A method, device and system for detecting the production quality of a vehicle steering cable

Abstract

The application relates to the technical field of cable detection, in particular to a method, equipment and system for detecting the production quality of a car operating cable, which comprises the following steps: calculating a quality instability coefficient by the mutual relationship between different detection data under the same curvature, and quantifying the stability of the internal resistance of the cable; calculating a quality out-of-control coefficient according to the change of the detection data under different curvatures, and quantifying the resistance change in the dynamic motion process of the cable; quantitatively analyzing the dynamic change characteristics of the resistance by combining the influence of the internal quality of the cable on the resistance; correcting the LOF value of the detection data in the static detection of the car cable by the quality out-of-control coefficient; and detecting the production quality of the car cable based on the corrected LOF value, so that the accuracy of the quality and performance detection of the cable is further improved, and the reliability of the cable quality detection result is ensured.

Description

Technical Field

[0001] This application relates to the field of cable testing technology, specifically to a method, equipment, and system for testing the production quality of automotive control cables. Background Technology

[0002] As a key mechanical transmission component connecting the cockpit control mechanisms and actuators, automotive control cables are widely used in systems such as clutch, brake, accelerator, gear shifting, hood opening, and door locking. Their performance directly affects the vehicle's handling precision, driving safety, and user experience. As the automotive industry moves towards higher quality and reliability, higher demands are placed on the dynamic response characteristics, operational consistency, and durability of control cables. While traditional cable quality control systems cover basic mechanical performance indicators, they exhibit significant limitations when facing increasingly stringent driving control quality requirements.

[0003] Current technologies for quality inspection in cable production primarily rely on static or quasi-static testing methods, such as directly measurable parameters like maximum load capacity, fatigue life, and length tolerance. However, they severely neglect the dynamic mechanical behavior of cables under actual operating conditions, such as the dynamic changes in cable resistance. Existing technologies for resistance testing often employ static loading methods with a single radius of curvature. In reality, resistance is not a fixed value but a dynamic variable influenced by intrinsic factors such as wire rope twisting quality, lubrication uniformity, and sheath matching. Its fluctuations significantly affect handling smoothness, cause operational lag, and accelerate fatigue failure. Due to the lack of dynamic, continuous, and multi-parameter coupled testing capabilities for the resistance-curvature relationship, current quality inspection methods struggle to accurately assess the true performance of cables. Cables with dynamic performance defects can lead to quality problems such as inconsistent feel, jamming, and even premature failure. Summary of the Invention

[0004] In view of the above, it is necessary to provide a method, equipment and system for testing the production quality of automotive control cables to solve the above problems.

[0005] According to one aspect of this application, a method for inspecting the production quality of automotive control cables is provided, the method comprising:

[0006] The car cable was tested under various bending curvatures to obtain tension data, displacement data, and vibration data for each sample.

[0007] For each curvature, the differences between the tension data at both ends of the cable at the same acquisition time are analyzed, and the changing trend of the differences is analyzed to determine the resistance measurement trend under each curvature. Combined with the dispersion of the differences, the mass instability coefficient of each curvature is obtained.

[0008] By comparing the mass instability coefficient obtained for each curvature with the overall distribution characteristics of all vibration data corresponding to each curvature, the variation of displacement data in each curvature is analyzed, and the mass instability coefficient is determined.

[0009] The LOF value of the detection data during static testing of automotive cables is corrected using the aforementioned quality runaway coefficient. Based on the corrected LOF value, the production quality of the automotive cables is then tested.

[0010] Preferably, determining the drag measurement trend for each curvature specifically involves:

[0011] The cable has two ends: a cable output end and a cable input end.

[0012] The difference between the input tension and the output tension obtained for each sample at the same acquisition time is taken as the cable resistance at that time.

[0013] The sequence of all cable resistances obtained for each sample at each curvature is denoted as the cable resistance sequence. The trend sequence of the cable resistance sequence is obtained and fitted to obtain the fitted curve function.

[0014] Obtain the change values ​​between the corresponding function values ​​of adjacent independent variables in the fitted curve function, and use the average of all the change values ​​as the resistance trend measure for each curvature.

[0015] Preferably, obtaining the mass instability coefficient for each curvature includes:

[0016] For each curvature, the coefficient of variation of the cable resistance sequence for each sample is obtained. The mean of the coefficients of variation obtained for all samples is calculated and positively integrated with the mean of the cable resistance of all samples and the resistance trend measure to obtain the mass instability coefficient for each curvature.

