Multi-parameter fusion analysis method for fault diagnosis of automobile shock absorber

Abstract

The invention discloses a multi-parameter fusion analysis method for fault diagnosis of an automobile shock absorber, and relates to the technical field of shock absorber analys.The method comprises the steps that a vehicle key point position set is obtained, and a sensor set is arranged to obtain a vehicle work sensing module; a vehicle working multi-sensing data flow is monitored and obtained, and a vibration damper working characteristic parameter set is obtained; optimizing to obtain a target data fusion algorithm, and obtaining working state fusion characteristic parameters of the shock absorber; carrying out matching degree calculation on the fusion characteristic parameters of the working state of the shock absorber; and carrying out fault judgment analysis on the dynamic matching degree of the shock absorber, determining fault diagnosis information of the shock absorber, and carrying out fault feedback optimization. The technical problems that the condition of the shock absorber is difficult to comprehensively and accurately detect and the fault part cannot be accurately and rapidly diagnosed and positioned in the existing shock absorber fault diagnosis parameter analysis, so that the comprehensiveness and reliability of the fault diagnosis of the automobile shock absorber are insufficient are solved, and the technical effects of improving the fault diagnosis efficiency and accuracy and the performance of the shock absorber are achieved.

Description

Technical Field

[0001] The present application relates to the technical field related to shock absorber analysis, and in particular to a multi-parameter fusion analysis method for automobile shock absorber fault diagnosis. Background Art

[0002] With the rapid development of modern automobile technology, the performance and driving comfort of vehicles have been significantly improved. As a key component of the automobile suspension system, the shock absorber is mainly responsible for absorbing the energy generated by uneven road surface or body vibration during vehicle driving, ensuring good contact between the wheel and the ground, and maintaining the stability and comfort of the vehicle. However, due to long-term use and complex and changeable driving environment, the shock absorber will wear, age or fail over time, thus affecting the handling and safety of the vehicle. For example, different road conditions, driving habits and ambient temperature changes can easily lead to performance degradation or even failure, which in turn affects the overall performance and safety of the vehicle. How to accurately diagnose the fault of the shock absorber, find problems in time and perform maintenance has become an important challenge for modern automobile manufacturing. Traditional shock absorber fault diagnosis methods often rely on single parameter monitoring, such as only detecting vibration through acceleration sensors. Although it can reflect the working status of the shock absorber to a certain extent, it is limited by single parameters and incomplete information, and it is difficult to fully and accurately diagnose the root cause of the fault. Vehicles of different models and configurations have different structural characteristics. Traditional methods lack universality and flexibility and are difficult to meet the increasingly diverse needs of the automobile market.

[0003] At present, there are technical problems in the related technologies of automobile shock absorber parameter fusion analysis, such as difficulty in comprehensively and accurately detecting the condition of the shock absorber, and inability to accurately and quickly diagnose and locate the faulty part, which leads to insufficient comprehensiveness and reliability of automobile shock absorber fault diagnosis. Summary of the invention

[0004] The present application provides a multi-parameter fusion analysis method for automobile shock absorber fault diagnosis, thereby solving the technical problems that the existing shock absorber fault diagnosis parameter analysis has difficulties in comprehensively and accurately detecting the shock absorber condition, and cannot accurately and quickly diagnose and locate the fault location, which leads to insufficient comprehensiveness and reliability of automobile shock absorber fault diagnosis, thereby achieving the technical effect of improving fault diagnosis efficiency, accuracy and shock absorber performance.

[0005] The present application provides a multi-parameter fusion analysis method for automobile shock absorber fault diagnosis, including: performing key point analysis on the structural characteristic information of the target vehicle to obtain a vehicle key point set, deploying a sensor group at the vehicle key point set to obtain a vehicle work perception module, the vehicle work perception module includes an angle sensor, a height sensor, a pressure sensor and an acceleration sensor; monitoring and obtaining a vehicle work multi-sensor data stream through the vehicle work perception module, performing parallel preprocessing and correlation feature extraction on the vehicle work multi-sensor data stream, and obtaining a shock absorber work characteristic parameter set; optimizing and obtaining a target data fusion algorithm , the target data fusion algorithm is used to fuse the shock absorber working characteristic parameter set according to the time series information to obtain the shock absorber working state fusion characteristic parameters; the driving scene information of the target vehicle is analyzed for influencing factors to determine the suspension balance influencing factor, and the suspension balance influencing factor is introduced to calculate the matching degree of the shock absorber working state fusion characteristic parameters to obtain the shock absorber dynamic matching degree; the dynamic matching degree of the shock absorber is analyzed for fault judgment based on a preset matching degree change threshold to determine the shock absorber fault diagnosis information, and the target vehicle is optimized for fault feedback based on the shock absorber fault diagnosis information.

[0006] In a possible implementation, the obtaining of the shock absorber operating characteristic parameter set further performs the following processing: performing noise characteristic analysis on the vehicle operating multi-sensor data streams respectively to obtain vehicle multi-sensor data noise characteristic information; designing a multi-noise filter based on the vehicle multi-sensor data noise characteristic information, and using the multi-noise filter to perform mapping preprocessing on the vehicle operating multi-sensor data stream to obtain a vehicle operating standard multi-sensor data stream; performing shock absorber associated feature analysis on the vehicle operating standard multi-sensor data stream to obtain a shock absorber associated feature set; and performing calculation and extraction on the vehicle operating standard multi-sensor data stream according to the shock absorber associated feature set to obtain the shock absorber operating characteristic parameter set.

