Method and system for efficiently detecting the conduction of a car wiring harness

Abstract

The application discloses a kind of high-efficiency detection automobile wire harness conduction method and system, it is related to automobile electronic testing technical field, including, based on passage configuration data file, inject constant direct current in the test passage of measured wire and calculate the conduction resistance of measured wire, obtain standardized resistance data matrix;Impose alternating current excitation signal on the test passage corresponding to standardized resistance data matrix, calculate each frequency point impedance and equivalent capacitance parameter, form impedance characteristic dataset;Standardized resistance data matrix, impedance characteristic dataset and fault positioning information are fused, form multi-dimensional conduction characteristic vector, and compare with normal conduction characteristic, generate conduction state report matrix;According to the detection report of each wire of conduction state report matrix output, realize the comprehensive quantitative evaluation of wire conduction state, improve the comprehensiveness, robustness and fault discrimination precision of detection.

Description

Technical Field

[0001] This invention relates to the field of automotive electronics testing technology, and in particular to a method and system for efficiently detecting the continuity of automotive wiring harnesses. Background Technology

[0002] With the increasing complexity of automotive electronic systems, automotive wiring harnesses, as the neural network of the vehicle's electrical architecture, have their continuity performance directly related to the functional safety and reliability of the entire vehicle. The detection technology for the continuity of automotive wiring harnesses mainly relies on traditional continuity testers or automated testing systems based on relay matrices. In recent years, some high-end testing platforms have introduced multi-channel switching matrices and software control logic, enabling batch testing of large quantities of wiring harnesses and improving testing efficiency. Research has been conducted to explore the introduction of impedance spectroscopy analysis or time domain reflection (TDR) technology to identify potential problems such as poor contact, micro-short circuits, or insulation degradation in wiring harnesses. Although existing technologies are relatively mature in basic continuity detection, they are still insufficient in terms of comprehensive judgment of conduction status when dealing with high-density, multi-branch, and long-distance modern automotive wiring harnesses. They usually rely on a single DC resistance threshold to determine whether conduction is possible, ignoring the changes in impedance characteristics of the conductor under different frequency excitations and the reflection characteristics caused by local defects. This makes it difficult to effectively identify "sub-healthy" states such as slow deterioration of contact resistance, local insulation damage, or micron-level fractures. Especially in complex electromagnetic environments or in the early stages of wiring harness aging, such problems often do not cause obvious continuity abnormalities, but may evolve into serious faults in subsequent use. Summary of the Invention

[0003] In view of the aforementioned existing problems, the present invention is proposed.

[0004] Therefore, this invention provides an efficient method for detecting the continuity of automotive wiring harnesses, solving the problem that a single resistance threshold cannot comprehensively reflect the true continuity state of the wiring harness under multi-frequency excitation and local defects.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for efficiently detecting the continuity of automotive wiring harnesses, comprising: identifying the coded information of the wiring harness under test, generating a template configuration file, controlling a multi-port electronic matrix switch to automatically switch to form a test path for the wire under test, and recording the path configuration data file; based on the path configuration data file, injecting a constant DC current into the test path of the wire under test and calculating the conduction resistance of the wire under test to obtain a standardized resistance data matrix; applying an AC excitation signal to the test path corresponding to the standardized resistance data matrix, calculating the impedance and equivalent capacitance parameters at each frequency point to form an impedance feature dataset; injecting a narrow pulse signal into the test path corresponding to the impedance feature dataset and acquiring the reflected waveform to obtain fault location information; performing feature fusion of the standardized resistance data matrix, the impedance feature dataset, and the fault location information to form a multi-dimensional continuity feature vector, and comparing it with normal continuity characteristics to generate a continuity status report matrix; and outputting a detection report for each wire according to the continuity status report matrix to obtain a wire detection report.

[0006] As a preferred embodiment of the efficient method for detecting automotive wiring harness continuity according to the present invention, the specific steps for identifying the encoding information of the automotive wiring harness under test and generating a template configuration file are as follows: Read the unique coding information of the automotive wiring harness under test, extract the wiring harness model, production batch and terminal number information, retrieve the corresponding standard connectivity table in the wiring harness database according to the wiring harness model information, and generate a standard connectivity matching result file; Based on the connectivity information in the standard connectivity matching result file, a detection task template is generated. The detection task template is then validated for format and path consistency, and a template configuration file is generated.

[0007] In a preferred embodiment of the efficient method for detecting automotive wiring harness continuity according to the present invention, the specific steps for recording the path configuration data file are as follows: The wire path table information in the template configuration file is parsed, and the starting port and ending port of each wire are mapped to the row and column nodes of the electronic matrix switch to form a matrix port mapping table. Based on the matrix port mapping table, channel switching instructions are executed sequentially according to the test task template to establish the test path of the conductor under test, and the path number and establishment time are recorded to generate the path configuration data file.

[0008] In a preferred embodiment of the efficient method for detecting automotive wiring harness continuity described in this invention, the specific steps for obtaining the standardized resistance data matrix are as follows: Based on the path configuration data file, the test parameters of the corresponding wire in the detection task template are called to generate constant current output configuration data. A constant current is injected into the path under test and a four-wire voltage measurement is performed to obtain the original voltage data. Based on the original voltage data, the on-resistance is calculated using Ohm's law and then standardized after temperature and length compensation. A matrix index is then established according to the port number to generate a standardized resistance data matrix.

[0009] In a preferred embodiment of the efficient method for detecting automotive wiring harness continuity according to the present invention, the steps for forming the impedance feature dataset are as follows: Based on the resistance characteristics of the conductor under test in the standardized resistance data matrix, AC excitation setting data is generated, and AC excitation signals are sequentially applied to the test path of the conductor under test corresponding to the standardized resistance data matrix. At the same time, the output voltage signal and input current signal are collected in real time to form the original frequency response data. Based on the original frequency response data, the amplitude ratio and phase difference of the voltage and current signals at each frequency are calculated to obtain the equivalent resistance and equivalent reactance components, and impedance spectrum data is generated with frequency as the index. Based on impedance spectrum data, the impedance amplitude, phase and equivalent capacitance parameters at all frequency points are normalized to establish the frequency-impedance-phase correspondence curve. Select the frequency band with phase abrupt change from the frequency-impedance-phase correspondence curve and perform multi-parameter fitting calculations to obtain frequency domain characteristic parameters; combine the frequency domain characteristic parameters into a frequency domain characteristic vector, and write it into the characteristic matrix in sequence according to the conductor number to generate an impedance characteristic dataset.

