance change are extracted, and the specific steps are as follows, Based on the high-precision micro-resistance change rate data stream, the data conversion from the resistance change amount to the action potential is performed, the pulse sequence and the real-time updated threshold control parameter are generated; According to the pulse sequence and the real-time updated threshold control parameter, the trend strength analysis, fluctuation characteristic capture and abnormal frequency statistics of resistance change are performed, and three characteristic indexes are integrated to generate.

3. The method of claim 2, wherein: The three characteristic indexes are converted into electric pulses, and a three-dimensional characteristic electric pulse signal is fused and generated.

4. The method of claim 3, wherein: According to the three-dimensional characteristic electric pulse signal, the decision result is converted into a control instruction to drive an execution mechanism to execute safety diagnosis regulation and control, and a device operation log is generated, and the specific steps are as follows, According to the three-dimensional characteristic electric pulse signal, the circuit is analyzed, and the physical characteristics of the three dimensions of the pulse are synchronously extracted, and a three-dimensional characteristic vector is generated; Based on the three-dimensional characteristic vector, real-time pattern matching is performed in the decision rule library, three-level response decision results are dynamically generated according to the trend strength, fluctuation characteristics and abnormal frequency of the three indexes; According to the three-level response decision result, the control instruction is dynamically generated to drive the execution mechanism to execute safety diagnosis regulation and control, and the device operation log is synchronously generated.

5. The method of claim 4, wherein: Based on the device operation log, a overheated area is located based on the Brillouin scattering principle, the dielectric loss change of insulating material is measured, and a diagnosis verification report is generated, and the specific steps are as follows, Based on the abnormal time nodes and position marks recorded in the device operation log, scanning is performed along the target wire harness path, and a temperature field distribution thermal map is generated; According to the temperature field distribution thermal map, the overheated area is located, the insulating material medium polarization response delay time is synchronously measured in the overheated area, and a three-dimensional coordinate and dielectric loss change index of the overheated area are generated; The three-dimensional coordinate of the overheated area, the dielectric loss change index and the abnormal event record in the operation log are integrated to generate a diagnosis verification report.

6. The method of claim 5, wherein: According to the diagnostic verification report and the decision result, the neural pathway connection weight is dynamically optimized, and a wire harness performance report is generated. The specific steps are as follows, According to the diagnostic verification report and the decision result, the neural pathway connection weight is dynamically optimized, and a wire harness performance report is generated. The specific steps are as follows, According to the diagnostic verification report and the decision result, the neural pathway connection weight is dynamically optimized, and a wire harness performance report is generated. The specific steps are as follows, Integrate the neural pathway weight mapping table and the diagnostic verification report, construct the performance topology graph, extract the spatial structure data of the performance topology graph, and generate the wire harness performance report.

7. A system for testing the performance of parameters of an automobile wiring harness, based on the method for testing the performance of parameters of an automobile wiring harness according to any one of claims 1 to 6, characterized in that: It comprises, The resistance change rate generation module is used for calculating the contact resistance value according to the quantum tunneling effect principle, and continuously generating high-precision micro-resistance change rate data stream; The electric pulse signal extraction module is used for extracting the trend strength, fluctuation characteristics and abnormal frequency of resistance change based on the high-precision micro-resistance change rate data stream, and fusing to generate a three-dimensional characteristic electric pulse signal; The control instruction conversion module is used for converting the decision result into a control instruction to drive the actuator to execute safety diagnosis and control according to the three-dimensional characteristic electric pulse signal, and generating a device operation log; The dielectric loss measurement module is used for positioning the overheating area based on the device operation log and combining the Brillouin scattering principle, measuring the dielectric loss change of the insulating material, and generating a diagnostic verification report; The neural pathway weight optimization module is used for dynamically optimizing the neural pathway connection weight according to the diagnostic verification report and the decision result, and generating a wire harness performance report.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that: The processor executes the computer program to realize the steps of the automobile wire harness parameter performance test method in any one of claims 1-6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to realize the steps of the automobile wire harness parameter performance test method in any one of claims 1-6.

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