[0007] In a possible implementation, the optimization for obtaining a target data fusion algorithm further performs the following processing: performing a fusion requirement analysis on the shock absorber working characteristic parameter set to obtain data fusion requirement characteristic information; constructing a data fusion algorithm list, performing characteristic matching based on the data fusion requirement characteristic information and the data fusion algorithm list to obtain an available data fusion algorithm; performing fusion verification and evaluation on the shock absorber working characteristic parameter set through the available data fusion algorithm to obtain data fusion performance evaluation parameters; and iteratively optimizing the available data fusion algorithm based on the data fusion performance evaluation parameters to obtain the target data fusion algorithm.

[0008] In a possible implementation, the acquisition of the target data fusion algorithm further performs the following processing: constructing an algorithm optimization strategy space, performing optimization direction analysis on the algorithm optimization strategy space based on the data fusion performance evaluation parameters, and determining the algorithm optimization direction; performing local iterative optimization search in the algorithm optimization strategy space according to the algorithm optimization direction, and obtaining algorithm optimization strategy parameters; and optimizing the configuration update of the available data fusion algorithm based on the algorithm optimization strategy parameters to obtain the target data fusion algorithm.

[0009] In a possible implementation, the determination of the suspension balance influencing factor also performs the following processing: extracting signal influencing parameters of the driving scene information of the target vehicle to obtain vehicle signal influencing characteristic parameters, wherein the vehicle signal influencing characteristic parameters include vehicle driving environment conditions, vehicle driving status and vehicle structural characteristics; performing signal delay analysis on both sides of the suspension based on the vehicle signal influencing characteristic parameters to obtain a suspension sensor signal delay factor; performing signal compensation analysis on both sides of the suspension for the suspension sensor signal delay factor to determine the suspension balance influencing factor.

[0010] In a possible implementation, the obtaining of the dynamic matching degree of the shock absorber further performs the following processing: obtaining a shock absorber matching degree index set, wherein the shock absorber matching degree index set includes an amplitude matching degree, a frequency matching degree, and a damping force matching degree; performing a distribution calculation on both sides of the suspension for the shock absorber working state fusion characteristic parameters according to the shock absorber matching degree index set to obtain an amplitude time series matching degree set, a frequency time series matching degree set, and a damping force time series matching degree set; constructing a shock absorber performance characteristic matrix according to the amplitude time series matching degree set, the frequency time series matching degree set, and the damping force time series matching degree set: ; Where R is the shock absorber performance characteristic matrix, T is the number of timing matching degrees on both sides of the suspension, is the amplitude matching degree at the Tth time sequence, is the frequency matching degree at the Tth time sequence, is the damping force matching degree at the Tth time sequence; a dynamic matching degree calculation is performed based on the shock absorber performance characteristic matrix to obtain the dynamic matching degree of the shock absorber.

[0011] In a possible implementation, the obtaining of the dynamic matching degree of the shock absorber further includes performing the following processing: performing maximization forward processing and normalization processing on the shock absorber performance characteristic matrix to obtain a normalized shock absorber performance characteristic matrix; constructing a matching evaluation function, and performing matching scoring on each time series object in the normalized shock absorber performance characteristic matrix based on the matching evaluation function to obtain T time series object matching scores; performing standard deviation and mean calculation on the matching scores of the T time series objects to obtain the matching score standard deviation and the matching score average; performing variation calculation based on the matching score standard deviation and the matching score average to obtain the dynamic matching degree of the shock absorber.

[0012] In a possible implementation, a matching evaluation function is constructed, and the following processing is further performed: the matching evaluation function is specifically: ; ; ; in, is the matching score for the i-th time series, , and are the weights of amplitude matching, frequency matching, and damping force matching, which can be determined according to the degree of influence on the shock absorber performance. , and are the maximum values ​​of the elements in each matrix column, , and are the minimum values ​​of the elements of each matrix column, , and is the amplitude matching, frequency matching, and damping force matching at the i-th time sequence.

[0013] It is intended to use the multi-parameter fusion analysis method for automobile shock absorber fault diagnosis proposed in this application to perform key point analysis on the structural characteristic information of the target vehicle to obtain a set of vehicle key points, and to deploy a sensor group at the vehicle key point set to obtain a vehicle working perception module, wherein the vehicle working perception module includes a rotation angle sensor, a height sensor, a pressure sensor, and an acceleration sensor; the vehicle working multi-sensor data stream is monitored and obtained through the vehicle working perception module, and the vehicle working multi-sensor data stream is parallel preprocessed and associated feature extraction is performed to obtain a set of shock absorber working characteristic parameters; the target data fusion algorithm is optimized to obtain The method adopts the target data fusion algorithm to fuse the shock absorber working characteristic parameter set according to the time series information to obtain the shock absorber working state fusion characteristic parameter; the driving scene information of the target vehicle is analyzed for influencing factors to determine the suspension balance influencing factor, and the suspension balance influencing factor is introduced to calculate the matching degree of the shock absorber working state fusion characteristic parameter to obtain the shock absorber dynamic matching degree; the dynamic matching degree of the shock absorber is analyzed for fault judgment based on the preset matching degree change threshold to determine the shock absorber fault diagnosis information, and the target vehicle is optimized for fault feedback based on the shock absorber fault diagnosis information. The technical problem that the existing shock absorber fault diagnosis parameter analysis is difficult to comprehensively and accurately detect the shock absorber status, and the fault location cannot be accurately and quickly diagnosed and located, which leads to insufficient comprehensiveness and reliability of automobile shock absorber fault diagnosis, is solved, and the technical effect of improving fault diagnosis efficiency, accuracy and shock absorber performance is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0015] Figure 1 A schematic diagram of a flow chart of a multi-parameter fusion analysis method for automobile shock absorber fault diagnosis provided in an embodiment of the present application; Figure 2 A schematic diagram of a flow chart of obtaining a set of operating characteristic parameters of a shock absorber in a multi-parameter fusion analysis method for automobile shock absorber fault diagnosis provided in an embodiment of the present application. DETAILED DESCRIPTION

[0016] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0017] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0018] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0019] The present application embodiment provides a multi-parameter fusion analysis method for automobile shock absorber fault diagnosis, such as Figure 1 As shown, the method includes: Step S100, performing key point analysis on the structural characteristic information of the target vehicle to obtain a vehicle key point set, deploying a sensor group at the vehicle key point set to obtain a vehicle work perception module, wherein the vehicle work perception module includes a rotation angle sensor, a height sensor, a pressure sensor, and an acceleration sensor.