[0010] In a preferred embodiment of the efficient method for detecting automotive wiring harness continuity described in this invention, the specific steps for obtaining fault location information are as follows: Based on the average impedance value, phase delay and equivalent capacitance characteristics of each test path in the impedance characteristic dataset, pulse excitation configuration data is obtained, and a narrow pulse signal is injected into the test path corresponding to the impedance characteristic dataset. The pulse emission time is recorded and the echo signal is acquired to generate the original data file of the reflected waveform. Based on the original data file of the reflected waveform, the echo signal is filtered and baseline corrected, the time point corresponding to the reflection peak is identified, and the reflection delay and distance to the reflection point are calculated to generate a reflection delay analysis result file. Based on the reflection delay analysis results file, the reflection delay, reflection peak amplitude and waveform polarity of each test path are classified to distinguish different types of faults and generate fault location information.

[0011] In a preferred embodiment of the efficient method for detecting automotive wiring harness continuity described in this invention, the specific steps for forming a multi-dimensional continuity feature vector are as follows: Based on the standardized resistance data matrix, impedance feature dataset and fault location information, a corresponding multi-dimensional feature index table is established according to the conductor number. The resistance value, impedance feature parameters and fault distance information of the same conductor under different detection dimensions are aligned and normalized to form an initial fusion feature set. Feature weights are assigned to each parameter in the initial fusion feature set. The comprehensive weight coefficients are calculated based on the conductor resistance stability, impedance frequency band characteristic change rate, and reflection positioning confidence, and a weighted fusion matrix is ​​generated. The electrical continuity of each conductor is quantitatively evaluated based on a weighted fusion matrix. The comprehensive parameters of conduction integrity index, impedance consistency index and reflection anomaly ratio are calculated to form a multi-dimensional conduction feature vector.

[0012] As a preferred embodiment of the efficient method for detecting automotive wiring harness continuity described in this invention, the continuity status report matrix refers to comparing and analyzing multi-dimensional continuity feature vectors with normal continuity characteristic samples, identifying abnormal wire paths through a similarity judgment algorithm, generating continuity status comparison results, and generating the report matrix based on the judgment results in the continuity status comparison results.

[0013] In a preferred embodiment of the efficient method for detecting continuity in automotive wiring harnesses according to the present invention, the specific steps for obtaining the wire detection report are as follows: Based on the continuity status report matrix, the detection number, continuity status level, anomaly type and confidence information of each conductor are extracted sequentially according to the conductor number to generate a single-line detection result table. The single-line detection result table is then formatted and converted to obtain a standardized detection result file. The standardized test result files are classified according to the continuity status level, and corresponding test report templates are generated. The test number, continuity status level, anomaly type and confidence level information of each conductor are written into the test report template to generate a test report.

[0014] Secondly, this invention provides a system for efficiently detecting the continuity of automotive wiring harnesses, comprising: a configuration module for identifying the coded information of the wiring harness under test, generating a template configuration file, controlling the automatic switching of a multi-port electronic matrix switch to form a test path for the wire under test, and recording the path configuration data file; a measurement module for injecting a constant DC current into the test path of the wire under test based on the path configuration data file and calculating the conduction resistance of the wire under test to obtain a standardized resistance data matrix; an analysis module for applying an AC excitation signal to the test path corresponding to the standardized resistance data matrix, calculating the impedance and equivalent capacitance parameters at each frequency point, and forming an impedance feature dataset; a location module for injecting a narrow pulse signal into the test path corresponding to the impedance feature dataset and acquiring the reflected waveform to obtain fault location information; an evaluation module for feature fusion of the standardized resistance data matrix, the impedance feature dataset, and the fault location information to form a multi-dimensional continuity feature vector, comparing it with normal continuity characteristics, and generating a continuity status report matrix; and a reporting module for outputting a detection report for each wire based on the continuity status report matrix to obtain a wire detection report.

[0015] The beneficial effects of this invention are as follows: by applying an AC excitation signal to the test path corresponding to the standardized resistance data matrix, an impedance feature dataset is formed, which can effectively identify hidden defects such as poor contact and insulation degradation; by constructing a weighted multidimensional conduction feature vector, and through cross-dimensional data alignment, normalization and weight allocation, a comprehensive quantitative evaluation of the conductor conduction state is achieved, which improves the comprehensiveness, robustness and fault discrimination accuracy of the detection. Attached Figure Description

[0016] 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.

[0017] Figure 1 A flowchart illustrating a method for efficiently detecting continuity in automotive wiring harnesses.

[0018] Figure 2 A schematic diagram of a system for efficiently detecting continuity in automotive wiring harnesses.

[0019] Figure 3 A flowchart for forming an impedance characteristic dataset.

[0020] Figure 4 A flowchart for obtaining fault location information. Detailed Implementation

[0021] 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.

[0022] 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.

[0023] 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.

[0024] Reference Figures 1-4 This is one embodiment of the present invention, which provides a method for efficiently detecting continuity in automotive wiring harnesses, comprising the following steps: S1. Identify the coding information of the automotive wiring harness under test, generate a template configuration file, control the multi-port electronic matrix switch to automatically switch, form a test path for the wire under test, and record the path configuration data file.

[0025] S1.1. Read the unique coding information of the automotive wiring harness under test, extract the wiring harness model, production batch and terminal number information, retrieve the corresponding standard connectivity table in the wiring harness database according to the wiring harness model information, and generate a standard connectivity matching result file.

