Automobile wire harness parameter performance test method and system

Abstract

The invention discloses an automobile wire harness parameter performance test method and system, and relates to the field of intelligent monitoring and predictive protection of electrical equipment, and the method comprises the steps: extracting the trend intensity, fluctuation characteristics and abnormal frequency of resistance change based on a high-precision micro-resistance change rate data stream, and carrying out the fusion to generate a three-dimensional characteristic electric pulse signal; according to the three-dimensional characteristic electric pulse signal, a decision result is converted into a control instruction to drive an execution mechanism to execute safety diagnosis regulation and control, and an equipment operation log is generated; based on an equipment operation log, positioning an overheat area by combining a Brillouin scattering principle, measuring dielectric loss change of an insulating material, and generating a diagnosis verification report; according to the diagnosis verification report and the decision result, the neural pathway connection weight is dynamically optimized, and a wire harness performance report is generated; intelligent extraction and compression coding of resistance change characteristics under complex working conditions are realized by adaptively adjusting threshold control parameters, and three-level response decisions are driven to complete fault grading judgment and execution in a millisecond-level time window.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-parameter testing and protection decision-making of automobile wiring harnesses, in particular to a method and system for testing parameter performance of automobile wiring harnesses. Background Art

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

[0003] First, at the detection principle level, the conventional resistance sampling method is affected by the potential barrier effect of the oxide film on the contact surface, and its ohmic contact model seriously deviates from the actual electron transport behavior at the micro-nano scale; secondly, in the data processing architecture, the fixed-threshold alarm mechanism is difficult to adapt to complex working conditions, especially when mechanical vibration and sudden temperature change multiple physical fields are coupled, and the contact resistance abnormality judgment benchmark cannot be dynamically adjusted. Summary of the Invention

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

[0005] Therefore, the present invention provides a method for testing the parameter performance of an automobile wiring harness to solve the problems of missed detection of micro-ohm resistance jump events caused by the potential barrier effect of the contact interface and delayed response to insulation failure warnings under complex working conditions.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for testing the parameter performance of an automotive wiring harness, which comprises 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 the 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 result is converted into a control instruction to drive the actuator to perform safety diagnosis and control, and generate a device operation log;

[0010] Based on the equipment operation log, combined with the Brillouin scattering principle, the overheating area is located, the dielectric loss change of the insulation material is measured, and a diagnostic verification report is generated;

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

[0012] As a preferred solution of the automotive wiring harness parameter performance test method of the present invention, wherein: the contact resistance value is calculated according to the principle of quantum tunneling effect, and a high-precision micro-resistance change rate data stream is continuously generated. The steps are as follows:

[0013] Construct a nanoscale insulating dielectric layer, capture the electron tunneling current response curve in real time, and generate voltage and current raw data sets;

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

[0015] Sliding window differential processing is performed on the time series resistor array to eliminate high-frequency noise interference and generate high-precision micro-resistance change rate data stream.

[0016] As a preferred solution of the automotive wiring harness parameter performance test method of the present invention, wherein: based on the high-precision micro-resistance change rate data stream, the trend intensity, fluctuation characteristics and abnormal frequency of resistance change are extracted, the steps are as follows:

[0017] Based on the high-precision micro-resistance change rate data stream, the data conversion from resistance change to action potential is performed to generate pulse trains and threshold control parameters that are updated in real time;

[0018] Based on the pulse sequence and real-time updated threshold control parameters, the trend intensity analysis of resistance changes, fluctuation feature capture and abnormal frequency statistics are performed, and three characteristic indicators are integrated to generate.

[0019] As a preferred solution of the automotive wiring harness parameter performance test method of the present invention, wherein: the fusion generates a three-dimensional characteristic electric pulse signal, the steps are as follows:

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

[0021] As a preferred solution of the automotive wiring harness parameter performance test method of the present invention, wherein: according to the three-dimensional characteristic electric pulse signal, the decision result is converted into a control instruction to drive the actuator to perform safety diagnosis and regulation, and generate a device operation log, the steps are as follows:

[0022] According to the three-dimensional characteristic electrical pulse signal, the circuit is analyzed and the physical characteristics of the three dimensions of the pulse are extracted synchronously to generate a three-dimensional feature vector;

[0023] Based on the three-dimensional feature vector, real-time pattern matching is performed in the decision rule library, and three-level response decision results are dynamically generated according to the three indicators of trend strength, fluctuation characteristics and abnormal frequency;

[0024] Based on the three-level response decision results, control instructions are dynamically generated to drive the actuator to perform safety diagnosis and regulation, and equipment operation logs are generated simultaneously.