[0020] Preferably, the structural characteristic information of the target vehicle refers to the overall mechanical structure of the target vehicle, the suspension system design, the relative position of the body and wheels, etc. The design of each vehicle is different, and the structural characteristic information can help determine at which locations to install sensors to most effectively capture data related to the shock absorber. Specifically, key point analysis refers to analyzing the structural characteristics of the vehicle to determine the key locations most relevant to shock absorber fault diagnosis. These locations are usually the suspension system, the connection between the wheels and the body, or other areas that reflect the working status of the shock absorber. Key point analysis is used to find the most suitable locations for sensor layout, that is, to obtain a set of vehicle key points, including multiple specific locations of the target vehicle, so as to most effectively capture the dynamic characteristics of the vehicle and the working status of the shock absorber.

[0021] Preferably, different types of sensors are then arranged at these key points to collect a variety of data related to the working state of the vehicle shock absorber, comprehensively reflecting the dynamic performance of the vehicle under different working conditions, including angle sensors, height sensors, pressure sensors and acceleration sensors. These sensors constitute a vehicle working perception module, which collects real-time operating data of the vehicle under different working conditions, including angle information, suspension amplitude, shock absorber internal pressure and vehicle acceleration, etc. Specifically, the angle information is obtained through the angle sensor, reflecting the suspension and shock absorber states of the vehicle at different steering angles. For example, when the vehicle makes a sharp turn, the suspension system will be subjected to greater lateral force, and the shock absorber will be The working state of the vehicle may change; the suspension amplitude is obtained through the height sensor and acceleration sensor, which describes the vertical movement amplitude of the vehicle suspension system and reflects the vibration of the vehicle under different road conditions. The quality of the shock absorber directly affects the vibration amplitude of the suspension; the vehicle acceleration is measured by the acceleration sensor, which reflects the acceleration changes of the vehicle under different working conditions, especially the vertical acceleration changes of the vehicle on bumpy roads. The fluctuation amplitude and frequency of the acceleration can reveal the damping performance of the shock absorber; the internal pressure of the shock absorber (especially the hydraulic shock absorber) is detected by the pressure sensor, which is used to judge the condition of the hydraulic oil inside the shock absorber, such as whether there is abnormal pressure or hydraulic oil leakage.

[0022] Preferably, through the sensor groups arranged at key points, the vehicle working perception module realizes real-time monitoring of the dynamic state of the vehicle. The collected data can not only reflect the working condition of the shock absorber, but also can be used for the performance evaluation of the overall suspension system. For example, when the vehicle encounters uneven road surface or turns during driving, the data combination of the angle sensor, acceleration sensor and height sensor can reveal whether the shock absorber can effectively absorb vibration and keep the vehicle body stable. When abnormally large acceleration or large fluctuations in height sensor data are detected, further analysis is performed to determine whether these abnormal changes are caused by shock absorber failure, which helps to accurately judge the state of the shock absorber and prevent possible failures.

[0023] Step S200, monitoring and acquiring the vehicle operation multi-sensor data stream through the vehicle operation perception module, performing parallel preprocessing and correlation feature extraction on the vehicle operation multi-sensor data stream, and obtaining a set of shock absorber operation characteristic parameters.

[0024] Preferably, through multiple sensors of the vehicle working perception module, various types of data of vehicle working are obtained in real time, from different key points of the vehicle, reflecting dynamic information of different positions and types of the vehicle during driving, including angle information, height data, pressure data and acceleration data, reflecting the working conditions of the suspension system and shock absorber of the vehicle during driving in real time, and forming a multi-sensor data stream of vehicle working; then the multi-sensor data stream of vehicle working is pre-processed in parallel, that is, the raw data collected by multiple sensors are processed simultaneously, including standardization, denoising and synchronization of these data, denoising, the data collected by the sensor may contain noise, which comes from road interference, sensor error, etc. Irrelevant or inaccurate data are removed by filtering and other methods; data synchronization, the data collection frequency and timing of different sensors may be different, and the data of multiple sensors must be time-aligned so that they can synchronously reflect the working status of the vehicle, for example, there may be a time difference between the angle data and the acceleration data; data cleaning, remove outliers and invalid data, and ensure the accuracy of subsequent feature extraction, for example, if a sensor fails within a certain period of time, mark these data and exclude abnormal data points.

[0025] Preferably, key features related to the working state of the shock absorber are extracted from the multi-sensor data stream after parallel preprocessing. Specifically, the correlation feature extraction will extract data that can accurately reflect the health status of the shock absorber based on the relationship between different sensors, including data fusion, which captures changes in the overall state of the vehicle by combining data from different sensors. For example, the steering angle of the steering angle sensor combined with the lateral acceleration of the acceleration sensor can extract the damping performance of the shock absorber when turning; time series analysis, using the time series data of each sensor to extract key dynamic features, such as the vehicle's vibration frequency, amplitude changes and the time point of suspension compression recovery, revealing the dynamic response capability of the shock absorber; feature association, the data between different sensors can reveal the working state of the shock absorber through correlation analysis. For example, the correlation between the compression degree of the suspension height sensor and the vibration frequency of the acceleration sensor reflects the vibration absorption performance of the shock absorber. If the acceleration suddenly increases and the compression state of the height sensor is abnormal, it may indicate that the shock absorber is not working properly; finally, a set of shock absorber working characteristic parameters is obtained, which represents the health status and working efficiency of the shock absorber, which may include damping characteristic parameters extracted according to the vehicle vibration amplitude, frequency and suspension compression rebound, suspension balance extracted through height sensor data, and by analyzing the dynamic response speed of the shock absorber under different conditions, the rate of change of vehicle vibration is extracted according to the acceleration sensor data, and the vibration of the vehicle under different road conditions is evaluated.