[0017] Preferably, determining the quality runaway coefficient specifically involves:

[0018] The sequence of mass instability coefficients obtained from all curvatures is taken as the instability coefficient sequence; the mean vibration data of each sample for each curvature is obtained, and the mean vibration data of all samples is used to form the total vibration sequence.

[0019] Based on the tension data at the cable output end, a first-order difference sequence of all displacement data under each curvature is fitted. The curve before the position of the maximum tension data is used as the output propulsion curve function, and the remaining curves are used as the return curve function. The distribution of the integral area corresponding to the output propulsion curve function and the return curve function is analyzed to determine the response speed coefficient and the curve consistency coefficient.

[0020] The curvature influence coefficient is determined based on the similarity between the distribution of the total vibration sequence and the instability coefficient sequence for each curvature.

[0021]

[0022] In the formula, It is the quality runaway coefficient. It is the length of the first-order difference sequence of the instability coefficient sequence. It is the first difference sequence of the instability coefficient sequence. The normalized result of each element, It is the general category of curvature. , They are the first The response speed coefficient and curve consistency coefficient are given. It is the curvature influence coefficient; It is the normalized result of the element mean of the total vibration sequence.

[0023] Preferably, the determination of the response speed coefficient is specifically the normalized value of the average length of the tension data at the input end of all sample cables under each curvature; the curve consistency coefficient is specifically the normalized result of the absolute value of the difference between the integral areas of the output propulsion curve function and the return curve function obtained for each curvature.

[0024] Preferably, the determination of the curvature influence coefficient is specifically the Pearson correlation coefficient between the total vibration sequence and the runaway coefficient sequence.

[0025] Preferably, the formula for correcting the LOF value of the test data during static testing of the vehicle cable is as follows:

[0026]

[0027] In the formula, It is the first The LOF value after weighting by the bar chart. In the LOF anomaly detection algorithm k-distance neighborhood, , These are the preset quality runaway coefficient threshold and the normalized first... The mass runaway coefficient of the cable, , These are the LOF anomaly detection algorithms. , Locally achievable density; , This indicates the cable number.

[0028] Preferably, the process of inspecting the production quality of automotive cables specifically involves: performing threshold segmentation on the LOF values ​​of all cables to obtain a segmentation threshold; cables with LOF values ​​greater than the segmentation threshold are considered abnormal and have unqualified production quality; otherwise, they are considered normal cables and have qualified production quality.

[0029] According to another aspect of this application, a production quality inspection device for automotive control cables is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0030] According to another aspect of this application, a production quality inspection system for automotive control cables is provided, wherein the system stores a computer program that, when executed by a processor, implements any of the methods described above.

[0031] This application has at least the following beneficial effects:

[0032] This application tests automotive cables at various curvatures, acquiring tension, displacement, and vibration data. By testing the tension, displacement, and vibration data of the cables at different curvatures, a comprehensive understanding of the cables' performance in actual use can be obtained, which helps to identify potential fault points in advance. The multi-dimensional acquisition of data provides more information for subsequent analysis; analyzing the differences in tension data between the two ends of the cable at the same acquisition time and analyzing the changing trends of these differences determines the resistance measurement trend. By analyzing the tension differences at both ends of the cable, the load imbalance of the cable under different curvatures can be effectively identified. Determining the resistance measurement trend helps to understand the cable's flexibility and resistance to deformation, which are important indicators for measuring cable performance. Combining the dispersion of the difference characteristics, a mass instability coefficient for each curvature is obtained. By quantifying the dispersion of the tension differences at both ends of the cable, the stability of the cable under different curvatures can be reflected. The calculation of the mass instability coefficient can help predict the performance of the cable under external disturbances, thereby identifying potential inhomogeneities or structural defects in the cable during production or use. The consistency and stability evaluation of cables during the manufacturing process is particularly important. By comparing the mass instability coefficient with the overall distribution of vibration data, the dynamic response of the cable can be further analyzed, which helps to comprehensively evaluate the dynamic performance and structural stability of the cable and improve the accuracy of the inspection. Analyzing the changes in displacement data in each curvature determines the mass instability coefficient. Displacement data can reflect the deformation of the cable under different loads. Combined with the analysis of the mass instability coefficient, it can help to determine whether the cable has deformation or instability problems that exceed the normal range, improve the monitoring accuracy of cable runaway behavior, and detect possible abnormal deformation of the cable in a timely manner. The mass instability coefficient is used to correct the LOF value of the detection data when statically inspecting automotive cables. By correcting the LOF (Local Outlier Factor) value of the static detection data, misjudgments caused by data deviations or outliers can be reduced, improving the accuracy of the detection results and thus helping to identify potential quality problems. Based on the corrected LOF value, the production quality of automotive cables is inspected. The corrected LOF value can more accurately reflect the production quality of the cable. Through the inspection of production quality, defects and non-uniformities in the production process can be detected in a timely manner, ensuring that the production quality of the cable meets the standards. Attached Figure Description