[0025] As a preferred solution of the automotive wiring harness parameter performance test method of the present invention, wherein: based on the equipment operation log, combined with the Brillouin scattering principle to locate the overheating area, measure the dielectric loss change of the insulation material, and generate a diagnostic verification report, the steps are as follows:

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

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

[0028] Integrate the 3D coordinates of the overheating area, dielectric loss change indicators and abnormal event records in the operation log to generate a diagnostic verification report.

[0029] As a preferred embodiment of the automotive wiring harness parameter performance test method of the present invention, the following steps are performed to dynamically optimize the neural pathway connection weights and generate a wiring harness performance report based on the diagnostic verification report and the decision result:

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

[0031] Trigger neural pathway connection weight adjustments based on the fusion analysis data set, dynamically adjust the sensitivity threshold according to the dielectric loss change rate, and generate an updated neural pathway weight mapping table;

[0032] Integrate the neural pathway weight mapping table and the diagnostic verification report, construct a performance topology map, extract the spatial structure data of the performance topology map, and generate a harness performance report.

[0033] In a second aspect, the present invention provides a vehicle 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 high-precision micro-resistance change rate data stream;

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

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

[0037] The dielectric loss measurement module is used to locate overheating areas based on equipment operation logs and the Brillouin scattering principle, measure changes in dielectric loss of insulation 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 the diagnosis verification report and decision results, and generate a harness performance report.

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

[0040] In a fourth aspect, 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, any step of the automotive wiring harness parameter performance testing method as described in the first aspect of the present invention is implemented.

[0041] The beneficial effects of the present invention are as follows: a quantum tunneling response model is established by atomically depositing a nano-insulating dielectric layer on the surface of the wiring harness terminal, and the instantaneous value of the contact electricity is accurately analyzed in combination with a dynamic dielectric compensation algorithm, thereby achieving nanosecond capture of micro-ohm resistance jumps; further, a pulse neuron dynamic threshold mechanism is adopted to achieve intelligent extraction and compression encoding of resistance change characteristics under complex working conditions by adaptively adjusting threshold control parameters, thereby driving the three-level response decision to complete fault classification judgment and execution within a millisecond time window. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 The figure is a flow chart of the automotive wiring harness parameter performance test method.

[0044] Figure 2 Flowchart of the workings of the dynamic threshold mechanism for spiking neurons.

[0045] Figure 3 Flowchart generated for three-level response decisions.

[0046] Figure 4Flowchart for neural pathway optimization and harness performance reporting. DETAILED DESCRIPTION

[0047] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0048] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0049] Secondly, the term "one embodiment" or "embodiment" 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 various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0050] Reference Figures 1 to 4 , is an embodiment of the present invention, which provides a method for testing the parameter performance of an automobile wiring harness, comprising the following steps:

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

[0052] S1.1: Build a nanoscale insulating dielectric layer using atomic layer deposition, capture the electron tunneling current response curve in real time, and generate raw voltage and current data sets;

[0053] More specifically, an atomic layer deposition process is implemented on the surface of the wiring harness terminal, and precursor gas and reaction gas are alternately introduced layer by layer to generate a nanoscale insulating dielectric layer of specified composition; a continuously changing scanning voltage sequence is applied on both sides of the insulating dielectric layer, and a high-sensitivity current sensor is used to capture in real time the tunneling current response formed by electrons passing through the insulating dielectric layer under the corresponding scanning voltage; the corresponding current measurement values when negative scanning voltage, zero scanning voltage, and positive scanning voltage are applied are fully recorded, and finally a voltage and current raw data set containing the scanning voltage sequence and tunneling current response is generated.

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

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

[0056] S1.3: Perform sliding window differential processing based on the time series resistor array, eliminate high-frequency noise interference through digital filtering algorithm, and generate high-precision micro-resistance change rate data stream.

[0057] More specifically, a sliding window differential processing is performed on the time series resistor array: an analysis window of a specific time length is set to move with a fixed step size, and the differential coefficient of the resistance value is calculated point by point using the central difference method; the Butterworth low-pass filtering algorithm is applied to the differential 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, which strictly inherits the timestamp system of the original time series resistor array, and the differential value is measured in ohms / second. For example, a millisecond sampling interval corresponds to a millisecond micro-resistance change rate resolution.