[0026] Specifically, the unique coding information of the vehicle wiring harness under test is read. The wiring harness model, production batch, and terminal number information are extracted sequentially from the wiring harness identification field contained in the unique coding information. The extracted wiring harness model information is matched item by item with the standard connectivity table stored in the existing wiring harness database to find the standard connectivity relationship data that completely corresponds to the wiring harness model information. Based on the mapping relationship between the wiring harness model information and the standard connectivity relationship data, a standard connectivity matching result file containing wire connection port number, wire sequence number, and connectivity path information is generated. The applicability of the wiring harness manufacturing version is confirmed by the production batch information, and the consistency of the connection port is verified by the terminal number information.

[0027] It should be noted that the wiring harness database originates from the wiring harness structure and connection data accumulated over a long period of time in the automotive manufacturing and testing processes. It was established by digitizing and organizing automotive wiring harness design data from different models, configurations, and production batches. During the automotive wiring harness design phase, wiring harness design drawings are generated based on the vehicle's electrical circuit schematic. These drawings include basic information such as wire numbers, connection port numbers, terminal types, and wire connection relationships. Computer-aided design software exports these drawings as standardized data files (e.g., XML or CSV format) to serve as the original data source for the wiring harness database. Wiring harness manufacturers establish corresponding production batch records for each batch of products based on manufacturing process parameters. After comparing wire color, wire diameter, shielding structure, and terminal crimping information with the wiring harness design drawings, the verified wiring harness connection relationship data is stored in the database to form a production-level standard connectivity table. Through continuity and impedance consistency testing of the actually produced wiring harnesses, the start and end ports and connectivity path data of each wire are collected and verified. The test results are verified and updated in the wiring harness database to ensure that the connectivity information in the database remains consistent with the actual production status. The wiring harness database undergoes multiple rounds of data verification and version management to form a standard connectivity data set covering different vehicle models, production batches, and design versions.

[0028] S1.2. Based on the connectivity information in the standard connectivity matching result file, generate a detection task template, perform format verification and path consistency verification on the detection task template, and generate a template configuration file.

[0029] Specifically, based on the connectivity information in the standard connectivity matching result file, the starting port number, ending port number, and corresponding wire number information of each wire are extracted. Using the connectivity information as the basic input, wire path tables are established sequentially according to the connection order of the wires and the detection logic order. All wire path tables are numbered and sorted according to the wire number and a detection task template is generated. The integrity of the file structure and field format in the detection task template is checked. By comparing the field names, data types and path index structure, it is verified whether the definition of each wire path in the detection task template conforms to the conduction relationship information in the standard connectivity matching result file. The connection path of each pair of start port number and end port number in the detection task template is compared one by one with the conduction path in the standard connectivity matching result file to confirm that the wire path direction, port number and connection quantity are completely consistent. It is also checked for duplicate paths, breakpoints or missing items. After all wire paths have passed the format verification and path consistency verification, the detection task template is saved in a standardized file format to form a template configuration file.

[0030] S1.3. Parse the wire path table information in the template configuration file and map the starting port and ending port of each wire to the row and column nodes of the electronic matrix switch to form a matrix port mapping table.

[0031] Specifically, the system reads the wire path table information from the template configuration file, parses each wire in the wire path table item by item, extracts the start port number and end port number of the wire in sequence, establishes the correspondence between wire ports according to the wire number order defined in the wire path table, maps the start port number to the row node position of the electronic matrix switch according to the start port number and the end port number to the column node position of the electronic matrix switch, and forms a complete matrix node mapping structure by establishing a bidirectional correspondence between the start port number and the row node, and between the end port number and the column node. The row and column coordinates of each set of starting port numbers and ending port numbers are uniquely verified to prevent duplicate port numbers or mapping conflicts. At the same time, the mapping relationship is recorded in the order of the conductor numbers to ensure that the port numbers in the mapping table correspond one-to-one with the matrix nodes. After the starting port numbers and ending port numbers of all conductors are mapped, the row node numbers, column node numbers and the corresponding conductor number information are summarized to generate a matrix port mapping table.

[0032] S1.4. Based on the matrix port mapping table, execute the channel switching instructions sequentially according to the test task template to establish the test path of the conductor under test, and record the path number and establishment time to generate the path configuration data file.

[0033] Specifically, following the wire detection order defined in the detection task template, the starting port number and ending port number information corresponding to each wire in the matrix port mapping table are read sequentially, and the corresponding row and column nodes of the starting port number and ending port number of the wire are used as the path switching target; According to the test order of the test task template, the channel switching command is executed one by one. By controlling the conduction status of row nodes and column nodes, the starting port number and the ending port number form a complete conduction path in the matrix structure, thereby establishing the test path of the conductor under test. During the execution of the channel switching command, the starting port number, ending port number, conductor number and switching action execution time of each path establishment are recorded. The path conduction is confirmed immediately after the path is formed. After the test path of each conductor is established, the conductor number, starting and ending port numbers, corresponding row and column node coordinates and path establishment time are written into the path configuration data file in the test order. After all conductor test paths are established and recorded, the path configuration data file is formed.

[0034] S2. Based on the path configuration data file, inject a constant DC current into the test path of the conductor under test and calculate the on-resistance of the conductor under test to obtain a standardized resistance data matrix.

[0035] S2.1. Based on the path configuration data file, call the test parameters of the corresponding wire in the test task template, generate constant current output configuration data, inject constant current into the path under test and perform four-wire voltage measurement to obtain the original voltage data.

[0036] Specifically, the system reads the wire number, start port number, and end port number information recorded in the path configuration data file, and then searches for the corresponding wire's test parameters in the test task template, including the constant current output value, voltage measurement range, and sampling duration. Based on the conductor path information defined in the test task template, connect the positive output terminal of the constant current to the starting port number recorded in the path configuration data file, and connect the negative output terminal of the constant current to the ending port number to form a complete current excitation path in the test path of the conductor under test. Set the constant current output value (e.g., 100mA in this example) and voltage limiting parameters to prevent short circuits. Start the constant current injection process and keep the current output stable. After the constant current in the test path of the conductor under test is stable, connect two independent voltage sampling lines at both ends of the same path and use the four-wire method to measure the voltage. During the measurement process, collect multiple sets of voltage measurement points and average the voltage values ​​of all measurement points to obtain an average voltage value with small voltage fluctuations that reflects the true conduction characteristics of the conductor. After the measurement is completed, match the average voltage value with the constant current output value of the corresponding conductor in the path configuration data file to generate raw voltage data containing conductor number, starting and ending port numbers, average voltage value, and current output value.

