A one-stop off-line detection method for automobile tailgate support rods

Abstract

The present invention discloses a one-stop offline detection method for automobile tailgate struts, which relates to the technical field of automobile tailgate strut detection and specifically includes the following steps: real-time detection of the process of the automobile tailgate strut's telescopic push-pull cycle operation to determine whether the phenomenon of impaired push-pull action continuity occurs; when the phenomenon of impaired push-pull action continuity is detected, real-time collection of characteristic information of the entire process of the strut's push-pull action is performed through a deployed sensor network, and pre-processing is performed; analysis of the pre-processed characteristic information of the entire process is performed to assess the degree of damage when the phenomenon of impaired push-pull action continuity occurs; and corresponding measures are respectively executed according to the assessment results of the degree of damage. The present invention solves the problem that the existing one-stop offline detection of automobile tailgate struts cannot perform hierarchical screening and response according to the degree of damage of the phenomenon of impaired push-pull action continuity, and realizes real-time detection, quantitative evaluation and intelligent hierarchical processing of abnormal struts.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile tailgate support rod detection, and in particular to a one-stop offline detection method for automobile tailgate support rods. Background Art

[0002] One-stop end-of-line testing for tailgate struts involves continuous, comprehensive testing of all tailgate strut performance indicators (such as push-pull force, stroke length, airtightness, and durability) on a single testing line after production. This testing system instantly generates test data and a qualification verdict, ensuring that the product meets design standards and usage requirements before shipment. As a critical component supporting the opening and closing of the tailgate, the performance of the tailgate strut is directly related to vehicle safety and user experience, necessitating rigorous and comprehensive testing at the end-of-line stage. Traditional, step-by-step testing suffers from fragmented testing procedures, low efficiency, frequent manual intervention, and error-prone data recording, making it difficult to meet the modern automotive manufacturing requirements for efficient, accurate, and traceable quality management. One-stop end-of-line testing significantly improves testing efficiency, reduces errors caused by manual operation, ensures the consistency and integrity of test data, accelerates production lines, and reduces overall production costs. This improves the stability of strut product quality while enhancing the company's market competitiveness. Therefore, developing and applying a one-stop off-line inspection method for automobile tailgate struts is an important means to improve the manufacturing process level of struts, realize intelligent manufacturing and controllable quality.

[0003] Existing one-stop off-line inspection technology for automobile tailgate struts usually includes clamping and positioning, push-pull force testing, stroke testing, air tightness testing, durability testing, appearance testing, and data recording and judgment. The entire process is highly integrated on an automated inspection line. First, a dedicated fixture quickly and accurately positions and secures the strut to be tested, ensuring a standardized testing process. A push-pull force detection device then extends and retracts the strut, measuring its push and pull forces in real time to determine whether it meets design parameters. This is followed by a stroke test, where a sensor monitors the strut's stroke length when fully extended and retracted, ensuring its mechanical properties are within specified limits. An airtightness test applies a certain amount of air pressure inside the strut and monitors the leakage rate to determine whether the strut's sealing performance meets standards. Durability testing typically involves rapid cyclic extension and retraction, subjecting the strut to a certain number of life tests to verify its long-term reliability. Simultaneously, the system performs an appearance inspection on the strut's surface to identify defects such as scratches and dents. All test data is automatically collected and recorded by the industrial control system, and acceptance is automatically determined based on preset standards. Unqualified products are sorted and processed, achieving standardized, automated, and traceable management of the entire process. Through this testing process, the existing one-stop end-of-line testing technology ensures the stable and reliable performance and quality of each factory-produced strut while ensuring efficient testing speeds.

[0004] The existing technology has the following deficiencies:

[0005] During the durability push-pull motion test phase of the tailgate strut's one-stop end-of-line inspection process, the inspection system performs multiple continuous extension and extension push-pull cycles on the strut to assess its service life stability. During the actual inspection process, due to the loss of lubricant within the strut after multiple cycles and the uneven distribution of friction due to increased wear on the contact surfaces of the components, the push-pull motion can experience sudden slowdowns or freezes during the push-pull motion, resulting in varying degrees of impairment in the consistency of the push-pull motion. Existing one-stop end-of-line inspection technology for tailgate struts fails to implement appropriate measures based on the degree of impairment in consistency during the durability push-pull motion test. This results in an inability to promptly identify and screen abnormal trends in consistency, allowing abnormal struts with varying degrees of damage to continue flowing into the subsequent assembly process as qualified products. This, in turn, can lead to quality risks in actual use, such as improper tailgate opening and closing, shortened strut life, increased after-sales complaints, and large-scale warranty claims.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0007] The purpose of the present invention is to provide a one-stop off-line detection method for a tailgate support rod of an automobile, so as to solve the problems in the above-mentioned background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: a one-stop offline detection method for automobile tailgate support rods, specifically comprising the following steps:

[0009] Real-time detection of the tailgate support rod's telescopic, push-pull and cyclic operation process to determine whether the push-pull action continuity is impaired;

[0010] When the push-pull motion continuity is detected, the sensor network is deployed to collect the whole process characteristic information of the push-pull motion in real time and pre-process it;

[0011] Analyze the pre-processed characteristic information of the entire process and evaluate the degree of damage when the push-pull action continuity is damaged;

[0012] Implement corresponding measures based on the damage assessment results;

[0013] The test data, evaluation results and processing records are stored in the database, and the test strategy and quality trend analysis are continuously optimized based on historical data.