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

[0059] S2.1: Based on the high-precision micro-resistance change rate data stream, the pulse neuron dynamic threshold mechanism is used to convert the resistance change to action potential data, generating a pulse train and real-time updated threshold control parameters;

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

[0061] The dynamic sensitivity threshold, a dynamic benchmark with real-time adaptive capabilities, precisely regulates pulse trigger sensitivity through a closed-loop feedback mechanism. During the initialization phase, a predefined initialization threshold is applied. During the operation of the spiking neuron dynamic threshold mechanism, when the accumulated voltage driven by the high-precision microresistance change rate data stream exceeds the current dynamic sensitivity threshold, the threshold is immediately exponentially reduced by the dynamic desensitization factor τ. If no trigger event is detected, the threshold level is continuously increased in increments of 0.5mV / ms. A minimum protection threshold of 5mV is set to prevent unlimited sensitivity increases, while a maximum noise tolerance threshold of 500mV is set to mitigate overload from environmental interference. Its dynamic evolution is characterized by the following: when environmental interference intensifies, the dynamic sensitivity threshold actively increases to suppress false triggering; when weak signals appear, the dynamic sensitivity threshold rapidly decreases to enhance detection sensitivity. Ultimately, the dynamic sensitivity threshold directly determines the pulse train density and is updated synchronously with pulse events, becoming a real-time dynamic indicator representing sensitivity and environmental noise levels.

[0062] S2.2: Based on the pulse sequence and the real-time updated threshold control parameters, perform resistance change trend intensity analysis, fluctuation feature capture, and abnormal frequency statistics, integrating and generating three characteristic indicators;

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

[0064] The analysis of the trend intensity of resistance change is to generate a pulse density time series based on the real-time statistics of the number of pulses in a fixed time unit of the pulse sequence, perform first-order differential processing on the time axis of the pulse density time series to calculate the pulse density change gradient, use linear regression fitting of the pulse density gradient sequence to extract the slope value, and use the real-time updated dynamic sensitivity threshold as the sensitivity correction factor to perform normalization calibration on the slope value. Finally, the scalar trend intensity value that quantifies the direction and rate of resistance change is output. Positive values represent continuous increase in resistance, negative values represent resistance attenuation, and floating near zero values are judged as steady state.

[0065] The fluctuation feature capture is to extract the time intervals between adjacent pulses from the pulse sequence to form an ordered pulse interval sequence, calculate the arithmetic mean of the pulse interval sequence to represent the basic fluctuation frequency, obtain the standard deviation of the interval sequence to quantify the absolute fluctuation amplitude, and generate the coefficient of variation as the normalized relative volatility; the dynamic sensitivity threshold updated in real time is used as a dynamic weighting factor to correct the fluctuation amplitude; the final physical mapping rule of the fluctuation amplitude feature is: the low value interval corresponds to the stable contact state, the median value interval reflects the normal operation fluctuation, and the high value interval warns of the abnormal connection risk.

[0066] Among them, the abnormal frequency statistics is to identify high-density pulse events in the pulse sequence through time window scanning: first, the upper limit of the pulse interval within the event, the event pulse interval threshold and the lower limit of the continuous pulse number are set. When the continuous pulse group meets the maximum interval that does not exceed the event judgment maximum interval and contains at least the continuous lower limit number of pulses, it is determined to be a valid event; the events are counted in fixed time units, and the abnormal occurrence frequency is dynamically corrected through the real-time updated dynamic sensitivity threshold; finally, the event frequency per unit time is output as the abnormal occurrence frequency, and a zero value indicates stability without abnormalities, a low frequency corresponds to acceptable instantaneous interference, and a high frequency warns of continuous contact failure risks.

[0067] S2.3: The three characteristic indicators are converted into electrical pulses through the physical property translation mechanism, and the three-dimensional characteristic electrical pulse signals are generated by memristor array fusion.