[0037] S2.2. Based on the original voltage data, the on-resistance is calculated using Ohm's law and then standardized after temperature and length compensation. A matrix index is established according to the port number to generate a standardized resistance data matrix.

[0038] Specifically, the system reads the conductor number, start port number, end port number, average voltage value, and constant current output value from the original voltage data. It then calculates the on-resistance value of each conductor under test according to Ohm's law. Based on the conductor number in the path configuration data file, the system retrieves the conductor length information from the harness database and uses the real-time temperature information in the detection environment to perform temperature and length compensation on the on-resistance value. Temperature compensation is achieved by correcting the rate of change of resistivity of the conductor material at different temperatures, while length compensation is achieved by converting the actual length of the conductor to the resistance value corresponding to a standard reference length (e.g., 1 meter in this example). The compensated on-resistance values ​​are standardized to make the resistance data of different conductors comparable under a unified dimension. Based on the start port number and end port number recorded in the path configuration data file for each conductor, a row and column index of the on-resistance matrix is ​​established. The standardized on-resistance values ​​are then filled into the corresponding row and column positions of the matrix to form a complete standardized resistance data matrix.

[0039] It should be noted that the expression for calculating the equivalent resistance of a conductor under standard conditions is: ; in, Indicates standardized resistance. This indicates the measured average voltage. Indicates constant current. Indicates the temperature coefficient of a conductor material. Indicates the measured temperature. Indicates reference temperature. Indicates the actual length of the conductor. Indicates the standard reference length of the conductor.

[0040] It should be noted that the temperature coefficient of conductor materials is specified in industry standards or material handbooks, such as the temperature coefficient of materials like copper and aluminum (for example, the temperature coefficient of copper at 20°C is 0.00393 / °C). The reference temperature specified in industry standards is usually 20°C. The standard reference length of conductors is specified in the harness design drawings, such as 1 m, 5 m, or specific engineering design values.

[0041] S3. Apply an AC excitation signal to the test path corresponding to the standardized resistance data matrix, calculate the impedance and equivalent capacitance parameters at each frequency point, and form an impedance characteristic dataset.

[0042] S3.1. Based on the resistance characteristics of the conductor under test in the standardized resistance data matrix, generate AC excitation setting data, and sequentially apply AC excitation signals to the test path of the conductor under test corresponding to the standardized resistance data matrix. At the same time, collect the output voltage signal and input current signal in real time to form the original frequency response data.

[0043] Specifically, the standardized resistance value of each conductor under test is read from the standardized resistance data matrix, and the frequency range, amplitude and step interval of the AC excitation signal are determined according to the resistance characteristics of the conductor. The AC excitation setting data is generated by combining the above parameters. Following the conductor testing sequence recorded in the testing task template, the corresponding test paths for the conductors under test are selected sequentially from the standardized resistance data matrix. The output of the AC excitation signal is connected to the starting port number of each test path, and the input of the AC excitation signal is connected to the ending port number. The AC excitation signal is output step by step according to the frequency range defined in the AC excitation setting data. During the output of the AC excitation signal at each frequency point, the output voltage signal on the test path of the conductor under test is acquired in real time through an independent voltage sampling channel. At the same time, the input current signal is acquired in real time through a current detection channel to ensure that the voltage sampling and current sampling processes are synchronized on the time axis. The output voltage signal and input current signal corresponding to each frequency point are organized according to the conductor number, starting and ending port numbers, and frequency order to generate original frequency response data containing multi-frequency voltage and current response information.

[0044] S3.2. Based on the original frequency response data, the amplitude ratio and phase difference of the voltage and current signals at each frequency point are calculated to obtain the equivalent resistance and equivalent reactance components, and impedance spectrum data is generated with frequency as the index.

[0045] Specifically, the output voltage signal and input current signal corresponding to each frequency point are read from the original frequency response data. For each frequency point, the ratio of the amplitude of the output voltage signal to the amplitude of the input current signal is calculated to obtain the impedance amplitude at that frequency point. At the same time, the phase difference between the voltage signal and the current signal on the time axis is calculated to distinguish the relative proportion between the resistance component and the reactance component in the impedance. Based on the calculation results of the amplitude ratio and phase difference, the impedance amplitude is used as the magnitude of the impedance vector, and the phase difference is used as the angle between the impedance vector and the real axis. Through trigonometric function decomposition of the impedance vector in the real axis and imaginary axis directions, the equivalent resistance component and the equivalent reactance component are obtained. A corresponding structure of frequency-equivalent resistance component-equivalent reactance component is established with frequency as the index. After the calculation is completed at all frequency points, the equivalent resistance component and equivalent reactance component corresponding to each frequency point are arranged in ascending order of frequency to generate impedance spectrum data containing conductor number, start port number, end port number, frequency point, equivalent resistance component, equivalent reactance component, and phase angle.

[0046] S3.3. Based on impedance spectrum data, the impedance amplitude, phase and equivalent capacitance parameters of all frequency points are normalized to establish the frequency impedance phase correspondence curve.

[0047] Specifically, the equivalent resistance component, equivalent reactance component, and phase angle information corresponding to each frequency point in the impedance spectrum data are read. For the impedance amplitude, phase angle, and equivalent capacitance parameters at different frequency points, the minimum-maximum normalization method or the mean normalization method is used to standardize the numerical range, so that each parameter can be compared within a unified scale. The mean impedance amplitude of all frequency points is used as the central reference. Abnormal fluctuation points are corrected by calculating the deviation of each frequency point from the mean, ensuring the smoothness and continuity of the normalization results. With frequency as the abscissa and the normalized impedance amplitude and phase angle as the ordinate parameters, a one-to-one correspondence between frequency, impedance, and phase is established in the frequency domain dimension, and the frequency-impedance-phase correspondence curve is formed in sequence form.