[0033] Figure 1 A flowchart of the steps for a production quality inspection method for automotive control cables provided in this application;

[0034] Figure 2 A flowchart for obtaining the quality runaway coefficient provided for this application. Detailed Implementation

[0035] In the description of the embodiments in this application, the words "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary," "or," and "for example" is intended to present the relevant concepts in a specific manner.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this application's specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0037] It should also be noted that the terms "first" and "second" in this application and its accompanying drawings are used to distinguish similar objects, rather than to describe a specific order or sequence. The methods disclosed in the embodiments of this application or the methods shown in the flowcharts include one or more steps for implementing the method. Without departing from the scope of this application, the execution order of multiple steps can be interchanged, and some steps can also be deleted.

[0038] Please see Figure 1 The diagram illustrates a flowchart of a production quality inspection method for automotive control cables according to an embodiment of this application. The method includes the following steps:

[0039] Step 1: Test the car cable at various bending curvatures and obtain the tension data, displacement data, and vibration data for each sample.

[0040] A universal testing machine was used for production quality inspection of automotive control cables. UTM load sensors at the input (moving end) and output (fixed base) of the universal testing machine collected tension data at both ends. A laser displacement sensor collected displacement data at the moving end. At any point where the cable contacts the guide wheels, a vibration sensor was used to collect vibration data of the cable during its movement. The acquisition frequency for all data types was set to 1 kHz, with one sample corresponding to one complete reciprocating motion. Replaceable guide wheels were installed along the cable path to control the cable's curvature. The automotive control cables were tested under different curvatures. This embodiment used four different curvatures, with guide wheel radii of 35 mm, 50 mm, 75 mm, and 100 mm for each curvature. Testing was conducted at each curvature. Next, in this embodiment Take 10. Normalize the collected data. In this embodiment, the maximum-minimum normalization method is used to normalize various types of data. The normalized data are arranged into corresponding data sequences according to the collection order. Each sample can obtain the input tension sequence, output tension sequence, displacement sequence, and vibration sequence.

[0041] Step 2: For each curvature, analyze the differences between the tension data at both ends of the cable at the same acquisition time, and analyze the changing trend of the differences to determine the resistance measurement trend under each curvature. Combined with the dispersion of the differences, obtain the mass instability coefficient for each curvature.

[0042] The resistance of a car cable refers to the torque generated on the shaft due to internal friction and external resistance (such as bending and vibration) during the cable's movement or under stress. This resistance not only affects the cable's operating efficiency and sensitivity but can also negatively impact its durability, thus accelerating performance degradation. The resistance is influenced by various factors, including the quality of the internal steel wire rope and the material and structure of the inner liner. Typically, the cable's core is composed of multiple strands of twisted steel wire. If the wire diameter is smaller, the number of strands is greater, and the dimensional tolerance of each strand is smaller, the overall cable structure becomes more flexible. This is because the thinner wires and higher strand count increase the number of contact points and create a more uniform contact distribution, resulting in less friction and lower resistance during bending.

[0043] Furthermore, during the twisting process of multiple steel wires, the uniformity of stress on each wire and the difference in twist pitch can also cause fluctuations in cable resistance. When the stress on each wire is uneven or the twist pitch is inconsistent, the cable may loosen when bending, leading to slippage between the strands. Such slippage makes the fluctuation of cable resistance unpredictable. In addition, due to the break-in effect, the resistance of the cable is usually high and fluctuates greatly in the initial stage, but with repeated use and testing, the different steel wires gradually adapt and break in, and the resistance will gradually decrease and tend to stabilize.

[0044] Finally, lubricant is typically added to the inner liner of automotive cables to reduce friction. However, uneven or insufficient lubricant application can lead to differences in resistance at different points on the cable. Because lubricant has a certain fluidity, uneven application can sometimes be self-corrected by the mechanical movement of the cable, thus the effect of lubricant is minor in some cases. However, if the total amount of lubricant is insufficient, the cable resistance may rebound after a slight decrease, resulting in more significant resistance fluctuations and potentially causing the resistance to rise again, affecting the cable's performance stability.