[0068] Furthermore, electric pulse conversion and fusion are performed based on three characteristic indicators: first, the trend intensity value is converted into an electric pulse signal in the amplitude dimension through the physical characteristic translation mechanism, the fluctuation amplitude characteristic is converted into a pulse sequence in the frequency dimension, and the frequency of abnormal occurrence triggers phase dimension modulation; the electric pulse signals after the three indicators are converted 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 synthetic waveform is output through the charge integration effect; finally, a three-dimensional characteristic electric pulse signal carrying the amplitude dimension, frequency dimension and phase dimension is generated at the output end 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 a control instruction to drive the actuator to perform safety diagnosis and control, and generate a device operation log;

[0070] S3.1: Based on the three-dimensional characteristic electrical pulse signal, the pulse physical characteristics parsing circuit of the behavior analysis engine is used to simultaneously extract the physical characteristics of the pulse in three dimensions and generate a three-dimensional feature vector;

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

[0072] The three-dimensional physical characteristics include amplitude dimension physical characteristics, frequency dimension physical characteristics and phase dimension physical characteristics;

[0073] Among them, the amplitude dimension physical characteristics characterize the voltage intensity change characteristics of the electric pulse signal. The processing process is: input the amplitude component of the original three-dimensional characteristic electric pulse signal, and subtract the baseline drift component in real time through the sliding window difference algorithm. The output signal peak is stable within the preset voltage range; the amplitude dimension physical characteristic directly quantifies the transient contact voltage fluctuation amplitude between conductors. The physical manifestation is that the larger the amplitude dimension physical characteristics, the higher the energy of the contact resistance mutation event; the final output unit is a scalar value in millivolts. For example, when the contact is in a stable state, the amplitude dimension physical characteristics maintain an example low millivolt value range, and jump to an example high millivolt value 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's steady-state contact, and the upper limit accommodates the contact arc peak. The typical range is set to -10V to +10V. Through the real-time benchmark correction mechanism and hardware protection, it is ensured that the steady-state working condition achieves millivolt-level resolution, the fault state fully captures the volt-level jump, and the range boundary strictly matches the conductor-insulation layer breakdown physical model.

[0075] Among them, the frequency dimension physical characteristics analyze the spectral energy distribution characteristics of the electric pulse signal. The processing process is: receive the refined signal after amplitude dimension processing, perform fast Fourier transform to calculate the frequency domain energy distribution, and extract the energy proportion of the main frequency band; specifically extract the example percentage bandwidth energy integral value around the peak frequency point, and output the relative energy percentage; the frequency dimension physical characteristic quantity directly maps the duration characteristics of the discharge event, and the physical manifestation is that the higher the frequency dimension physical characteristics, the shorter the time scale of the pulse event; the output range is a pure numerical value of 0-100%. For example, during corona discharge, the frequency dimension physical characteristics are concentrated in the example medium frequency band, and during arc discharge, they are transferred to the example high frequency band.

[0076] The physical characteristics of the phase dimension describe the timing deviation characteristics of the electrical pulse signal. The processing process is: deploy a voltage comparator circuit at the zero-crossing point of the three-dimensional characteristic electrical pulse signal, measure the time difference between the triggering moments of the rising edges of adjacent pulses, and apply the time-angle mathematical relationship to map the phase deviation angle; the physical characteristic quantity of the phase dimension reflects the synchronization degradation of the collaborative work of multiple contacts in real time. The physical manifestation is that the larger the physical characteristics of the phase dimension, the more detailed the timing disorder of the contact action; the output unit is a continuous value of radians (range from 0 to 2π). For example, in ideal synchronization, the physical characteristics of the phase dimension are ≈0 radians. When mechanical wear causes timing desynchronization, the physical characteristics of the phase dimension increase to > the example critical radian value.

[0077] S3.2: Perform real-time pattern matching in the decision rule library based on the three-dimensional feature vector, and dynamically generate three-level response decision results based on three indicators: trend strength, fluctuation characteristics, and abnormal frequency;

[0078] More specifically, real-time pattern matching is performed in the decision rule library based on the three-dimensional feature vector. The basic quadrant partition is determined by the trend strength value symbol, and the layers are nested according to the range of the fluctuation amplitude characteristic value. Finally, the three-level response decision is triggered by the frequency of abnormal occurrence: when the trend strength value is in the steady-state range, the fluctuation amplitude characteristic is in the low value range, and the abnormal occurrence frequency = 0 times, the first-level response "steady-state maintenance" is output; when the trend strength value is in the negative attenuation zone, the fluctuation amplitude characteristic is in the median range, and the abnormal occurrence frequency is ≤3 times / minute, the second-level response "instantaneous interference record" is output; when the trend strength value is in the positive growth zone, the fluctuation amplitude characteristic is in the high value range, and the abnormal occurrence frequency is >5 times / minute, the third-level response "continuous contact failure warning" is output; the matching cycle is ≤2 milliseconds, and the decision result is refreshed 200 times per second.