[0014] Preferably, the pre-processed characteristic information of the entire process is analyzed to evaluate the degree of damage when the push-pull action continuity is damaged, specifically comprising the following steps:

[0015] Extracting motion behavior disturbance information and dynamic execution deviation information from the preprocessed full-process characteristic information, and analyzing them after extraction to generate action fluctuation coefficient and pattern deviation index respectively;

[0016] An impairment assessment model is constructed based on the generated motion fluctuation coefficient and pattern deviation index, and an impairment coefficient is generated through weighted summation.

[0017] A pre-set damage coefficient threshold range is determined, and after determination, it is compared with the generated damage coefficient, and the degree of damage when the push-pull action continuous damage phenomenon occurs is evaluated based on the comparison result.

[0018] Preferably, the logic for obtaining the action fluctuation coefficient is as follows:

[0019] The motion behavior disturbance information is extracted from the pre-processed whole process characteristic information, specifically including the actual displacement of the tailgate support rod and the actual push-pull speed acting on the tailgate support rod at different times within a period of time when the push-pull action continuity is damaged, and calibrated as and , Indicates that when the push and pull action continuity is impaired for a period of time The actual displacement of the tailgate support rod of the car at the moment, Indicates that when the push and pull action continuity is impaired for a period of time The actual push and pull speed acting on the tailgate support rod at all times, , is a positive integer;

[0020] Calculate the average value of the actual displacement of the tailgate support rod of the car at all times during a period of time when the push-pull action is damaged , according to the formula: ;

[0021] Calculate the average value of the actual push and pull speed acting on the tailgate support rod of the car at all times during a period of time when the push and pull action continuity is impaired , according to the formula: ;

[0022] Calculate the action fluctuation coefficient. The specific calculation formula is as follows: Where, is the action fluctuation coefficient.

[0023] Preferably, the logic for obtaining the mode deviation index is as follows:

[0024] The dynamic execution deviation information is extracted from the pre-processed whole process characteristic information, specifically including the actual acceleration of the tailgate support rod and the push-pull resistance of the tailgate support rod at different times within a period of time when the push-pull action continuity is damaged, and calibrated as and , Indicates that when the push and pull action continuity is impaired for a period of time The actual acceleration of the tailgate support rod of the car at the moment, Indicates that when the push and pull action continuity is impaired for a period of time The push and pull resistance of the tailgate support rod of the car at all times, , is a positive integer;

[0025] By clustering and statistically modeling the acceleration and push-pull resistance data of historical qualified pole samples at each moment, the standard acceleration reference value and standard push-pull resistance reference value are determined and calibrated as and ;

[0026] Calculate the mode deviation index. The specific calculation formula is as follows: Where, is the mode deviation index.

[0027] Preferably, the generated action fluctuation coefficient and mode deviation index A damage assessment model is constructed, and the damage coefficient is generated by weighted summation. The specific calculation formula is as follows: Where, is the damage coefficient, and Action Fluctuation Coefficient and mode deviation index The non-zero weight coefficient of .

[0028] Preferably, a predetermined damage coefficient threshold interval is determined , and after determination, the damage coefficient generated Compare and evaluate the degree of damage when the push-pull action continuity is damaged based on the comparison results. The specific comparison analysis is as follows:

[0029] like , when the push-pull action continuity is impaired, the degree of damage is low;

[0030] like , when the push-pull movement continuity is impaired, the degree of damage is moderate;

[0031] like The degree of damage is severe when the continuity of pushing and pulling movements is impaired.

[0032] Preferably, corresponding measures are implemented according to the damage assessment results, specifically:

[0033] If the assessment result shows that the damage is low, the specific measures to be implemented are: the corresponding strut will be directly judged as qualified and allowed to enter the subsequent assembly process;

[0034] If the assessment result shows that the damage is moderate, the specific measures to be implemented are: re-inspection process for the corresponding brace, repeat inspection and re-testing of movement characteristics, and re-classification based on the re-inspection results;

[0035] If the assessment result shows that the degree of damage is severe, the specific measures to be implemented are: the corresponding struts will be judged as unqualified products and removed from the assembly process, and placed in the defective product processing station for isolation.

[0036] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0037] 1. This invention monitors the entire push-pull motion during a one-stop, off-line inspection of tailgate struts in real time. When a loss of push-pull motion continuity is detected, it automatically triggers data collection and preprocessing. This allows the invention to immediately detect push-pull motion anomalies caused by factors such as lubricant loss and component wear, avoiding the problem of traditional inspection techniques that rely solely on cycle counts while ignoring the details of the motion. Through precise, real-time detection, this invention significantly enhances the ability to detect potential strut continuity deterioration early, improves the accuracy and timeliness of anomaly identification, and lays a solid foundation for subsequent grading, screening, and quality control.

[0038] 2. The present invention extracts motion behavior disturbance information and dynamic execution deviation information, calculates the motion fluctuation coefficient and pattern deviation index, respectively, and generates a damage coefficient based on weighted summation, thereby achieving a quantitative assessment of the degree of damage to the consistency of push-pull movements. By comparing this with a preset damage coefficient threshold range, the degree of damage is subdivided into low, medium, and severe levels, each corresponding to a different treatment strategy. Compared to existing technologies that cannot distinguish between different levels of damage and easily mix abnormal struts with qualified products, the present invention can automatically release, re-inspect, or eliminate measures based on different levels of damage, achieving intelligent hierarchical management of strut quality, significantly reducing the missed detection rate and false positive rate, and enhancing the quality control accuracy of the detection process.