[0048] S3.4. Select the frequency band with phase change in the frequency-impedance-phase correspondence curve and perform multi-parameter fitting calculation to obtain frequency domain characteristic parameters; form a frequency domain characteristic vector from the frequency domain characteristic parameters and write it into the characteristic matrix according to the conductor number to generate an impedance characteristic dataset.

[0049] Specifically, the phase change trend in the frequency-impedance-phase correspondence curve is traversed. By calculating the phase difference between adjacent frequency points, phase abrupt change points are identified, and the abrupt change frequency range is determined. The abrupt change frequency range is used to clarify the frequency segment in which the conductor gradually transitions from resistive to reactive characteristics under AC excitation conditions. By traversing the frequency-impedance-phase correspondence curve point by point and calculating the phase difference between each pair of adjacent frequency points, a phase abrupt change point is obtained when the phase difference value increases significantly within a very short frequency span (e.g., the example value is a change of tens of degrees). A phase abrupt change interval is formed between multiple frequency points where rapid phase changes occur consecutively. This phase abrupt change interval is defined as a characteristic frequency band. Within each characteristic frequency band, the frequency... With the rate as the independent variable, impedance amplitude, and phase angle as the dependent variables, a multi-parameter fitting method (such as the least squares fitting method) is used to perform function fitting calculations on the impedance change curve to obtain a set of fitting parameters describing the frequency domain characteristics of the conductor. By extracting the fitting parameters of each characteristic frequency band, characteristic parameters such as phase change amplitude, impedance gradient change rate, equivalent capacitance change coefficient, and impedance phase coupling ratio are obtained. All characteristic parameters are arranged into a set of frequency domain characteristic parameters in a predetermined order. Using the conductor number as an index, the set of frequency domain characteristic parameters of the corresponding conductor is arranged sequentially into a frequency domain feature vector. The frequency domain feature vectors of all conductors are written into the feature matrix in numerical order to form an impedance feature dataset containing conductor number, frequency domain characteristic parameters, and their corresponding frequency band information.

[0050] S4. Inject a narrow pulse signal into the test path corresponding to the impedance characteristic dataset and collect the reflected waveform to obtain fault location information.

[0051] S4.1. Based on the average impedance value, phase delay and equivalent capacitance characteristics of each test path in the impedance characteristic dataset, obtain the pulse excitation configuration data, inject a narrow pulse signal into the test path corresponding to the impedance characteristic dataset, record the pulse emission time and collect the echo signal to generate the original data file of the reflected waveform.

[0052] Specifically, the average impedance value, phase delay, and equivalent capacitance characteristics of each test path in the impedance characteristic dataset are read. The impedance matching conditions required for the pulse signal are determined by analyzing the average impedance value. The pulse propagation time interval is calculated based on the phase delay, and key parameters such as the pulse rise width and pulse repetition period are determined by combining the equivalent capacitance characteristics, thereby generating pulse excitation configuration data. The pulse excitation configuration data is matched with the port correspondence of the test path. The pulse transmission task is executed sequentially according to the path number in the impedance characteristic dataset. Narrow pulse signals are injected into the corresponding test path. The time information and port number of each pulse transmission are recorded. The signal acquisition device is started simultaneously to monitor the echo waveform of the reflected signal of the path. The time domain waveform data of the echo signal is associated with the corresponding transmission time, path number, and start and end port information to generate a raw data file of the reflected waveform containing pulse transmission time, echo waveform amplitude sequence, and time axis information.

[0053] S4.2. Based on the original data file of the reflected waveform, the echo signal is filtered and the baseline is corrected. The time point corresponding to the reflection peak is identified, and the reflection delay and the distance of the reflection point are calculated to generate a reflection delay analysis result file.

[0054] Specifically, the time-domain sequence of the echo signal corresponding to each test path in the original data file of the reflected waveform is read, and the band-pass filtering method is used to remove high-frequency interference components and low-frequency drift components, making the main peak structure of the echo signal clearer. Baseline correction is performed, and the entire waveform is offset and adjusted based on the signal average value to obtain a standard echo signal with zero mean. The echo signal is scanned using an envelope detection algorithm or differential extremum method to identify the positions of the main reflection peaks and record the time points corresponding to each reflection peak. The reflection delay is obtained by calculating the time difference between the pulse emission time and the time of reflection peak appearance. Then, the physical distance to the reflection point is calculated using the electromagnetic wave propagation speed parameters in the conductor (e.g., a propagation speed coefficient determined based on the conductor's medium properties, with values ​​ranging from 0.6 to 0.8 times the speed of light). The reflection delay, reflection peak amplitude, reflection point distance, and conductor number information for each test path are stored in tabular form, generating a reflection delay analysis result file. S4.3. Based on the reflection delay analysis result file, the reflection delay, reflection peak amplitude and waveform polarity of each test path are classified to distinguish different types of faults and generate fault location information.

[0055] Specifically, the reflection delay, reflection peak amplitude, and waveform polarity information of each test path recorded in the reflection delay analysis result file are read. Based on the time distribution characteristics in the reflection delay analysis result file, all reflection peaks appearing in the same test path are arranged in time-axis order, and their relative positions, time intervals between adjacent reflection peaks, peak density, and aggregation or dispersion patterns on the time axis are determined. This helps to identify the number of reflection sources, the relative distance between them, and whether the reflection event is a single fault, a compound fault, or a multi-point fault. The reflection peak amplitude is used as the basis for determining reflection energy, and high-amplitude reflection signals are marked for identification. For faults involving breakage or severe poor contact, the polarity of low-amplitude reflected signals is analyzed. When the polarity of the reflected waveform is consistent with the direction of the transmitted pulse, it is determined to be an impedance-increasing fault (such as a broken wire or loose contact). When the polarity of the reflected waveform is opposite, it is determined to be an impedance-decreasing fault (such as insulation damage or short-circuit tendency). By clustering the time delay location and amplitude characteristics of different types of reflected signals in each test path, the specific location range and fault type identifier of each type of fault are obtained. The information such as path number, fault type, distance of reflection location, and reflection intensity level are summarized to generate a fault location information file containing fault location results, fault category, and spatial distribution characteristics.