[0079] The decision rule library is constructed based on historical data samples of electrical contact working conditions at the thousand-hour level, and includes rule entries composed of trend intensity value interval division, fluctuation amplitude characteristic stratification and abnormal occurrence frequency classification; each rule clearly defines the mapping relationship between three-dimensional condition combinations and three-level response decisions, and embeds dynamic correction parameters; the matching process scans all library entries in real time to execute parallel condition judgments.

[0080] S3.3: Based on the third-level response decision results, dynamically generate control instructions to drive the actuator to perform safety diagnosis and regulation, and simultaneously generate equipment operation logs.

[0081] More specifically, control instructions are dynamically generated based on the three-level response decision results: when the decision rule library outputs a first-level early warning response, a control instruction for activating the yellow warning light is generated; when a second-level operation response is triggered, a proportional adjustment load current instruction is generated; when a third-level alarm response is triggered, a circuit breaker tripping instruction and an audible and visual alarm drive signal are generated; all instructions are transmitted to the actuator in real time through the industrial bus protocol; and a device operation log is generated synchronously: the timestamp, response level code, execution action details and a copy of the instruction parameters are recorded, and the log entries are stored in segments by minute.

[0082] S4: Based on the equipment operation log, combined with the Brillouin scattering principle, locate the overheating area, measure the dielectric loss change of the insulation material, and generate a diagnostic verification report;

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

[0084] Furthermore, based on the abnormal time nodes and location identifiers recorded in the equipment operation log, the photonic crystal fiber sensing network synchronously emits laser pulses to the target beam path, and collects the backward Brillouin scattering signal intensity and frequency shift in real time; demodulation points are arranged along the path with millimeter spatial resolution, and the frequency shift is converted into a temperature value through the Brillouin frequency shift-temperature mapping relationship, and the temperature data set is organized in the order of the physical coordinates of the beam; the abnormal area is located according to the location identifier, and the temperature gradient is reconstructed by oversampling in the abnormal area, and finally a temperature field distribution thermodynamic map is output.

[0085] S4.2: Based on the temperature field distribution thermogram, apply the Brillouin scattering principle to locate the overheating area. Simultaneously measure the dielectric polarization response delay time of the insulating material in the overheating area to generate the three-dimensional coordinates of the overheating area and the dielectric loss change index.

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

[0087] The calculation formula of dielectric loss change 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 overheating area, the dielectric loss change index, and the abnormal event records in the operation log, and generate a diagnostic verification report through spatiotemporal correlation analysis.

[0091] Furthermore, the three-dimensional coordinates of the overheating area, the dielectric loss change index, and the abnormal event records in the operation log are integrated to perform spatiotemporal correlation analysis: first, the timestamps are aligned and the three-dimensional coordinates of the overheating area are mapped to the harness position identifier marked in the operation log; then the spatial overlap of the dielectric loss change index exceeding the limit event and the operation log response level decision result is verified; finally, a diagnostic verification report is generated, which contains four parts: 1. Spatiotemporal matching table of abnormal events; 2. Conclusion of thermal-electrical correlation analysis; 3. Root cause determination of failure; 4. Verification status mark.

[0092] The abnormal event record is a timestamp, location identifier, response level code, execution action code, response decision dynamic criterion set, and load and environmental parameter snapshot in the operation log that accurately marks the moment when the third-level response decision is triggered, as well as a structured data entry of the verification status updated by spatiotemporal correlation analysis. Its millisecond-level timestamp is connected in series with photon scanning, and the location identifier maps the physical topology.

[0093] S5: Based on the diagnosis verification report and decision results, the neural pathway connection weights are dynamically optimized and a harness performance report is generated.