[0039] 3. This invention also integrates test data, evaluation results, and processing records into a unified database, combining historical data for quality trend analysis and test strategy optimization. This not only enables the tracing of anomalies and problem sources, but also supports the dynamic adjustment and continuous evolution of the detection model. By periodically updating the weighting parameters of the action fluctuation coefficient and the pattern deviation index, and optimizing the damage coefficient threshold setting, the system can continuously adapt to actual operating conditions such as changes in brace manufacturing batches and differences in material aging characteristics. This significantly improves the long-term stability, accuracy, and self-learning capabilities of the detection system, effectively supporting the continuous improvement of the tailgate brace product quality system and increasing customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0041] Figure 1 The present invention is a flow chart of a one-stop off-line detection method for a vehicle tailgate support rod.

[0042] Figure 2This is a method mind map of a one-stop off-line detection method for a car tailgate support rod according to the present invention. DETAILED DESCRIPTION

[0043] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0044] The present invention provides Figure 1 and Figure 2 The one-stop off-line detection method for a tailgate support rod of an automobile shown in the figure specifically comprises the following steps:

[0045] Real-time detection of the tailgate support rod's telescopic, push-pull and cyclic operation process to determine whether the push-pull action continuity is impaired;

[0046] To achieve real-time detection of the tailgate strut's telescopic, push-pull, and retractable motion, the system integrates multiple sensor types on the detection platform, including displacement sensors, acceleration sensors, a motion time acquisition module, and push-pull resistance sensors. This creates a sensor network that collects multidimensional physical property data for each push-pull motion cycle. The output signals from all sensors are uploaded to the data processing module in real time via a communication interface. This module, running on the host computer system, synchronizes, caches, and formats the received data at high frequency, and continuously stores the data in a time series format. This multidimensional, continuous, and high-frequency data acquisition process fully reproduces the motion process of each cycle, enabling real-time, full-process digital detection of the entire push-pull operation.

[0047] After acquiring the full-process detection data of the strut, the software system conducts data analysis on each action cycle by constructing a feature extraction algorithm, focusing on identifying the following types of performance: whether there is a sudden drop in the action speed during the continuous pushing and pulling process, whether the action curve is interrupted, whether there are obvious discontinuities in the acceleration change curve, whether there are abnormal fluctuations in the push-pull resistance within the cycle, etc. The system makes logical judgments on abnormal behaviors in the above multiple dimensions and sets conditional thresholds for triggering "impaired push-pull action continuity". For example, if the action speed suddenly drops by more than the set deviation within a certain cycle, there is a pause segment in the displacement-time curve, or the number of continuous abnormal actions reaches a set number, once any one or more of these conditions are met, it can be automatically determined that the strut has experienced impaired push-pull action continuity, and then enter the downstream detailed analysis process.

[0048] The reason for real-time detection and automatic judgment of the movement continuity of the support rod during the telescopic push-pull operation is that traditional durability testing methods mainly focus on whether the number of push-pull cycles meets the standards, but ignore the changes in the movement quality during the cycle. In particular, continuity anomalies caused by factors such as lubricant consumption and component wear often gradually appear in the middle and late stages of the test. Although such anomalies may still meet the standards in terms of quantitative indicators, they have actually shown an early failure trend and are very likely to cause tailgate jamming, stuttering, or poor engagement during subsequent use. Therefore, by using software to monitor the entire movement process in real time and identify impaired continuity, it is possible to discover potentially defective products earlier, achieve improved detection granularity and enhanced grading and screening capabilities, thereby building a more rigorous and intelligent quality control system, and effectively reducing after-sales risks and quality costs.

[0049] When the push-pull motion continuity is detected, the sensor network is deployed to collect the whole process characteristic information of the push-pull motion in real time and pre-process it;

[0050] In order to collect the characteristic information of the entire process of the push-pull action of the strut in real time after detecting the phenomenon of impaired push-pull action continuity, the system deploys a sensor network composed of multiple different types of sensors on the detection platform, including but not limited to displacement sensors, acceleration sensors, push-pull force sensors and time acquisition modules. These sensors are responsible for collecting key data such as displacement changes, movement trends, resistance fluctuations and action execution cycles during the action process, and establish a communication connection with the host computer or edge processing system through wired or wireless data channels. The system controls the acquisition frequency and trigger mechanism through software to achieve synchronous acquisition of dynamic parameters at every moment during the entire push-pull action process. All collected raw data will be cached in real time in a structured data format and organized and classified according to the action cycle number to ensure that the physical state changes of the entire action process can be accurately restored in the subsequent processing process.

[0051] Preprocessing the collected full-process characteristic information is a critical step in ensuring the accuracy and computational efficiency of subsequent analysis. Raw sensor data often suffers from signal noise, timeline asynchrony, and inconsistent numerical scales. Direct use can lead to biased assessments and even misjudgments. Therefore, the system performs various preprocessing operations on the raw data at the software level, including denoising filtering, outlier removal, data normalization, time synchronization correction, and discrete point interpolation and reconstruction. Denoising filtering smoothes high-frequency disturbances using a sliding window or wavelet algorithm; outlier removal identifies and removes sudden changes based on thresholds and trend differences; data normalization unifies dimensions using range normalization or Z-score normalization; time synchronization interpolates and aligns data across channels based on a master control signal; and interpolation and reconstruction restores complete trajectories when data gaps exist. This series of software processing steps effectively improves the temporal consistency, structural integrity, and physical comparability of the data, ensuring that subsequent evaluation logic is based on stable and reliable input.