[0056] S5. The standardized resistance data matrix, impedance feature dataset, and fault location information are fused to form a multi-dimensional conduction feature vector, which is then compared with the normal conduction characteristics to generate a conduction status report matrix.

[0057] S5.1. Based on the standardized resistance data matrix, impedance feature dataset and fault location information, establish a corresponding multi-dimensional feature index table according to the conductor number, and perform data alignment and normalization processing on the resistance value, impedance feature parameters and fault distance information of the same conductor under different detection dimensions to form an initial fusion feature set.

[0058] Specifically, a multidimensional feature index table is established with the conductor number as the main index. The resistance value of each conductor in the standardized resistance data matrix, the frequency domain feature parameters of the corresponding conductor in the impedance feature dataset, and the reflection distance parameters in the fault location information are matched and associated through conductor number index comparison. The resistance value of each conductor in the standardized resistance data matrix, the frequency domain feature parameters of the corresponding conductor in the impedance feature dataset, and the reflection distance parameters in the fault location information are uniformly processed, mapping data of different dimensions to the same scale range, realizing the standardized alignment of feature data under different detection dimensions, and arranging them in order of conductor number to form a multidimensional data set containing resistance feature vectors, impedance frequency domain feature vectors, and fault space feature vectors. The multidimensional data set is integrated and stored as an initial fusion feature set.

[0059] S5.2. Perform feature weight allocation on each parameter in the initial fusion feature set, calculate the comprehensive weight coefficient based on the conductor resistance stability, impedance frequency band characteristic change rate and reflection positioning confidence, and generate a weighted fusion matrix.

[0060] Specifically, the resistance characteristic parameters, impedance characteristic parameters, and fault location characteristic parameters corresponding to each conductor in the initial fusion characteristic set are read. The time stability coefficient is obtained by the resistance value sequence of the resistance characteristic parameters recorded in the initial fusion characteristic set at different sampling times during the detection process. The fluctuation range of resistance with the detection process is evaluated by performing dispersion calculation on the resistance characteristic parameters obtained by the same conductor at multiple sampling times (e.g., using the ratio of standard deviation to mean for numerical expression). The frequency band response difference index is calculated based on the rate of change of impedance amplitude in different frequency bands in the impedance characteristic parameters to characterize the trend of dielectric characteristic change of the conductor under AC excitation, reflecting the consistency of dielectric parameters, the linearity of frequency band response, and the degree of frequency-related impedance offset of the conductor under multi-frequency excitation conditions. The reliability of the pulse reflection positioning is calculated based on the clarity of the reflection peak and the noise interference ratio in the fault location characteristic parameters. The statistical results of the three types of characteristic parameters are normalized to map the values ​​of each parameter to the same numerical range. The distribution of characteristic weight coefficients is determined according to their physical meaning. Among them, resistance stability is used to reflect the resistance fluctuation of the conductor under DC excitation, dielectric property change trend is used to reflect the impedance amplitude change rate, phase change direction and frequency band response linearity of the conductor under multi-frequency excitation conditions, impedance frequency band change rate is used to reflect the change gradient of AC impedance in the characteristic frequency band, and reflection positioning reliability is used to reflect the credibility level of the pulse reflection positioning result. With resistance stability as the main weight, dielectric property change trend and impedance frequency band change rate as secondary weights, and reflection positioning reliability as the correction weight, the weighted characteristic value is calculated based on the product of the characteristic weight coefficient and the corresponding characteristic parameter. The weighted characteristic value of each conductor is arranged in sequence according to the characteristic dimension to form a multi-dimensional weighted vector. The weighted characteristic vectors of all conductors are summarized in the order of conductor number to generate a weighted fusion matrix.

[0061] S5.3. The electrical continuity of each conductor is quantitatively evaluated based on the weighted fusion matrix. The comprehensive parameters of conduction integrity index, impedance consistency index and reflection anomaly ratio are calculated to form a multi-dimensional conduction feature vector.

[0062] Specifically, by comparing the deviation between the fluctuation range of the resistance-dimensional weighted eigenvalues ​​during the detection process and the standardized resistance benchmark, a continuity integrity index is calculated to quantify the degree of electrical continuity of the conductor under static energized conditions. Based on the consistency of impedance amplitude and phase at different frequency points corresponding to the impedance-dimensional weighted eigenvalues, the dispersion of impedance amplitude and phase at each frequency point is statistically analyzed to calculate the impedance consistency index, which evaluates the stability of the conductor's signal transmission characteristics under multi-frequency excitation conditions. Based on the continuity integrity index and the impedance consistency index, combined with the number of reflection peaks, reflection peak amplitude, and reflection position distribution in the reflection-dimensional weighted eigenvalues, the reflection anomaly ratio is calculated to quantify the relative proportion of local impedance abrupt changes or latent defects within the conductor. The continuity integrity index, impedance consistency index, and reflection anomaly ratio are arranged in sequence and associated with the corresponding conductor numbers to form a multi-dimensional continuity feature vector for each conductor.

[0063] S5.4. Compare and analyze the multidimensional conduction feature vector with the normal conduction characteristic sample, identify the wire path with abnormal conduction state through the similarity judgment algorithm, generate the conduction state comparison result, and generate the result based on the judgment result in the conduction state comparison result.