[0026] like Figure 2 As shown, step S200 further includes step S210, respectively performing noise characteristic analysis on the vehicle working multi-sensor data stream to obtain vehicle multi-sensor data noise characteristic information; step S220, designing a multi-noise filter according to the vehicle multi-sensor data noise characteristic information, and using the multi-noise filter to perform mapping preprocessing on the vehicle working multi-sensor data stream to obtain a vehicle working standard multi-sensor data stream; step S230, performing shock absorber associated feature analysis on the vehicle working standard multi-sensor data stream to obtain a shock absorber associated feature set; step S240, calculating and extracting the vehicle working standard multi-sensor data stream according to the shock absorber associated feature set to obtain the shock absorber working characteristic parameter set.

[0027] Preferably, the multi-sensor data streams of vehicle operation are often affected by noise, which may come from the inaccuracy of the sensor itself, environmental interference (such as wind noise, road vibration, etc.), or other external factors. The noise characteristic analysis aims to analyze and identify the type and characteristics of noise in each sensor data, including the frequency, intensity and pattern of the noise. For example, the acceleration sensor may be affected by vibration noise, the height sensor may be affected by the height change noise caused by ground inequality factors, and the angle sensor may detect a small steering noise when the vehicle is stationary; according to the noise characteristics of different sensor data streams, multiple noise filters suitable for different noise types are designed, and each filter processes a specific type of noise to ensure that the noise has the least impact on the sensor data, such as a low-pass filter is used to filter the high-frequency acceleration noise caused by vehicle vibration; Mapping preprocessing refers to using a designed multi-noise filter to process the original vehicle working multi-sensor data stream, filter out the noise data, and generate a vehicle working standard multi-sensor data stream, that is, mapping the original data to a standardized data space after noise reduction. This standard data stream can more accurately reflect the actual working status of the vehicle and eliminate the influence of noise.

[0028] Preferably, by analyzing the standard multi-sensor data stream after noise processing, the associated features related to the working state of the shock absorber are extracted, for example, the relationship between the acceleration sensor and the height sensor is analyzed to determine the working performance of the suspension and the shock absorber when the vehicle is vibrating, the combination of the pressure sensor and the height sensor is used to determine the working condition of the hydraulic system inside the shock absorber, whether there is hydraulic oil leakage or pressure instability, and when the vehicle is turning, the data combination between the acceleration sensor and the angle sensor can reveal the responsiveness of the shock absorber when turning, and determine whether it can effectively maintain the stability of the vehicle, and obtain the shock absorber associated feature set, and then further calculate and extract the shock absorber working characteristic parameter set, which includes multiple key performance indicators and directly reflects the health status and working efficiency of the shock absorber, such as amplitude matching, vibration frequency, acceleration change rate, etc. The shock absorber working characteristic parameters directly reflect the working state and health status of the shock absorber, ensure the accuracy and real-time performance of shock absorber fault diagnosis, and help improve the safety of the vehicle.

[0029] Step S300, optimizing and acquiring a target data fusion algorithm, using the target data fusion algorithm to fuse the shock absorber operating characteristic parameter set according to time series information, and obtaining shock absorber operating state fusion characteristic parameters.

[0030] Preferably, through the optimization process, the best data fusion algorithm suitable for processing the shock absorber working characteristic parameters is found, and the overall characteristics that can reflect the working state of the shock absorber are generated by fusing multi-dimensional sensor data. The target fusion algorithm is used to perform fusion processing on the shock absorber working characteristic parameter set according to the time series information. Specifically, the characteristic parameters of multiple sensors are integrated in time series to generate an overall characteristic parameter that reflects the dynamic working state of the shock absorber. Through fusion processing, the data between different sensors can work together to form a more complete and comprehensive description of the shock absorber working state. For example, when the vehicle turns, the data of the angle sensor and the acceleration sensor are combined to evaluate the dynamic response of the shock absorber when turning; on bumpy roads, the data of the acceleration sensor and the suspension height sensor are combined to evaluate whether the shock absorber can effectively buffer the impact; finally, the shock absorber working state fusion characteristic parameters are obtained, which reflect the overall working performance of the vehicle shock absorber under different dynamic conditions and more accurately describe the health status and working performance of the shock absorber. The accuracy of data analysis is greatly improved, which helps to carry out real-time fault diagnosis and preventive maintenance.

[0031] Step S300 further includes step S310, performing fusion demand analysis on the shock absorber working characteristic parameter set to obtain data fusion demand characteristic information; step S320, constructing a data fusion algorithm list, performing characteristic matching based on the data fusion demand characteristic information and the data fusion algorithm list to obtain an available data fusion algorithm; step S330, performing fusion verification and evaluation on the shock absorber working characteristic parameter set through the available data fusion algorithm to obtain data fusion performance evaluation parameters; step S340, iteratively optimizing the available data fusion algorithm based on the data fusion performance evaluation parameters to obtain the target data fusion algorithm.