[0052] Analyze the pre-processed characteristic information of the entire process and evaluate the degree of damage when the push-pull action continuity is damaged;

[0053] In this embodiment, the pre-processed characteristic information of the entire process is analyzed to evaluate the degree of damage when the push-pull action continuity is damaged, specifically including the following steps:

[0054] Extracting motion behavior disturbance information and dynamic execution deviation information from the preprocessed full-process characteristic information, and analyzing them after extraction to generate action fluctuation coefficient and pattern deviation index respectively;

[0055] At the software implementation level, extracting motion behavior disturbance information and dynamic execution deviation information from preprocessed full-process characteristic information requires first dimensional classification and structural mapping of the preprocessed time series data. Multiple characteristic data types, such as displacement, velocity, acceleration, and push-pull resistance, corresponding to each moment are reconstructed into a multidimensional data matrix under a unified time axis. Subsequently, based on data labeling rules and signal feature extraction models, the system automatically selects column vectors related to the "displacement-velocity" coupling characteristics to construct motion behavior disturbance information. It also extracts column vectors related to the "acceleration-resistance" coupling pattern and its deviation from the historical template to construct dynamic execution deviation information. During this process, the system invokes built-in data channel screening rules and template alignment mechanisms. Through sliding window analysis, sampling frequency alignment, and curve normalization, it achieves alignment and extraction of multidimensional behavioral features with standard data. Ultimately, it outputs structured disturbance information sets and deviation information sets, providing feature input for subsequent parameter calculation modules.

[0056] An impairment assessment model is constructed based on the generated motion fluctuation coefficient and pattern deviation index, and an impairment coefficient is generated through weighted summation.

[0057] A pre-set damage coefficient threshold range is determined, and after determination, it is compared with the generated damage coefficient, and the degree of damage when the push-pull action continuous damage phenomenon occurs is evaluated based on the comparison result.

[0058] In order to determine the pre-set damage coefficient threshold range, the system first needs to build a multi-level damage level label library based on historical sample data. The library consists of a large number of pole detection samples labeled manually or by empirical rules. The samples include the corresponding motion fluctuation coefficient, pattern deviation index and actual verification results. Subsequently, the system performs cluster analysis and statistical distribution modeling on the sample set, such as using the Gaussian mixture model (GMM) or K-means clustering algorithm to fit and segment the distribution trend of the damage coefficient between different damage levels, and combines the actual product quality feedback to perform soft constraint optimization on the interval boundary. The system finally automatically generates a multi-segment damage coefficient threshold range based on the matching degree of the clustering results and the quality label, usually including three levels: mild, moderate and severe, and writes it into the rule module in the form of configuration parameters for real-time comparison and damage judgment call, so as to achieve adaptive setting of the threshold range and long-term maintainable update.

[0059] In this embodiment, the logic for obtaining the action fluctuation coefficient is as follows:

[0060] The motion behavior disturbance information is extracted from the pre-processed whole process characteristic information, specifically including the actual displacement of the tailgate support rod and the actual push-pull speed acting on the tailgate support rod at different times within a period of time when the push-pull action continuity is damaged, and calibrated as and , Indicates that when the push and pull action continuity is impaired for a period of time The actual displacement of the tailgate support rod of the car at the moment, Indicates that when the push and pull action continuity is impaired for a period of time The actual push and pull speed acting on the tailgate support rod at all times, , is a positive integer;

[0061] During the push-pull motion detection process, in order to achieve real-time acquisition of the actual displacement and actual push-pull speed of the tailgate support at each moment when continuous damage occurs, the detection system uses displacement sensors and speed encoders integrated into the actuator to build a high-precision data acquisition network. The displacement sensor can take the form of a photoelectric encoder, linear potentiometer, or laser displacement meter to record the real-time travel changes of the support during the push-pull process. Its output signal is synchronously sent to the data processing system through the sampling module. The software can automatically collect the corresponding displacement value at each moment within the set time interval. , forming a continuous displacement time series. At the same time, the push-pull speed is not derived from a fixed set value, but is automatically deduced by the system based on the change in the displacement of the support rod per unit time, or directly outputs the real-time push-pull speed value through the speed encoder After collecting data, the software system will perform pre-processing such as time sequence alignment, exception elimination and unit conversion, so that the displacement and speed data at each moment can be accurately associated, thereby fully recording the real dynamic process of the push-pull action over a period of time. Indicates in At each moment, the distance change of the push-pull stroke of the support rod relative to the initial position reflects the degree of completion of the mechanical execution; and the actual push-pull speed This represents the instantaneous velocity of the system's action on the pole at that moment, reflecting the rhythm and stability of the push-pull motion. These two types of data, as the core physical foundation for motion disturbance assessment, accurately capture discontinuities, sudden changes, or fluctuations that occur during the push-pull process, providing high-quality input for the subsequent calculation of the motion fluctuation coefficient.

[0062] Calculate the average value of the actual displacement of the tailgate support rod of the car at all times during a period of time when the push-pull action is damaged , according to the formula: ;

[0063] Calculate the average value of the actual push and pull speed acting on the tailgate support rod of the car at all times during a period of time when the push and pull action continuity is impaired , according to the formula: ;

[0064] Calculate the action fluctuation coefficient. The specific calculation formula is as follows: Where, is the action fluctuation coefficient.