[0064] Specifically, using the conductor number as an index, the resistance, impedance, and reflection characteristic parameters in the multidimensional conduction feature vector are compared item by item with the corresponding parameters in the normal conduction characteristic sample. The degree of deviation of the conduction characteristics of each conductor is evaluated by the similarity value between each feature parameter. The similarity value is calculated using the cosine similarity or Euclidean distance method. When the similarity value of the multidimensional conduction feature vector is higher than the similarity threshold, it is judged as normal conduction; when the similarity value is lower than the similarity threshold, it is judged as abnormal conduction. The judgment results of all conductors are classified and summarized to form a conduction status comparison result containing conductor number, conduction status category, feature deviation value, and abnormality type label. Based on the judgment information in the conduction status comparison result, the conduction status data of each conductor is written into a matrix structure in sequence, and a conduction status report matrix is ​​generated with the conductor number as the row index and the conduction status parameter as the column index.

[0065] It should be noted that resistance, impedance, and reflection characteristic parameters were collected from different samples. A multidimensional conduction feature vector was calculated for each conductor, and the cosine similarity method was used to calculate the feature similarity distribution between samples. Probabilistic statistics were performed on the similarity results of all normal samples to determine the confidence boundary values ​​within the distribution interval, which were then used as the similarity threshold. The similarity threshold ranged from 0.90 to 0.98 in the example.

[0066] S6. Output the detection report of each conductor according to the continuity status report matrix to obtain the conductor detection report.

[0067] S6.1. Based on the continuity status report matrix, extract the detection number, continuity status level, anomaly type and confidence information of each conductor in sequence according to the conductor number, generate a single-line detection result table, and perform format conversion on the single-line detection result table to obtain a standardized detection result file.

[0068] Specifically, the parameters in the continuity status report matrix are read one by one according to the conductor number. The detection number, continuity status level, anomaly type, and confidence level information corresponding to each conductor are extracted. The extracted results are arranged in the order of fields to form a structured single-line detection result table. The field format in the single-line detection result table is standardized, including unifying the encoding format of the detection number, standardizing the representation of the continuity status level, and classifying the anomaly type into clear categories such as poor contact, local breakage, and impedance drift. The data structure of the single-line detection result table is transformed from a row structure to a matrix storage format, so that each detection number corresponds to a unique continuity status data record, forming a formatted standardized detection result file.

[0069] S6.2. Classify the standardized test result files according to the continuity status level, generate corresponding test report templates, and write the test number, continuity status level, anomaly type and confidence level information of each conductor into the test report template to generate a test report.

[0070] Specifically, the test records in the standardized test result file are classified according to the continuity status level. The continuity status level is divided into categories such as normal continuity, continuity abnormality, and retesting required. During the classification process, the consistency between the conductor number and the test number is maintained. Corresponding test report templates are established according to different continuity status levels. The test report template contains fields such as test number, conductor number, continuity status level, abnormality type, confidence information, and test conclusion. The test records in the standardized test result file are read one by one, and the test number, continuity status level, abnormality type, and confidence information of each conductor are written into the corresponding category of test report template in the order of the fields. The test results of conductors in the same category are summarized and statistically analyzed to generate a conductor test report with overview statistical information.

[0071] This embodiment also provides a system for efficiently detecting the continuity of automotive wiring harnesses, comprising: a configuration module for identifying the coded information of the automotive wiring harness under test, generating a template configuration file, controlling the automatic switching of a multi-port electronic matrix switch to form a test path for the wire under test, and recording the path configuration data file; a measurement module for injecting a constant DC current into the test path of the wire under test based on the path configuration data file and calculating the conduction resistance of the wire under test to obtain a standardized resistance data matrix; an analysis module for applying an AC excitation signal to the test path corresponding to the standardized resistance data matrix, calculating the impedance and equivalent capacitance parameters at each frequency point, and forming an impedance feature dataset; a location module for injecting a narrow pulse signal into the test path corresponding to the impedance feature dataset and acquiring the reflected waveform to obtain fault location information; an evaluation module for performing feature fusion of the standardized resistance data matrix, the impedance feature dataset, and the fault location information to form a multi-dimensional continuity feature vector, comparing it with normal continuity characteristics, and generating a continuity status report matrix; and a reporting module for outputting a detection report for each wire based on the continuity status report matrix to obtain a wire detection report.

[0072] In summary, this invention achieves the following: by applying an AC excitation signal to the test path corresponding to the standardized resistance data matrix to form an impedance feature dataset, it can effectively identify hidden defects such as poor contact and insulation degradation; by constructing a weighted multidimensional continuity feature vector and through cross-dimensional data alignment, normalization, and weight allocation, it realizes a comprehensive quantitative evaluation of the conductor continuity state, thereby improving the comprehensiveness, robustness, and fault identification accuracy of the detection.

[0073] 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 efficiently detecting continuity in automotive wiring harnesses, characterized in that: include, Identify the coding information of the automotive wiring harness under test, generate a template configuration file, control the automatic switching of the multi-port electronic matrix switch to form a test path for the wire under test, and record the path configuration data file; Based on the path configuration data file, a constant DC current is injected into the test path of the conductor under test and the on-resistance of the conductor under test is calculated to obtain a standardized resistance data matrix. An AC excitation signal is applied to the test path corresponding to the standardized resistance data matrix, and the impedance and equivalent capacitance parameters at each frequency point are calculated to form an impedance feature dataset. The specific steps for forming the impedance feature dataset are as follows. Based on the resistance characteristics of the conductor under test in the standardized resistance data matrix, AC excitation setting data is generated, and AC excitation signals are sequentially applied to the test path of the conductor under test corresponding to the standardized resistance data matrix. At the same time, the output voltage signal and input current signal are collected in real time to form the original frequency response data. Based on the original frequency response data, the amplitude ratio and phase difference of the voltage and current signals at each frequency are calculated to obtain the equivalent resistance and equivalent reactance components, and impedance spectrum data is generated with frequency as the index. Based on impedance spectrum data, the impedance amplitude, phase and equivalent capacitance parameters at all frequency points are normalized to establish the frequency-impedance-phase correspondence curve. Select the frequency band with phase abrupt change in the frequency-impedance-phase correspondence curve and perform multi-parameter fitting calculation to obtain frequency domain characteristic parameters; combine the frequency domain characteristic parameters into a frequency domain characteristic vector, and write it into the characteristic matrix in sequence according to the conductor number to generate an impedance characteristic dataset; A narrow pulse signal is injected into the test path corresponding to the impedance characteristic dataset and the reflected waveform is acquired to obtain fault location information; The standardized resistance data matrix, impedance feature dataset, and fault location information are fused to form a multi-dimensional conduction feature vector, which is then compared with normal conduction characteristics to generate a conduction status report matrix. The detection report for each conductor is obtained by outputting the detection report for each conductor based on the continuity status report matrix.