[0032] Preferably, the data characteristics in the shock absorber working characteristic parameter set are analyzed to determine the problems that need to be solved in the data fusion process, which may include the timing, multidimensionality, noise sensitivity, etc. of the data, and obtain the data fusion requirement characteristic information. For example, the shock absorber characteristic parameters may contain different types of data (such as acceleration, angle, pressure, etc.), wherein the data fusion requirement characteristic information is used to describe the relationship between different sensor data, the timing characteristics of the data, the amount of data and its requirements for noise and processing speed, and defines the key characteristics that need to be considered in the data fusion process, such as timing synchronization, multidimensional data processing and noise sensitivity; then a list of data fusion algorithms is constructed and characteristic matching is performed, that is, based on the data fusion requirement characteristic information, suitable algorithms are screened. Different algorithms are suitable for different data fusion requirements. For example, Kalman filtering is suitable for dynamic timing data, while machine learning algorithms are suitable for processing multidimensional complex data. Through characteristic matching, it is determined which algorithms can meet the current fusion requirements, thereby obtaining Available data fusion algorithms. The data fusion algorithm list refers to the collection of all optional data fusion algorithms, including a variety of fusion methods for processing sensor data. Use the available data fusion algorithms to perform actual fusion processing on the set of shock absorber working characteristic parameters, and evaluate the fusion effect to ensure that it can correctly process the data and output meaningful fusion results, and obtain data fusion performance evaluation parameters, which usually include the accuracy, stability, real-time and robustness of the fused data. Through these evaluation parameters, the performance of each available algorithm can be quantified. Finally, by evaluating the performance of the available algorithms, select the algorithm with the best initial performance, and optimize it on this basis, and perform multiple iterations. Each time, by adjusting the parameters, weights or structure of the algorithm, the data fusion effect is continuously improved. Through multiple rounds of iterative optimization, the fusion performance of the algorithm is continuously evaluated, and its performance is gradually improved. Finally, the target data fusion algorithm is obtained, which can efficiently process and fuse the working characteristic data of the shock absorber under various working conditions.

[0033] Step S340 further includes step S341, constructing an algorithm optimization strategy space, performing optimization direction analysis on the algorithm optimization strategy space based on the data fusion performance evaluation parameters, and determining the algorithm optimization direction; step S342, performing local iterative optimization search in the algorithm optimization strategy space according to the algorithm optimization direction, and obtaining algorithm optimization strategy parameters; step S343, optimizing the configuration and updating the available data fusion algorithm based on the algorithm optimization strategy parameters, and obtaining the target data fusion algorithm.

[0034] Preferably, an algorithm optimization strategy space is constructed by optimizing and adjusting the data fusion algorithm in a variety of ways, which is a collection of all possible algorithm optimization strategies. The algorithm optimization strategy space contains multiple possible optimization directions and parameter combinations for improving the performance of the data fusion algorithm, such as weight adjustment strategy, model structure adjustment strategy, filter parameter adjustment strategy and data cleaning strategy. Then, the algorithm optimization strategy space is optimized according to the data fusion performance evaluation parameters to find the key parameters that affect the algorithm performance and to identify the priority directions for improvement in subsequent optimizations. That is, the shortcomings of the current algorithm are analyzed and the directions that need to be optimized are determined. For example, if the accuracy is insufficient, the optimization direction may involve improving the weight adjustment of sensor data. If the real-time performance is insufficient, the computational efficiency of the algorithm needs to be optimized and redundant calculations need to be reduced. If the stability is poor, it may need to be improved. Improve the noise processing capability, or increase the algorithm's ability to handle data mutations; then, according to the optimization direction, perform local iterative optimization, that is, make small adjustments to a specific parameter, and then evaluate the performance of the adjusted algorithm. After each iteration, judge the effect of the adjustment through the data fusion performance evaluation parameter, and decide whether to continue optimizing in the current direction, or try to adjust other parameters, and obtain the algorithm optimization strategy parameters, which refers to the best parameter combination found in the local iterative optimization process, which is used to improve the performance of the data fusion algorithm; finally, apply the best algorithm optimization strategy parameters obtained by iterative optimization to the available data fusion algorithm to update and improve the configuration update of the algorithm, and finally obtain the target data fusion algorithm with the best fusion performance, which can efficiently process multi-source sensor data under different working conditions and accurately evaluate the working status of the shock absorber.

[0035] Step S400, analyzing the influencing factors of the driving scene information of the target vehicle, determining the suspension balance influencing factor, introducing the suspension balance influencing factor to calculate the matching degree of the shock absorber working state fusion characteristic parameters, and obtaining the shock absorber dynamic matching degree.

[0036] Preferably, the driving scenario information refers to the environmental conditions and dynamic working conditions of the vehicle during actual driving. Different driving scenarios will affect the working state of the suspension and shock absorber, including road conditions (such as road surface flatness, slope, obstacles, unevenness), vehicle speed, turning and braking, and vehicle load (such as the number of passengers and cargo weight). By analyzing the dynamic factors in these driving scenarios, it is determined which conditions have the greatest impact on the balance of the suspension system. For example, when the vehicle is turning at high speed, the suspension system may be subjected to greater lateral force, and under full load, the working pressure of the shock absorber may be greater. The degree of influence of these influencing factors on the shock absorber and suspension balance is evaluated, and the suspension balance influencing factor is determined, that is, the key variable extracted from the driving scenario information that has a direct impact on the suspension balance; then the suspension balance influencing factor is fused with the shock absorber working state characteristic parameter A comparison is made and the degree of match between the two is calculated. Specifically, by analyzing the correlation between the suspension balance influencing factors (such as the vehicle's vertical acceleration and steering angle) and the shock absorber characteristic parameters (such as damping response time and suspension height change), it is evaluated whether the shock absorber's response ability is consistent with the current driving scenario. If some shock absorber parameters (such as suspension compression amplitude) exceed a certain critical value, and this value does not match the suspension balance influencing factors (such as the degree of road bumps), it may mean that the shock absorber's performance is poor. According to the suspension balance influencing factors, the calculation method of the shock absorber's working state characteristics is adjusted in real time to ensure that the shock absorber's performance meets expectations in different scenarios. The degree of match can reflect whether the shock absorber can effectively play a role under the current working conditions. For example, when a vehicle is driving on a bumpy road, the shock absorber should be able to absorb vibrations in time to keep the vehicle stable. If the damping effect of the shock absorber does not meet expectations, the matching degree will be low, indicating possible performance degradation or failure. The dynamic matching degree of the shock absorber is obtained through matching calculation, which indicates the degree of consistency between the actual performance of the shock absorber in a specific driving scenario and the expected working state, and reflects the working efficiency of the shock absorber in real time. A high matching degree means that the shock absorber can adapt well to the current driving scenario, the suspension balance is good, and the vehicle remains stable; a low matching degree means that the shock absorber fails to play an ideal role in the current driving scenario, and there may be problems or adjustments are needed. For example, the shock absorber response time is too long or the damping performance is reduced, resulting in greater vibration and lack of stability during driving.