[0065] In order to accurately reflect the degree of motion disturbance of the tailgate support rod during the pushing and pulling process, the motion fluctuation coefficient ( The calculation formula of ) adopts the exponential weighted difference accumulation method based on time series. In this formula, the actual displacement at different times during a period of time when the push-pull action is damaged is first calculated. The actual push and pull speed Perform moment-by-moment combination operations, through The displacement value is coupled with the influence of velocity in the form of It is used to enhance the disturbance effect caused by small displacement fluctuations at high speed, making the disturbance more sensitive when the push-pull speed suddenly changes; then the coupling value is combined with the average displacement and average speed coupling value in the time period The difference is calculated to measure the relative offset between the action state at each moment and the overall stable state. The absolute value of the offset is then averaged across all moments to comprehensively assess the amplitude of fluctuations during the action. Finally, the overall difference is wrapped in a natural logarithmic function, which can both suppress interference in low-fluctuation backgrounds and significantly amplify the output during severe fluctuations, ensuring that the coefficient has both sufficient sensitivity and numerical stability. This computational design can effectively capture the changes in disturbances caused by setbacks, sudden stops, or non-uniform speeds during push-pull movements, providing a quantitative basis for subsequent determination of impaired coherence.

[0066] Action fluctuation coefficient The value of is positively correlated with the degree of damage to the push-pull action continuity phenomenon. The core of this coefficient is that it quantifies the degree of dynamic disturbance of the support rod during the push-pull process. When the tailgate support rod runs smoothly during the extension and retraction process and the speed and displacement changes are stable, the and the overall average state The difference is small, the cumulative disturbance value is limited, and after logarithmic compression, The coefficient is maintained at a low level; on the contrary, when the pole is stuck, unevenly pushed and pulled, or the speed suddenly changes, the deviation between the single moment and the mean state is amplified sharply, resulting in an increase in the absolute difference and further amplification under the exponential operation, which ultimately makes Therefore, The higher the value, the more intense the disturbance and the more serious the damage to the movement continuity during the pushing and pulling process. It can be used as an important quantitative basis for measuring the degree of damage to the push-pull movement continuity.

[0067] In this embodiment, the logic for obtaining the mode deviation index is as follows:

[0068] The dynamic execution deviation information is extracted from the pre-processed whole process characteristic information, specifically including the actual acceleration of the tailgate support rod and the push-pull resistance of the tailgate support rod at different times within a period of time when the push-pull action continuity is damaged, and calibrated as and , Indicates that when the push and pull action continuity is impaired for a period of time The actual acceleration of the tailgate support rod of the car at the moment, Indicates that when the push and pull action continuity is impaired for a period of time The push and pull resistance of the tailgate support rod of the car at all times, , is a positive integer;

[0069] During the push-pull motion detection process, in order to obtain the actual acceleration and push-pull resistance of the tailgate support rod in real time, the system integrates the acceleration sensor and the push-pull force sensor on the detection actuator and builds a data acquisition channel to transmit various sensor signals to the central processing module in real time. The acceleration sensor can be installed on the structural member near the support rod connection point to sense the linear acceleration change of the support rod during the extension and retraction process. The collected signal is sampled at a fixed time interval to generate an acceleration sequence. The system calibrates the acceleration value at each moment according to the sampling number in the software. , which is used to characterize the dynamic response strength of the action at that moment. At the same time, the push-pull force sensor is installed on the driving end or the intermediate force transmission component that applies the driving force, and is used to sense the tension or pressure applied by the system to the support rod in real time. The digital signal is output through the force-electricity conversion module and recorded by the host computer as the push-pull resistance value. , represents the stress state of the support rod at that moment. After data collection is completed, the software system will automatically synchronize the time axis, perform data denoising and unit normalization operations to ensure that the one-to-one correspondence between acceleration and push and pull resistance is accurate. Actual acceleration It is used to reveal the inertial response characteristics of the brace structure during the movement process, reflecting whether it has hysteresis, mutation or unstable behavior; and the push-pull resistance The metric reflects changes in load and friction during movement, serving as a crucial physical indicator for identifying factors impairing continuity, such as insufficient lubrication, sealing anomalies, or component wear. Together, these two types of data form the foundation for multi-dimensional monitoring of movement stability and structural integrity, providing the core input for the subsequent calculation of the Mode Deviation Index.

[0070] By clustering and statistically modeling the acceleration and push-pull resistance data of historical qualified pole samples at each moment, the standard acceleration reference value and standard push-pull resistance reference value are determined and calibrated as and ;

[0071] To determine the standard acceleration and push-pull resistance reference values ​​for comparison, the system, using a software platform, centrally archives the acceleration and push-pull resistance data collected from historically qualified pole samples at different sampling points during testing over a period of time. This data is then structured along a timeline to form a two-dimensional behavioral data matrix across the samples. Subsequently, at each standard time point, the system clusters the acceleration and push-pull resistance data for all samples at that time. Using a K-means algorithm or a density-based clustering algorithm (such as DBSCAN), the system automatically classifies data points with similar behavioral patterns. After removing outliers, the center of the main cluster is extracted as the standard reference value for the current moment. To enhance robustness, the system also performs Gaussian fitting or kernel density estimation on the sample distribution at each time point after clustering. This further smooths the trends of the reference values ​​and avoids sudden changes caused by individual sample anomalies or test fluctuations. This entire process is automated by the software module, requiring no human intervention and supporting dynamic model updates as new samples accumulate. Ultimately, the system will form a set of stable and reusable reference template data sequences at all standard moments, corresponding to standard acceleration reference values ​​and standard push-pull resistance reference values, which will serve as a reference for subsequent evaluation of the degree of deviation of the current pole vaulting movement pattern.