[0045] The difference between the input and output tensions obtained at the same acquisition moment is taken as the cable resistance at that moment. Therefore, based on the input and output tension sequences for each sample, the cable resistance sequence for each sample can be obtained. The cable resistance sequences of all samples under the same curvature, arranged in the sample acquisition order, constitute the resistance fluctuation sequence under that condition. Using a resistance fluctuation sequence as input, STL time series decomposition is applied to obtain the trend sequence. STL time series decomposition is a well-known technique and will not be elaborated further. Finally, using the index of the element in the trend sequence as the independent variable and the element value as the dependent variable, a polynomial fitting is performed, and the corresponding fitted curve function is output. Polynomial fitting is a well-known technique and will not be elaborated further.

[0046] Based on the above analysis, a mass instability coefficient is calculated to measure the stability characteristics of the vehicle control cable mass. Specifically, this involves obtaining the change values ​​between corresponding function values ​​of adjacent independent variables in the fitted curve function, and using the average of all change values ​​as the resistance trend measure for each curvature. In this embodiment, the specific formula for the resistance trend measure is: In the formula, It is the first The resistance trend measurement below; It is the first The length of the downtrend sequence The independent variable on the trend sequence fitting curve function is... The change in the function value corresponding to the previous independent variable is calculated using the absolute value of the difference.

[0047] Furthermore, for each curvature, the mean of the coefficient of variation of the cable resistance sequence of all test samples is obtained, and positively fused with the mean resistance of all test samples and the resistance trend measure to obtain the mass instability coefficient for each curvature. In this embodiment, the positive fusion of multiple variables is performed using a multiplication calculation method.

[0048] Understandably, for cables of poor quality, the main reason is the poor quality of the internal steel wire rope. The resistance of these cables fluctuates significantly due to the quality of the steel wire rope. Furthermore, due to the break-in effect of the cable and the lubricating effect of the lubricant, cables of poor quality initially exhibit higher resistance and stronger fluctuations. As testing progresses, the resistance gradually decreases and the fluctuations also lessen, but it is still greater than the resistance of a normally high-quality cable. When the lubricant quality is also poor, the resistance may rebound after a reduction, leading to a further increase in resistance and stronger fluctuations. Therefore, the worse the quality of the cable, the stronger the resistance fluctuations during use, resulting in poorer cable stability and a higher quality instability coefficient.

[0049] Step 3: Compare the mass instability coefficient obtained for each curvature with the overall distribution characteristics of all vibration data corresponding to each curvature, analyze the changes in displacement data in each curvature, and determine the mass instability coefficient.

[0050] The mass instability coefficient measures the stability of the control cable mass under the same curvature. However, since the state of the automotive cable changes dynamically during actual use, the fluctuation of cable resistance under a single working condition is insufficient to guarantee the accuracy of the test. For example, there may be some sudden braking situations during the use of a car. If the uncontrollability of the cable resistance is stronger, it is more likely to cause loss of control of the car in extreme cases and generate corresponding risks. Therefore, it is necessary to further test the cable mass based on the fluctuation of automotive cable resistance under different curvatures.

[0051] Under normal circumstances, high-quality cables can adapt more smoothly to changes in curvature, resulting in more stable resistance changes. As the curvature of the cable increases, its resistance fluctuations are smaller, and its response speed is faster. It's important to note that the cable's response refers to the time required for the cable to move to the target position and return to its initial state. When the cable quality is poor, as the curvature increases, the interference between the internal wires restricts their free sliding, leading to a gradual increase in resistance and a longer response time.

[0052] In addition, under normal circumstances, the resistance changes inside a high-quality cable are relatively uniform and stable. Even when the curvature increases, the dynamic motion of the cable will maintain small and consistent vibrations. However, due to the uneven distribution of internal resistance, a poor-quality cable may experience increased vibration fluctuations during dynamic motion.

[0053] For high-quality cables, their internal structure is relatively fixed and stable, which makes the force-displacement curve during cable movement smooth and repeatable. In other words, the consistency between the advance curve and the return curve is relatively high during reciprocating motion.

[0054] The mass instability coefficients under each curvature are arranged in order of curvature magnitude to form an instability coefficient sequence, and the mean values ​​of the vibration sequence elements of all samples under each curvature are arranged in order of curvature magnitude to form a total vibration sequence.