[0037] Step S400 further includes step S410, extracting signal influence parameters of the driving scene information of the target vehicle to obtain vehicle signal influence characteristic parameters, wherein the vehicle signal influence characteristic parameters include vehicle driving environment conditions, vehicle driving status and vehicle structural characteristics; step S420, performing signal delay analysis on both sides of the suspension based on the vehicle signal influence characteristic parameters to obtain a suspension sensor signal delay factor; step S430, performing signal compensation analysis on both sides of the suspension for the suspension sensor signal delay factor to determine the suspension balance influence factor.

[0038] Preferably, key signal characteristic parameters that affect the operation of the vehicle and the suspension system are extracted from the driving scene information of the target vehicle, including the vehicle driving environment conditions, the vehicle driving state and the vehicle structural characteristics. Specifically, the vehicle driving environment conditions include road conditions (flat or bumpy), climate conditions (temperature, humidity), wind speed, etc., all of which affect the vehicle's suspension system and sensor operation. For example, bumpy roads may cause sensor signals to be unstable, or extreme temperatures may affect the sensor's response speed; the vehicle driving state includes the vehicle's speed, acceleration, steering angle, braking state, etc., which have a direct impact on the load and signal transmission of the suspension system; the vehicle structural characteristics refer to the physical structure of the vehicle itself, such as body weight, wheelbase, suspension type, etc. Different types of vehicle structures will have different effects on the working mode of the suspension system, thereby affecting the response of the sensor signal. By extracting these signal influencing parameters, the factors that have a direct impact on the performance of the suspension system and signal transmission during vehicle driving can be identified.

[0039] Preferably, multiple sensors are usually installed on both sides of the suspension system to monitor the dynamic performance of the vehicle during driving, such as suspension compression, extension, load and wheel position. Since the sensors are located at different positions of the suspension system, there may be certain differences in the response time of the signals on both sides of the suspension. This difference may be caused by factors such as signal transmission distance, sensor type, road conditions, and vehicle structure. Signal delay analysis refers to identifying and quantifying the response time difference of sensors on both sides of the suspension to the same event at the same time. For example, when a vehicle passes through a bumpy road, the sensors on the left and right sides of the suspension may detect different vibration waveforms and timings, causing signal delays. The suspension sensor signal delay factor is obtained by analyzing the response time difference of the sensors on both sides of the suspension system, which is used to describe the degree of delay of the signals on both sides.

[0040] Preferably, the signal delay on both sides of the suspension is compensated to ensure the balance of the vehicle suspension system. Specifically, by adjusting or correcting the delay of the sensor data on both sides of the suspension, it is ensured that the suspension system can correctly balance the load and dynamic changes of the vehicle during driving. It may include timing compensation. According to the delay factor, the signal timing on both sides of the suspension system is adjusted so that the sensor data on both sides can be processed synchronously at the same time point. For example, if the right suspension signal is slightly delayed than the left side, the right signal can be processed in advance to achieve synchronization on both sides; amplitude compensation. In some cases, signal delay may also cause differences in the amplitude detected by the sensor. By adjusting the signal output of the sensor, it is ensured that the load perception and feedback on both sides of the suspension are consistent; by adjusting the signals on both sides of the suspension, the signal delay problem caused by the driving environment, dynamic changes or vehicle structure is eliminated to ensure that the suspension system can maintain balance under various conditions, and finally determine the suspension balance influencing factor to describe the state of suspension system balance. The suspension balance influencing factor may include dynamic road conditions, vehicle load conditions, and signal synchronization on both sides of the suspension to determine whether the suspension system is in the best working state.

[0041] Step S400 further includes step S440, obtaining a shock absorber matching index set, wherein the shock absorber matching index set includes amplitude matching, frequency matching, and damping force matching; step S450, performing a suspension two-side distribution calculation on the shock absorber working state fusion characteristic parameters according to the shock absorber matching index set to obtain an amplitude time series matching set, a frequency time series matching set, and a damping force time series matching set; step S460, constructing a shock absorber performance characteristic matrix according to the amplitude time series matching set, the frequency time series matching set, and the damping force time series matching set: ; Where R is the shock absorber performance characteristic matrix, T is the number of timing matching degrees on both sides of the suspension, is the amplitude matching degree at the Tth time sequence, is the frequency matching degree at the Tth time sequence, is the damping force matching degree at the Tth time sequence; step S470, performing dynamic matching degree calculation based on the shock absorber performance characteristic matrix to obtain the dynamic matching degree of the shock absorber.