[0072] Calculate the mode deviation index. The specific calculation formula is as follows: Where, is the mode deviation index.

[0073] In order to accurately evaluate the overall deviation between the push-pull action process of the tailgate support and the standard action template, the pattern deviation index The calculation formula is designed based on the cumulative summation method of the difference in absolute acceleration and the nonlinear amplification difference in push-pull resistance. In this formula, the actual acceleration of the tailgate support rod of the car at each moment is first calculated. Perform square root processing, that is To ensure that the acceleration reflects the action intensity uniformly regardless of the positive or negative direction, to avoid directional interference, and at the same time to compare with the standard acceleration reference value Make the absolute value difference to quantify the degree of deviation between the dynamic characteristics at the current moment and the normal state; secondly, the actual push and pull resistance at each moment Perform exponential operation , by nonlinearly amplifying the abnormal performance caused by small resistance fluctuations, and comparing it with the standard push-pull resistance reference value The absolute value difference is calculated to enhance the sensitivity to subtle mechanical anomalies; then, the acceleration difference and the push-pull resistance difference at each moment are summed to comprehensively reflect the overall deviation amplitude at that moment, and finally the deviation values ​​at all moments are averaged to ensure that the evaluation result not only reflects the anomaly at a single moment, but also comprehensively reflects the overall trend of the degradation of the action coherence over a period of time. The higher the value, the more obvious the deviation of the pushing and pulling action pattern from the standard template, the worse the stability of the pole movement, and the more serious the degree of continuity damage.

[0074] Mode Deviation Index The value of is positively correlated with the degree of damage to the push-pull motion continuity phenomenon, which essentially reflects the overall deviation between the dynamic behavior characteristics of the tailgate support rod during the push-pull process and the standard motion template. When the support rod movement is stable and the structural performance is normal, its acceleration and push-pull resistance curves at each moment are highly consistent with the standard template. The difference generated in the calculation is small, and the final index value is at a low level; however, when the support rod has abnormal fluctuations, sudden increase in friction, mechanical unevenness or response lag during the pushing and pulling process, the actual performance of acceleration and resistance will continue to deviate from the template, and the difference terms will continue to accumulate, eventually making The index increased significantly. The larger the value is, the more serious the deviation of the pushing and pulling action from the normal state in the overall behavior pattern is. The system can quantify the severity of the damage to the coherence of the pushing and pulling action based on this value, and use it as one of the important bases for determining the damage level.

[0075] In this embodiment, the generated action fluctuation coefficient and mode deviation index A damage assessment model is constructed, and the damage coefficient is generated by weighted summation. The specific calculation formula is as follows: Where, is the damage coefficient, and Action Fluctuation Coefficient and mode deviation index The non-zero weight coefficient of .

[0076] In order to achieve the action fluctuation coefficient Deviation index from the pattern The system builds a damage assessment model at the software level, performs weighted summation based on the preset weight coefficients, and generates a comprehensive damage coefficient. This process is automatically completed by the model calculation module, in which the system completes and After the calculation of , both are input into the fusion model in floating point form and The weight coefficient is processed by the formula. and Are all non-zero real numbers, used for control and The contribution ratio in the final damage assessment is fixed to 1 to ensure that the calculation results are kept in a uniform dimension. The weight setting can be based on the historical samples. and The discrimination effect of actual fault conditions is automatically optimized through minimum error method, regression fitting or machine learning model. For example, when historical analysis shows that More sensitive to damage than When the system can dynamically set a larger The value is used to improve the recognition weight of the difference in behavior patterns. The resulting damage coefficient Comprehensive consideration of the pole's performance in two dimensions, movement stability and behavioral deviation, can be used to more finely divide the damage level and drive subsequent treatment strategies.

[0077] In this embodiment, the pre-set damage coefficient threshold interval is determined , and after determination, the damage coefficient generated Compare and evaluate the degree of damage when the push-pull action continuity is damaged based on the comparison results. The specific comparison analysis is as follows:

[0078] like , when the push-pull action continuity is impaired, the degree of damage is low;

[0079] This indicates that the dynamic behavior parameters of the strut during push-pull motions deviate minimally from the standard template, indicating minimal fluctuations in motion and a stable behavior pattern, with no apparent discontinuities, stalls, or friction anomalies. In this case, the strut exhibited good structural integrity and consistency of motion during the test cycle, a typical example of a normal specimen, indicating its performance has not been significantly affected by wear or aging. The system determined the damage to be low, meaning the strut can be directly classified as qualified and entered the subsequent assembly process without the need for re-inspection or special handling, thereby improving inspection efficiency and reducing costs.