[0094] S5.1: Based on the diagnostic validation report and the tertiary response decision results, perform spatiotemporal alignment of data using 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 starts the spatiotemporal alignment of data: first, a millisecond-level timestamp alignment channel is established to synchronously correct the spatial mapping relationship; multi-dimensional data is fused through a dynamic priority weighted algorithm, and the response decision dynamic criterion set of the three-level response decision results and the verification conclusion of the diagnostic verification report generate a cross-check field; finally, a fusion analysis data set is output, which includes a unified timestamp, spatial topology coding, weighted dielectric loss change index, response action coding, and a fusion field of decision threshold and measured value deviation. The data set is stored as a matrix table in ascending order of timestamps.

[0096] S5.2: Triggering neural pathway connection weight adjustment based on the fusion analysis data set, and dynamically adjusting the sensitivity threshold according to the dielectric loss change rate to generate an updated neural pathway weight mapping table;

[0097] Furthermore, based on the time series of the weighted dielectric loss change index in the fusion analysis data set, the neural pathway optimization engine performs Hebbian rule weight adjustment: the corresponding neural pathway connection is selected according to the spatial encoding of the target position, the gradient value of the weighted dielectric loss change index at adjacent moments is calculated, and incremental adjustment is applied based on the gradient sign and amplitude; the sensitivity threshold is dynamically adjusted according to the instantaneous change rate of the weighted dielectric loss change index; and finally an updated neural pathway weight mapping table is generated, which contains three fields: position encoding, updated connection weight, and sensitivity threshold.

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

[0099] The dynamic adjustment follows the formula:

[0100]

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

[0102] S5.3: Integrate the neural pathway weight mapping table and 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, the position codes in the neural pathway weight mapping table, the updated connection weights and the verification conclusion fields of the diagnostic verification report are integrated to perform three-dimensional performance modeling: first, the position codes are converted into three-dimensional points in physical space, and the updated connection weight values in the neural pathway weight mapping table are associated as performance intensity coefficients; the measured values of the dielectric loss change indicators in the diagnostic verification report are superimposed, and a material degradation coefficient distribution surface is established according to spatial coordinates; the weighted performance index is injected into the spatial segmentation 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: position topology code, current performance index, performance degradation rate, historical reference difference, spatial critical mark, 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 area, performance gradient change vector, weight mutation node set and spatial topological 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 harness, 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 high-precision micro-resistance change rate data stream;

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

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

[0109] The dielectric loss measurement module is used to locate overheating areas based on equipment operation logs and the Brillouin scattering principle, measure changes in dielectric loss of insulation 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 the diagnosis verification report and decision results, and generate a harness performance report.

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

[0112] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0113] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for testing the parameter performance of an automotive wiring harness as proposed in the above embodiment; 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 read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk.

[0114] In summary, the present invention achieves nanosecond capture of micro-ohm resistance jumps by: establishing a quantum tunneling response model through atomic layer deposition of a nano-insulating dielectric layer on the surface of the wiring harness terminal, and combining it with a dynamic dielectric compensation algorithm to accurately analyze the instantaneous value of the contact electricity; further adopting a pulse neuron dynamic threshold mechanism, and adaptively adjusting the threshold control parameters to achieve intelligent extraction and compression encoding of resistance change characteristics under complex working conditions, driving the three-level response decision to complete fault classification judgment and execution within a millisecond 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 the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for testing the performance of automotive wiring harness parameters, characterized by: include, Calculate the contact resistance value based on the principle of quantum tunneling effect and continuously generate high-precision micro-resistance change rate data stream; Based on the 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; Based on the three-dimensional characteristic electrical pulse signal, the decision result is converted into a control instruction to drive the actuator to perform safety diagnosis and control, and generate a device operation log; Based on the equipment operation log, combined with the Brillouin scattering principle, the overheating area is located, the dielectric loss change of the insulation material is measured, and a diagnostic verification report is generated; Based on the diagnosis verification report and response decision results, the neural pathway connection weights are dynamically optimized and a harness performance report is generated.

2. The method for testing the performance of automotive wiring harness parameters according to claim 1, wherein: The contact resistance value is calculated based on the principle of quantum tunneling effect, and high-precision micro-resistance change rate data stream is continuously generated. The specific steps are as follows: Construct a nanoscale insulating dielectric layer, capture the electron tunneling current response curve in real time, and generate voltage and current raw data sets; Based on the principle of quantum tunneling effect, the original voltage and current data sets are analyzed, environmental interference is dynamically compensated, the instantaneous value of contact electricity is calculated, and a time series resistance array is generated; Sliding window differential processing is performed on the time series resistor array to eliminate high-frequency noise interference and generate high-precision micro-resistance change rate data stream.