[0042] Preferably, the shock absorber matching index set includes amplitude matching, frequency matching and damping force matching, which are used to evaluate the working state of the shock absorber, wherein the amplitude matching measures whether the vibration amplitude of the shock absorber under different conditions meets the expectations; the frequency matching is used to evaluate whether the response frequency of the shock absorber matches the dynamic frequency of the vehicle during operation; the damping force matching is used to analyze whether the damping characteristics of the shock absorber meet the vibration reduction effect required by the suspension system; the shock absorber matching index set is used to analyze the fusion characteristic parameters of the shock absorber working state, and calculate the matching degree at each time series, that is, to quantify the similarity of the amplitude, frequency and damping force of the left and right sides of the front and rear suspension. The greater the matching degree, the stronger the vibration reduction balance on both sides of the suspension, and the better the working performance of the shock absorber, and the matching degree is calculated according to the time series. The information is arranged in sequence, and the calculation results will form three time series matching degree sets, including amplitude time series matching degree set, frequency time series matching degree set and damping force time series matching degree set, which respectively represent the amplitude matching, frequency matching and damping force matching of the shock absorber at each time point; then the multiple time series matching degree sets are used to construct a shock absorber performance characteristic matrix, and then based on the shock absorber performance characteristic matrix, the matrix is ​​analyzed using a statistical or algorithmic model to calculate the overall dynamic matching degree of the shock absorber, such as weighted calculation of the matching degree of amplitude, frequency and damping force, and the weight can be determined according to the importance of each indicator, which is an indicator for evaluating the performance consistency and effectiveness of the shock absorber under different working conditions and time periods, and usually reflects the responsiveness, adaptability and overall performance of the shock absorber.

[0043] Step S470 further includes step S471, performing maximization forward processing and standardization processing on the shock absorber performance characteristic matrix to obtain a normalized shock absorber performance characteristic matrix; step S472, constructing a matching evaluation function, and performing matching scores on each time series object in the normalized shock absorber performance characteristic matrix based on the matching evaluation function to obtain T time series object matching scores; step S473, performing standard deviation and mean calculation on the matching scores of the T time series objects to obtain the matching score standard deviation and the matching score average; step S474, performing variation calculation based on the matching score standard deviation and the matching score average to obtain the dynamic matching of the shock absorber.

[0044] Step S472 further includes that the matching evaluation function is specifically: ; ; ; in, is the matching score for the i-th time series, , and are the weights of amplitude matching, frequency matching, and damping force matching, which can be determined according to the degree of influence on the shock absorber performance. , and are the maximum values ​​of the elements in each matrix column, , and are the minimum values ​​of the elements of each matrix column, , and is the amplitude matching, frequency matching, and damping force matching at the i-th time sequence.

[0045] Preferably, the shock absorber performance characteristic matrix is ​​subjected to maximization forward processing, usually by converting or adjusting the numerical values ​​so that all characteristics are directed toward a positive effect (such as higher amplitude, frequency or damping force indicates better performance), and each indicator is normalized so that its value is in the same range (usually 0 to 1) to eliminate the dimensional influence between different indicators, improve the comparability of each indicator in the performance characteristic matrix, and obtain a normalized shock absorber performance characteristic matrix; a matching evaluation function is constructed to quantify the matching degree of each time series object (measurement values ​​at different time points) in the normalized shock absorber performance characteristic matrix, and then a matching degree score is performed on each time series object in the normalized shock absorber performance characteristic matrix based on the matching evaluation function. For each time series object, a score is calculated using the matching evaluation function, that is, T time series object matching scores are obtained, which reflect the shock absorber performance at each time point. The standard deviation and mean of the matching score are calculated. The standard deviation of the matching score indicates the fluctuation degree of the matching score of the shock absorber at different time points. If the standard deviation is small, it means that the performance of the shock absorber is relatively stable under each time series. The average matching score is the average matching score of all time series objects, which indicates the overall performance of the shock absorber. Finally, the degree of variation is calculated based on the standard deviation of the matching score and the average matching score, that is, the ratio of the standard deviation of the matching score to the average matching score is calculated to compare the degree of discreteness between data of different units or dimensions and obtain the dynamic matching degree of the shock absorber. The greater the degree of variation, the greater the relative change degree of the data, indicating that the performance of the shock absorber fluctuates greatly in some cases and the dynamic matching degree is low. If the degree of variation is low, it means that the performance consistency of the shock absorber under different conditions is good and the dynamic matching degree is high.

[0046] Step S500: performing fault judgment analysis on the dynamic matching degree of the shock absorber based on a preset matching degree change threshold, determining shock absorber fault diagnosis information, and performing fault feedback optimization on the target vehicle based on the shock absorber fault diagnosis information.

[0047] Preferably, by comparing the calculated dynamic matching degree with a preset matching degree change threshold, it is analyzed whether a fault occurs, wherein the preset matching degree change threshold is a preset standard value for judging whether the shock absorber is in a normal working state, which can be set according to the shock absorber specifications and models and experience. If the change in dynamic matching degree exceeds the threshold, it can be judged that the shock absorber may have a fault, such as insufficient damping, excessive vibration and other problems. After the fault judgment analysis, the shock absorber fault diagnosis information is determined, including recording the fault type, such as performance degradation of the shock absorber, instability of the damping force or abnormal fluctuations in amplitude or frequency. Finally, according to the diagnosed fault information, the suspension system or shock absorber of the target vehicle is adjusted or optimized as necessary, for example, fault repair, replacement of the shock absorber or adjustment of the vehicle's suspension settings, to ensure that the vehicle restores the best dynamic performance during driving, improve driving safety, and avoid greater problems caused by shock absorber failure.