[0080] like , when the push-pull movement continuity is impaired, the degree of damage is moderate;

[0081] This situation indicates that the strut has experienced a certain degree of volatility or deviation in its movement pattern during the inspection cycle, which may manifest as occasional setbacks during pushing and pulling, slight discontinuities in acceleration, or abnormal force peaks. Although it has not reached the level of serious failure, its behavior trend has deviated from the standard template, and there are potential signs of structural looseness, lubrication degradation, or early signs of component wear. The system has determined it to be moderately damaged, which means that although the strut has not yet shown a clear fault, there are risks to its stability. It usually needs to be transferred to a re-inspection or secondary movement test for confirmation to prevent potential quality problems from flowing into the final assembly process and ensure the consistency and reliability of the finished product.

[0082] like The degree of damage is severe when the continuity of pushing and pulling movements is impaired.

[0083] This indicates that the strut experienced significant degradation in motion continuity during testing, with violent fluctuations in motion, and changes in acceleration and resistance significantly deviating from the standard template, possibly accompanied by noticeable sticking, friction anomalies, or motion interruptions. This condition is typically caused by lubricant failure, component fatigue damage, or assembly deviations, and has reached an unacceptable failure boundary. The system determines that the damage is severe, meaning that the strut no longer meets the requirements for use and must be removed from the production process as a defective product to prevent the tailgate from opening and closing smoothly after subsequent installation, premature failure, or the risk of user complaints and large-scale warranty claims.

[0084] Implement corresponding measures based on the damage assessment results;

[0085] In this embodiment, corresponding measures are executed according to the damage assessment results, specifically:

[0086] If the assessment result shows that the damage is low, the specific measures to be implemented are: the corresponding strut will be directly judged as qualified and allowed to enter the subsequent assembly process;

[0087] When the assessment result indicates that the degree of damage is low, the software triggers the low-level status response process. This process automatically identifies the "low-level" result label through the comparison logic module in the background, writes the qualified mark to the data management module, and binds the unique code of the brace to the test result and transmits it to the MES (Manufacturing Execution System) interface to achieve status synchronization on the production line. Once the qualified status is confirmed, the system will place the brace in the "automatic release queue". The control logic drives the mechanical platform to transport it to the subsequent assembly unit, eliminating manual review or buffer zone diversion, thereby simplifying the process and improving cycle efficiency. This method ensures automatic judgment and release without sacrificing quality, helps shorten the response cycle in the inspection-assembly chain, and increases the circulation rate of qualified products.

[0088] If the assessment result shows that the damage is moderate, the specific measures to be implemented are: re-inspection process for the corresponding brace, repeat inspection and re-testing of movement characteristics, and re-classification based on the re-inspection results;

[0089] When the assessment result shows that the degree of damage is moderate, the system will automatically mark the strut as "medium" and trigger the re-inspection process through the workflow engine. In specific operations, the software will write the result to the exception handling queue and call the re-inspection scheduling logic to designate the strut as the "re-inspection priority object", guiding it to be sent to the secondary action characteristic detection module, repeating the push-pull action several times, and collecting new full-process characteristic information at the same time. The re-inspection data will be compared with the difference evaluation model of the initial data. If the difference decreases significantly or the state is stable, the strut can be reclassified as qualified; otherwise, it will be converted to an unqualified state and the elimination process will be executed. This strategy allows the system to tolerate slight random disturbances or marginal anomalies, improves the fault tolerance of the detection system for mild fluctuations, and prevents the loss of qualified products caused by misjudgment, ensuring a balance between yield and risk control.

[0090] If the assessment result shows that the degree of damage is severe, the specific measures to be implemented are: the corresponding struts will be judged as unqualified products and removed from the assembly process, and placed in the defective product processing station for isolation.

[0091] When the assessment result shows that the degree of damage is severe, the software will immediately mark the strut as "severe" and trigger the logic for rejecting defective products. At this time, the system will write the identification code and defective grade information of the strut into the quality database, and at the same time control the mechanical sorting device through industrial control instructions to divert the strut from the main line conveying path to the defective product isolation area. In order to ensure the consistency of subsequent processing, the system will also upload the batch number, test data and judgment reasons to the central quality traceability module, and generate an alarm prompt in the interface system for quality inspectors to review. This operation ensures that obviously abnormal struts will not flow into subsequent links, blocking potential quality risks at the source. It is a key mechanism to ensure product stability and customer satisfaction. It is completely based on software automation and has high responsiveness and full process traceability.

[0092] The test data, evaluation results and processing records are stored in the database, and the test strategy and quality trend analysis are continuously optimized based on historical data.

[0093] After the system completes push-pull motion detection, damage assessment, and action decision-making for the tailgate strut, all core data related to the process is structured and encapsulated by the data management module and written to the backend database. This storage includes, but is not limited to, full-process characteristic data (such as time series of displacement, velocity, acceleration, and resistance), calculated intermediate parameters (such as motion fluctuation coefficient, mode deviation index, and damage coefficient), the system-determined damage level, and the final action taken (such as release, re-inspection, or rejection). After each processing step, the software system automatically invokes the database interface via a data write command. This information is stored in a structured format (such as JSON, CSV, or SQL table) in the quality database, indexed by fields such as strut code, inspection time, and action result, enabling data searchability, traceability, and multi-dimensional query capabilities. This mechanism ensures a complete historical record for each inspected object, providing fundamental data support for subsequent data analysis and quality management.