3. The method for testing the performance of automotive wiring harness parameters according to claim 2, wherein: The method of extracting the trend strength, fluctuation characteristics and abnormal frequency of resistance change based on high-precision micro-resistance change rate data stream is as follows: Based on the high-precision micro-resistance change rate data stream, the data conversion from resistance change to action potential is performed to generate pulse trains and threshold control parameters that are updated in real time; Based on the pulse sequence and real-time updated threshold control parameters, the trend intensity analysis of resistance changes, fluctuation feature capture and abnormal frequency statistics are performed, and three characteristic indicators are integrated to generate.

4. The method for testing the performance of automotive wiring harness parameters according to claim 3, wherein: The fusion generates a three-dimensional characteristic electric pulse signal, performs electric pulse conversion on the three characteristic indicators, and fuses them to generate a three-dimensional characteristic electric pulse signal.

5. The method for testing the performance of automotive wiring harness parameters according to claim 4, wherein: According to the three-dimensional characteristic electric pulse signal, the decision result is converted into a control instruction to drive the actuator to perform safety diagnosis and regulation, and generate a device operation log. The specific steps are as follows: According to the three-dimensional characteristic electrical pulse signal, the circuit is analyzed and the physical characteristics of the three dimensions of the pulse are extracted synchronously to generate a three-dimensional feature vector; Based on the three-dimensional feature vector, real-time pattern matching is performed in the decision rule library, and three-level response decision results are dynamically generated according to the three indicators of trend strength, fluctuation characteristics and abnormal frequency; Based on the three-level response decision results, control instructions are dynamically generated to drive the actuator to perform safety diagnosis and regulation, and equipment operation logs are generated simultaneously.

6. The method for testing the performance of automotive wiring harness parameters according to claim 5, wherein: Based on the equipment operation log, combined with the Brillouin scattering principle to locate the overheating area, measure the dielectric loss change of the insulation material, and generate a diagnostic verification report, the specific steps are as follows: Based on the abnormal time nodes and location identifiers recorded in the equipment operation log, a scan is performed along the target harness path to generate a temperature field distribution heat map; Based on the temperature field distribution thermodynamic map, the overheating area is located, and the dielectric polarization response delay time of the insulating material is measured in the overheating area simultaneously to generate the three-dimensional coordinates of the overheating area and the dielectric loss change index; Integrate the 3D coordinates of the overheating area, dielectric loss change indicators and abnormal event records in the operation log to generate a diagnostic verification report.

7. The method for testing the performance of automotive wiring harness parameters according to claim 6, wherein: The steps of dynamically optimizing the neural pathway connection weights and generating a harness performance report based on the diagnostic verification report and decision results are as follows: Based on the diagnostic verification report and the three-level response decision results, perform data spatiotemporal alignment to generate a fusion analysis dataset; Trigger neural pathway connection weight adjustments based on the fusion analysis data set, dynamically adjust the sensitivity threshold according to the dielectric loss change rate, and generate an updated neural pathway weight mapping table; Integrate the neural pathway weight mapping table and the diagnostic verification report, construct a performance topology map, extract the spatial structure data of the performance topology map, and generate a harness performance report.

8. An automotive wiring harness parameter performance testing system, based on the automotive wiring harness parameter performance testing method according to any one of claims 1 to 7, characterized in that: include, 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 high-precision micro-resistance change rate data stream; The electric pulse signal extraction module is used to extract the trend intensity, fluctuation characteristics and abnormal frequency of resistance changes based on the high-precision micro-resistance change rate data stream, and fuse them to generate a three-dimensional characteristic electric pulse signal; The control instruction conversion module is used to convert the decision results into control instructions based on the three-dimensional characteristic electrical pulse signal to drive the actuator to perform safety diagnosis and regulation, and generate equipment operation logs; The dielectric loss measurement module is used to locate overheating areas based on equipment operation logs and the Brillouin scattering principle, measure changes in dielectric loss of insulation materials, and generate diagnostic verification reports; The neural pathway weight optimization module is used to dynamically optimize the neural pathway connection weights based on the diagnosis verification report and decision results, and generate a harness performance report.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the automobile wiring harness parameter performance testing method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the automobile wiring harness parameter performance testing method according to any one of claims 1 to 7 are implemented.

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