2. The method for efficiently detecting continuity in automotive wiring harnesses as described in claim 1, characterized in that: The specific steps for identifying the encoding information of the vehicle wiring harness under test and generating a template configuration file are as follows. Read the unique coding information of the automotive wiring harness under test, extract the wiring harness model, production batch and terminal number information, retrieve the corresponding standard connectivity table in the wiring harness database according to the wiring harness model information, and generate a standard connectivity matching result file; Based on the connectivity information in the standard connectivity matching result file, a detection task template is generated. The detection task template is then validated for format and path consistency, and a template configuration file is generated.

3. The method for efficiently detecting continuity in automotive wiring harnesses as described in claim 2, characterized in that: The specific steps for configuring the recording path data file are as follows. The wire path table information in the template configuration file is parsed, and the starting port and ending port of each wire are mapped to the row and column nodes of the electronic matrix switch to form a matrix port mapping table. Based on the matrix port mapping table, channel switching instructions are executed sequentially according to the test task template to establish the test path of the conductor under test, and the path number and establishment time are recorded to generate the path configuration data file.

4. The method for efficiently detecting continuity in automotive wiring harnesses as described in claim 2, characterized in that: The specific steps for obtaining the standardized resistance data matrix are as follows: Based on the path configuration data file, the test parameters of the corresponding wire in the detection task template are called to generate constant current output configuration data. A constant current is injected into the path under test and a four-wire voltage measurement is performed to obtain the original voltage data. Based on the original voltage data, the on-resistance is calculated using Ohm's law and then standardized after temperature and length compensation. A matrix index is then established according to the port number to generate a standardized resistance data matrix.

5. The method for efficiently detecting continuity in automotive wiring harnesses as described in claim 1, characterized in that: The specific steps for obtaining fault location information are as follows: Based on the average impedance value, phase delay and equivalent capacitance characteristics of each test path in the impedance characteristic dataset, pulse excitation configuration data is obtained, and a narrow pulse signal is injected into the test path corresponding to the impedance characteristic dataset. The pulse emission time is recorded and the echo signal is acquired to generate the original data file of the reflected waveform. Based on the original data file of the reflected waveform, the echo signal is filtered and baseline corrected, the time point corresponding to the reflection peak is identified, and the reflection delay and distance to the reflection point are calculated to generate a reflection delay analysis result file. Based on the reflection delay analysis results file, the reflection delay, reflection peak amplitude and waveform polarity of each test path are classified to distinguish different types of faults and generate fault location information.

6. The method for efficiently detecting continuity in automotive wiring harnesses as described in claim 1, characterized in that: The specific steps for forming the multidimensional conduction feature vector are as follows. Based on the standardized resistance data matrix, impedance feature dataset and fault location information, a corresponding multi-dimensional feature index table is established according to the conductor number. The resistance value, impedance feature parameters and fault distance information of the same conductor under different detection dimensions are aligned and normalized to form an initial fusion feature set. Feature weights are assigned to each parameter in the initial fusion feature set. The comprehensive weight coefficients are calculated based on the conductor resistance stability, impedance frequency band characteristic change rate, and reflection positioning confidence, and a weighted fusion matrix is ​​generated. The electrical continuity of each conductor is quantitatively evaluated based on a weighted fusion matrix. The comprehensive parameters of conduction integrity index, impedance consistency index and reflection anomaly ratio are calculated to form a multi-dimensional conduction feature vector.

7. The method for efficiently detecting continuity in automotive wiring harnesses as described in claim 1, characterized in that: The conduction status report matrix refers to the comparison and analysis of multi-dimensional conduction feature vectors with normal conduction characteristic samples, the identification of wire paths with abnormal conduction status through similarity judgment algorithms, the generation of conduction status comparison results, and the generation of the matrix based on the judgment results in the conduction status comparison results.

8. The method for efficiently detecting continuity in automotive wiring harnesses as described in claim 1, characterized in that: The specific steps to obtain the conductor inspection report are as follows: Based on the continuity status report matrix, the detection number, continuity status level, anomaly type and confidence information of each conductor are extracted sequentially according to the conductor number to generate a single-line detection result table. The single-line detection result table is then formatted and converted to obtain a standardized detection result file. The standardized test result files are classified according to the continuity status level, and corresponding test report templates are generated. The test number, continuity status level, anomaly type and confidence level information of each conductor are written into the test report template to generate a test report.

9. A system for efficiently detecting continuity in automotive wiring harnesses, based on the method for efficiently detecting continuity in automotive wiring harnesses according to any one of claims 1 to 8, characterized in that: include, The configuration module is used to identify the coding information of the automotive wiring harness under test, generate a template configuration file, control the automatic switching of the multi-port electronic matrix switch, form a test path for the wire under test, and record the path configuration data file. The measurement module is used to inject a constant DC current into the test path of the conductor under test based on the path configuration data file and calculate the on-resistance of the conductor under test to obtain a standardized resistance data matrix. The analysis module is used to apply an AC excitation signal to the test path corresponding to the standardized resistance data matrix, calculate the impedance and equivalent capacitance parameters at each frequency point, and form an impedance characteristic dataset. The positioning module is used to inject a narrow pulse signal into the test path corresponding to the impedance characteristic dataset and acquire the reflected waveform to obtain fault location information. The evaluation module is used to fuse standardized resistance data matrix, impedance feature dataset and fault location information to form a multi-dimensional conduction feature vector, and compare it with normal conduction characteristics to generate a conduction status report matrix. The reporting module is used to output the detection report of each conductor based on the continuity status report matrix, thus obtaining the conductor detection report.

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