[0094] To achieve continuous optimization of detection strategies and long-term analysis of product quality trends, the system uses the data analysis module built into the software platform to regularly perform batch statistics and model operations on the stored historical detection data. In the specific implementation process, the system can filter historical samples according to dimensions such as time period, batch number, abnormality category or parameter fluctuation range, and use cluster analysis, regression models or machine learning algorithms to identify typical patterns, boundary features and evolution trends of abnormal occurrences. For example, by comparing the distribution differences of the initial characteristic data of struts with different damage levels, the system can dynamically adjust the weights of the action fluctuation coefficient and the pattern deviation index to optimize the evaluation model of the damage coefficient; for example, by observing the downward trend of the qualified rate in a certain period of time, the system can trigger an automatic alarm and recommend adjustments to the detection parameters or assembly links. This software solution based on a data closed-loop feedback mechanism can achieve intelligent evolution of detection logic and early warning of quality problems. It is a key support capability to ensure the long-term stable operation of the system and continuously improve the yield rate.

[0095] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0096] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means (e.g., infrared, wireless, microwave, etc.). A computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0097] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0098] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0099] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0100] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0101] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0102] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A one-stop off-line detection method for automobile tailgate support rods, characterized in that: The specific steps include: Real-time detection of the tailgate support rod's telescopic, push-pull and cyclic operation process to determine whether the push-pull action continuity is impaired; When the push-pull motion continuity is detected, the sensor network is deployed to collect the whole process characteristic information of the push-pull motion in real time and pre-process it; Analyze the pre-processed characteristic information of the entire process and evaluate the degree of damage when the push-pull action continuity is damaged; The specific steps include: Extracting motion behavior disturbance information and dynamic execution deviation information from the preprocessed full-process characteristic information, and analyzing them after extraction to generate action fluctuation coefficient and pattern deviation index respectively; The logic for obtaining the action fluctuation coefficient is as follows: The motion behavior disturbance information is extracted from the pre-processed whole process characteristic information, specifically including the actual displacement of the tailgate support rod and the actual push-pull speed acting on the tailgate support rod at different times within a period of time when the push-pull action continuity is damaged, and calibrated as and , Indicates that when the push and pull action continuity is impaired for a period of time The actual displacement of the tailgate support rod of the car at the moment, Indicates that when the push and pull action continuity is impaired for a period of time The actual push and pull speed acting on the tailgate support rod at all times, , is a positive integer; Calculate the average value of the actual displacement of the tailgate support rod of the car at all times during a period of time when the push-pull action is damaged , according to the formula: ; Calculate the average value of the actual push and pull speed acting on the tailgate support rod of the car at all times during a period of time when the push and pull action continuity is impaired , according to the formula: ; Calculate the action fluctuation coefficient. The specific calculation formula is as follows: Where, is the action fluctuation coefficient; The logic for obtaining the mode deviation index is as follows: The dynamic execution deviation information is extracted from the pre-processed whole process characteristic information, specifically including the actual acceleration of the tailgate support rod and the push-pull resistance of the tailgate support rod at different times within a period of time when the push-pull action continuity is damaged, and calibrated as and , Indicates that when the push and pull action continuity is impaired for a period of time The actual acceleration of the tailgate support rod of the car at the moment, Indicates that when the push and pull action continuity is impaired for a period of time The push and pull resistance of the tailgate support rod of the car at all times, , is a positive integer; By clustering and statistically modeling the acceleration and push-pull resistance data of historical qualified pole samples at each moment, the standard acceleration reference value and standard push-pull resistance reference value are determined and calibrated as and ; Calculate the mode deviation index. The specific calculation formula is as follows: Where, is the mode deviation index; An impairment assessment model is constructed based on the generated motion fluctuation coefficient and pattern deviation index, and an impairment coefficient is generated through weighted summation. Determine a pre-set damage coefficient threshold range, compare it with the generated damage coefficient after determination, and evaluate the damage degree when the push-pull action coherent damage phenomenon occurs based on the comparison result; Implement corresponding measures based on the damage assessment results; The test data, evaluation results and processing records are stored in the database, and the test strategy and quality trend analysis are continuously optimized based on historical data.

2. The one-stop off-line detection method for automobile tailgate support rods according to claim 1, characterized in that: The generated action fluctuation coefficient and mode deviation index A damage assessment model is constructed, and the damage coefficient is generated by weighted summation. The specific calculation formula is as follows: Where, is the damage coefficient, and Action Fluctuation Coefficient and mode deviation index The non-zero weight coefficient of .

3. The one-stop off-line detection method for automobile tailgate support rods according to claim 2, characterized in that: Determine the pre-set damage coefficient threshold range , and after determination, the damage coefficient generated Compare and evaluate the degree of damage when the push-pull action continuity is damaged based on the comparison results. The specific comparison analysis is as follows: like , when the push-pull action continuity is impaired, the degree of damage is low; like , when the push-pull movement continuity is impaired, the degree of damage is moderate; like The degree of damage is severe when the continuity of pushing and pulling movements is impaired.

4. The one-stop off-line detection method for automobile tailgate support rods according to claim 3 is characterized in that: Based on the damage assessment results, corresponding measures will be implemented, specifically: If the assessment result shows that the damage is low, the specific measures to be implemented are: the corresponding strut will be directly judged as qualified and allowed to enter the subsequent assembly process; If the assessment result shows that the damage is moderate, the specific measures to be implemented are: re-inspection process for the corresponding brace, repeat inspection and re-testing of movement characteristics, and re-classification based on the re-inspection results; If the assessment result shows that the degree of damage is severe, the specific measures to be implemented are: the corresponding struts will be judged as unqualified products and removed from the assembly process, and placed in the defective product processing station for isolation.

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