[0055] Then, the first-order difference sequence of the displacement sequence of each sample under a curvature condition is calculated. Then, the elements of the tension sequence at the output end of each sample, except for the last element, are taken as independent variables, and the elements of the first-order difference sequence of the displacement sequence of the sample are taken as dependent variables. Polynomial fitting is performed on the advancing and returning processes of the reciprocating motion, and the advancing curve function and the returning curve function are output respectively. Specifically, in a tension sequence, the maximum value of the tension is selected. The points formed by the elements before the maximum value and the elements with the same index in the displacement sequence are the advancing points. The points formed by the elements after the maximum value and the elements with the same index in the displacement sequence are the returning points. All advancing points and returning points are fitted respectively, and the advancing curve and the returning curve are output. Polynomial fitting is a well-known technique and will not be elaborated further.

[0056] Based on the above analysis, a mass runaway coefficient is calculated to measure the uncontrollability of the vehicle control cable's mass. The specific formula is as follows:

[0057]

[0058] In the formula, It is the quality runaway coefficient. It is the length of the first-order difference sequence of the instability coefficient sequence. It is the first difference sequence of the instability coefficient sequence. The normalized result of each element, It is the general category of curvature. , They are the first The response speed coefficient and curve consistency coefficient are given. It is the curvature influence coefficient, which is obtained by the Pearson correlation coefficient between the total vibration sequence and the instability coefficient sequence; This is the normalized result of the element mean of the total vibration sequence. In this embodiment, It is the first The normalized value of the length mean of the input tensile force sequence of all samples; It is the first The normalized result is the absolute value of the difference between the integral areas of the downward thrust curve and the return curve, with the upper and lower limits of integration being 0 and the maximum displacement, respectively. It should be noted that the normalization uses maximum value normalization, where the maximum value in the denominator is the maximum value under all curvatures, not the case of a single curvature.

[0059] The flowchart for obtaining the quality runaway coefficient is as follows: Figure 2 As shown.

[0060] Understandably, when the cable quality is poor, the stability of the resistance during cable movement is poor, and the fluctuation of cable resistance between different curvatures is greater. Firstly, the lower the quality of the cable, the more drastic the internal vibration changes when the resistance changes. The corresponding resistance changes between different curvatures fluctuate more, resulting in more intense vibrations. Furthermore, the correlation between vibration and changes in the cable's instability coefficient is stronger. At the same time, the lower the quality of the cable, the slower its response speed, meaning the stronger the hysteresis, and the longer it takes to complete the control of the cable. Due to the fluctuation of resistance, the consistency between the advance curve and the return curve is also worse, resulting in a larger quality runaway coefficient.

[0061] Step 4: Correct the LOF value of the detection data during static testing of the automotive cable using the aforementioned quality runaway coefficient, and then test the production quality of the automotive cable based on the corrected LOF value.

[0062] collection The data for the cable is obtained from standard test data used in static testing of automotive cables, such as maximum load value, axial fatigue, and stroke efficiency. In this embodiment... The value is set to 200, which can be set by the implementer according to the actual situation. The quality runaway coefficient of each cable is calculated. Then, the standard test data of all cables are used to form a test matrix. Each row of the matrix corresponds to the test data of one cable, and each column corresponds to a standard test data. LOF anomaly detection is performed, and the LOF value of each cable is output. LOF anomaly detection is a well-known technology and will not be described in detail.

[0063] In traditional LOF anomaly detection, all samples have equal weight. However, current technologies for automotive cable quality inspection mostly rely on static measurements, making it difficult to assess the dynamic quality of automotive cables. The calculated quality runaway coefficient, however, measures data fluctuations under dynamic conditions by adapting to changes in detection parameters. Therefore, the weights of different cables during anomaly detection can be adjusted using the quality runaway coefficient, resulting in a more sensitive LOF value for dynamic quality anomalies in cables. The specific formula is as follows:

[0064]

[0065] In the formula, It is the first The LOF value after weighting by the bar chart. In the LOF anomaly detection algorithm k-distance neighborhood, , The preset quality runaway coefficient threshold and the normalized first... The mass runaway coefficient of the cable, , These are the LOF anomaly detection algorithms. , Locally achievable density; , This indicates the cable number.

[0066] It should be noted that the quality runaway coefficient threshold can be set as needed, or by... The mass runaway coefficient of the cable is obtained by obtaining the corresponding threshold, including but not limited to the upper quartile and the Otsu threshold.