[0048] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A multi-parameter fusion analysis method for automobile shock absorber fault diagnosis, characterized in that: The method comprises: Performing key point analysis on the structural characteristic information of the target vehicle to obtain a vehicle key point set, deploying a sensor group at the vehicle key point set to obtain a vehicle work perception module, wherein the vehicle work perception module includes a rotation angle sensor, a height sensor, a pressure sensor, and an acceleration sensor; Acquiring a vehicle operation multi-sensor data stream through the vehicle operation perception module, performing parallel preprocessing and correlation feature extraction on the vehicle operation multi-sensor data stream, and obtaining a shock absorber operation characteristic parameter set; Optimizing and acquiring a target data fusion algorithm, using the target data fusion algorithm to fuse the shock absorber working characteristic parameter set according to time series information to obtain a shock absorber working state fusion characteristic parameter; Analyzing the influencing factors of the driving scene information of the target vehicle, determining the suspension balance influencing factor, introducing the suspension balance influencing factor to calculate the matching degree of the shock absorber working state fusion characteristic parameters, and obtaining the shock absorber dynamic matching degree; The dynamic matching degree of the shock absorber is subjected to fault judgment analysis based on a preset matching degree change threshold, shock absorber fault diagnosis information is determined, and fault feedback optimization is performed on the target vehicle based on the shock absorber fault diagnosis information.

2. The multi-parameter fusion analysis method for automobile shock absorber fault diagnosis according to claim 1, characterized in that: The step of obtaining a set of operating characteristic parameters of the shock absorber comprises: Performing noise characteristic analysis on the vehicle working multi-sensor data streams respectively to obtain vehicle multi-sensor data noise characteristic information; Designing a multi-noise filter according to the noise characteristic information of the vehicle multi-sensor data, and using the multi-noise filter to perform mapping preprocessing on the vehicle working multi-sensor data stream to obtain a vehicle working standard multi-sensor data stream; Performing shock absorber associated feature analysis on the vehicle working standard multi-sensor data stream to obtain a shock absorber associated feature set; The vehicle working standard multi-sensor data stream is calculated and extracted according to the shock absorber associated feature set to obtain the shock absorber working characteristic parameter set.

3. The multi-parameter fusion analysis method for automobile shock absorber fault diagnosis according to claim 1, characterized in that: The optimization and acquisition target data fusion algorithm includes: Performing a fusion demand analysis on the shock absorber working characteristic parameter set to obtain data fusion demand characteristic information; Constructing a data fusion algorithm list, performing feature matching on the data fusion requirement feature information and the data fusion algorithm list, and obtaining an available data fusion algorithm; Performing fusion verification and evaluation on the shock absorber working characteristic parameter set by using the available data fusion algorithm to obtain data fusion performance evaluation parameters; The available data fusion algorithms are iteratively optimized based on the data fusion performance evaluation parameters to obtain the target data fusion algorithm.

4. The multi-parameter fusion analysis method for automobile shock absorber fault diagnosis according to claim 3 is characterized in that: The step of obtaining the target data fusion algorithm comprises: Constructing an algorithm optimization strategy space, analyzing the optimization direction of the algorithm optimization strategy space based on the data fusion performance evaluation parameters, and determining the algorithm optimization direction; Perform local iterative optimization in the algorithm optimization strategy space according to the algorithm optimization direction to obtain algorithm optimization strategy parameters; The available data fusion algorithm is optimized and configured updated based on the algorithm optimization strategy parameters to obtain the target data fusion algorithm.

5. The multi-parameter fusion analysis method for automobile shock absorber fault diagnosis according to claim 1, characterized in that: The determining of the suspension balance influencing factor comprises: Extracting signal influence parameters from the driving scene information of the target vehicle to obtain vehicle signal influence characteristic parameters, wherein the vehicle signal influence characteristic parameters include vehicle driving environment conditions, vehicle driving state and vehicle structural characteristics; Based on the vehicle signal impact characteristic parameters, signal delay analysis is performed on both sides of the suspension to obtain a suspension sensor signal delay factor; The suspension sensor signal delay factor is subjected to a signal compensation analysis on both sides of the suspension to determine the suspension balance influencing factor.

6. The multi-parameter fusion analysis method for automobile shock absorber fault diagnosis according to claim 1, characterized in that: The obtaining of the dynamic matching degree of the shock absorber comprises: Acquire a shock absorber matching index set, wherein the shock absorber matching index set includes amplitude matching, frequency matching, and damping force matching; According to the shock absorber matching index set, the shock absorber working state fusion characteristic parameters are calculated for distribution on both sides of the suspension to obtain an amplitude time series matching set, a frequency time series matching set, and a damping force time series matching set; According to the amplitude time series matching degree set, the frequency time series matching degree set and the damping force time series matching degree set, a shock absorber performance characteristic matrix is ​​constructed: ; Among them, R is the shock absorber performance characteristic matrix, T is the number of timing matching degrees on both sides of the suspension, is the amplitude matching degree at the Tth time sequence, is the frequency matching degree at the Tth time sequence, is the damping force matching degree at the Tth time sequence; A dynamic matching degree calculation is performed based on the shock absorber performance characteristic matrix to obtain the shock absorber dynamic matching degree.

7. The multi-parameter fusion analysis method for automobile shock absorber fault diagnosis according to claim 6, characterized in that: The obtaining of the dynamic matching degree of the shock absorber comprises: Performing maximization forward processing and standardization processing on the shock absorber performance characteristic matrix to obtain a normalized shock absorber performance characteristic matrix; Constructing a matching evaluation function, and performing matching scores on each time series object in the normalized shock absorber performance characteristic matrix based on the matching evaluation function to obtain T time series object matching scores; Calculating the standard deviation and average of the matching scores of the T time series objects to obtain the matching score standard deviation and the matching score average; The degree of variation is calculated based on the matching score standard deviation and the matching score average to obtain the dynamic matching degree of the shock absorber.

8. The multi-parameter fusion analysis method for automobile shock absorber fault diagnosis according to claim 7, characterized in that: The matching evaluation function is specifically: ； ； ； in, is the matching score for the i-th time series, , and are the weights of amplitude matching, frequency matching, and damping force matching, which can be determined according to the degree of influence on the shock absorber performance. , and are the maximum values ​​of the elements in each matrix column, , and are the minimum values ​​of the elements of each matrix column, , and is the amplitude matching, frequency matching, and damping force matching at the i-th time sequence.

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