[0067] In this embodiment, after obtaining the LOF values ​​of all cables, Otsu threshold segmentation is performed to obtain a segmentation threshold. Otsu threshold segmentation is a well-known technique and will not be described in detail here. Cables with LOF values ​​greater than the segmentation threshold are considered abnormal and their corresponding production quality is determined to be substandard.

[0068] Based on the same concept as the method embodiments of this application, a production quality inspection device for automotive control cables is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the methods described above.

[0069] Based on the same concept as the method embodiments of this application, a production quality inspection system for automotive control cables is provided. The system stores a computer program, which, when executed by a processor, implements any of the methods described above.

[0070] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of the systems, methods, and computer program products according to embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description; sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0071] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for detecting the production quality of a steering cable for an automobile, characterized by, The method includes the following steps: The car cable was tested under various bending curvatures to obtain tension data, displacement data, and vibration data for each sample. The cable has two ends: an output end and an input end. The difference between the input end tension and the output end tension obtained by each sample at the same acquisition time is taken as the cable resistance at that time. The sequence of all cable resistances obtained by each sample at each curvature is recorded as the cable resistance sequence. The trend sequence of the cable resistance sequence is obtained and fitted to obtain the fitted curve function. The change value between the corresponding function values ​​of adjacent independent variables in the fitted curve function is obtained, and the average value of all the change values ​​is taken as the resistance trend measure under each curvature. For each curvature, the coefficient of variation of the cable resistance sequence of each sample is obtained. The average value of the coefficient of variation obtained by all samples is calculated and positively fused with the average value of the cable resistance of all samples and the resistance trend measure to obtain the mass instability coefficient under each curvature. The sequence of mass instability coefficients obtained from all curvatures is taken as the instability coefficient sequence; the mean of vibration data of all samples for each curvature is obtained, and the mean of vibration data obtained from all curvatures is used to form the total vibration sequence. Based on the tension data at the cable output end, the first-order difference sequence of all displacement data under each curvature is fitted. The curve before the position of the maximum tension data is used as the output advance curve function, and the remaining curves are used as the return curve function. The distribution of the integral area corresponding to the output advance curve function and the return curve function is analyzed to determine the curve consistency coefficient. The curvature influence coefficient is determined based on the similarity between the distribution of the total vibration sequence and the instability coefficient sequence for each curvature. In the formula, It is the quality runaway coefficient. It is the length of the first-order difference sequence of the instability coefficient sequence. It is the first difference sequence of the instability coefficient sequence. The normalized result of each element, It is the general category of curvature. , They are the first The response speed coefficient and curve consistency coefficient are given. It is the curvature influence coefficient; It is the normalized result of the element mean of the total vibration sequence; the response speed coefficient is specifically the normalized value of the sequence length mean of the tension data at the input end of all samples of the cable under each curvature; The LOF value of the detection data during static testing of automotive cables is corrected using the aforementioned quality runaway coefficient. Based on the corrected LOF value, the production quality of the automotive cables is then tested.

2. The method for quality inspection of automotive control cables as described in claim 1, characterized in that, The curve consistency coefficient is specifically the normalized result of the absolute value of the difference between the integral areas of the output propulsion curve function and the return curve function obtained for each curvature.

3. The method for quality inspection of automotive control cables as described in claim 1, characterized in that, The specific curvature influence coefficient is the Pearson correlation coefficient between the total vibration sequence and the instability coefficient sequence.

4. The method for quality inspection of automotive control cables as described in claim 1, characterized in that, The formula for correcting the LOF value of the test data during static testing of automotive cables is as follows: In the formula, It is the first The LOF value after weighting by the bar chart. In the LOF anomaly detection algorithm k-distance neighborhood, , These are the preset quality runaway coefficient threshold and the normalized first... The mass runaway coefficient of the cable, , These are the LOF anomaly detection algorithms. , Locally achievable density; , This indicates the cable number.

5. The method for quality inspection of automotive control cables as described in claim 1, characterized in that, The process of inspecting the production quality of automotive cables involves: thresholding the LOF values ​​of all cables to obtain a threshold; cables with LOF values ​​greater than the threshold are considered abnormal and have unqualified production quality; otherwise, they are considered normal cables and have qualified production quality.

6. A production quality inspection device for automotive control cables, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-5.

7. A production quality inspection system for automotive control cables, wherein the system stores a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-5.

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