Prediction System for Performance Degradation of Automotive Shock Absorbers

Abstract

The present invention discloses a system for predicting automobile shock absorber performance degradation, relating to the field of shock absorption technology. The system comprises: obtaining historical monitoring records of a preset shock absorber; matching records at a first historical node from multiple sets of records; analyzing the first historical hardness at the node according to a predetermined hardness and softness strategy; further analyzing the correspondence between the first historical node and the hardness and softness to obtain the real-time hardness and softness; and finally, analyzing the deviation between the real-time hardness and the hardness and hardness benchmark to obtain a real-time performance degradation index. This system solves the technical problem of the prior art lacking a means to accurately predict shock absorber performance degradation. By using hardness and softness as key factors to characterize the state of the shock absorber, the system achieves accurate real-time prediction of automobile shock absorber performance degradation, thereby improving the safety of automobile driving.

Description

Technical Field

[0001] The present application relates to the field of shock absorption technology, and in particular to a prediction system for automobile shock absorber performance attenuation. Background Art

[0002] During vehicle operation, the degradation of shock absorber performance poses significant safety and comfort challenges, creating an increasingly pressing conflict between the need to accurately assess shock absorber condition and efficiently allocate maintenance resources. Shock absorber hardness, as a key indicator, is crucial for accurately reflecting the performance of the shock absorber. Traditional shock absorber performance testing and evaluation methods are often fragmented and localized, relying solely on manual experience and periodic testing equipment. This approach fails to adequately monitor long-term changes in shock absorber performance and lacks in-depth analysis and utilization of historical monitoring data. This critical metric, hardness, is particularly underappreciated, making it difficult to comprehensively and accurately assess shock absorber degradation. Inefficient resource allocation results in some shock absorbers in urgent need of maintenance not receiving timely attention, while others in better condition may receive excessive attention. Consequently, shock absorber maintenance plans are often simplistic and rigid, failing to effectively address the ever-changing real-world driving conditions.

[0003] At present, relevant technologies lack the technical means to accurately predict the attenuation of shock absorber performance. Summary of the Invention

[0004] The present application provides a prediction system for automobile shock absorber performance degradation by obtaining historical monitoring records of preset shock absorbers, matching records under the first historical node from multiple sets of records, and obtaining the first historical hardness under the node according to a predetermined hardness and softness strategy analysis. The correspondence between the first historical node and the hardness and softness is then analyzed to obtain the real-time hardness and softness. Finally, the real-time performance degradation index is obtained by analyzing the deviation between the real-time hardness and the hardness and the hardness benchmark. This achieves the technical effect of accurately predicting the performance degradation of automobile shock absorbers in real time by using hardness and softness as the key factors to characterize the state of the shock absorber, thereby improving the safety of automobile driving.

[0005] This application provides a prediction system for automobile shock absorber performance attenuation, including:

[0006] A historical shock absorber monitoring record acquisition module, the historical shock absorber monitoring record acquisition module is used to acquire historical shock absorber monitoring records, and the historical shock absorber monitoring records include multiple groups of monitoring records of preset shock absorbers under multiple historical nodes; a first monitoring record matching module, the first monitoring record matching module is used to match the first monitoring record under the first historical node in the multiple groups of monitoring records, and the first historical node refers to any one of the multiple historical nodes; a first historical hardness and softness acquisition module, the first historical hardness and softness acquisition module is used to analyze the first monitoring record according to a predetermined hardness and softness strategy to obtain the first historical hardness and softness under the first historical node; a first correspondence analysis module, the first correspondence analysis module is used to analyze the first correspondence between the first historical node and the first historical hardness and hardness to obtain the real-time hardness and softness of the preset shock absorber; a real-time performance attenuation index acquisition module, the real-time performance attenuation index acquisition module is used to analyze the real-time hardness and softness deviation between the real-time hardness and the hardness benchmark to obtain a real-time performance attenuation index.

[0007] The automobile shock absorber performance attenuation prediction system proposed in this application first obtains the historical monitoring records of the preset shock absorber, matches the records under the first historical node from multiple groups of records, obtains the first historical hardness under the node according to the predetermined hardness and softness strategy analysis, and then analyzes the correspondence between the first historical node and the hardness to obtain the real-time hardness and softness. Finally, the real-time performance attenuation index is obtained by analyzing the deviation between the real-time hardness and the hardness benchmark. The technical effect of accurately predicting the performance attenuation of the automobile shock absorber and improving the safety of automobile driving is achieved by using hardness and softness as the key factors to characterize the state of the shock absorber. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0009] Figure 1 A schematic diagram of the structure of a system for predicting automobile shock absorber performance degradation provided in an embodiment of the present application;

[0010] Figure 2 This is a schematic structural diagram of the first historical hardness and softness acquisition module of the automobile shock absorber performance attenuation prediction system provided in an embodiment of the present application.

[0011] Explanation of the accompanying symbols: historical shock absorber monitoring record acquisition module 10, first monitoring record matching module 20, first historical softness and hardness acquisition module 30, first correspondence analysis module 40, real-time performance attenuation index acquisition module 50. DETAILED DESCRIPTION

[0012] 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.

[0013] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0014] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be 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 used to distinguish similar objects and do not represent a specific ordering of the 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 that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0015] The present application embodiment provides a prediction system for automobile shock absorber performance attenuation, such as Figure 1 As shown, the system includes:

[0016] The module 10 for acquiring historical shock absorber monitoring records is used to acquire historical shock absorber monitoring records, which include multiple sets of monitoring records of preset shock absorbers at multiple historical nodes. Specifically, the module 10 for acquiring historical shock absorber monitoring records plays a key basic role in the automobile shock absorber performance attenuation prediction system. Multiple sets of monitoring records of preset shock absorbers at multiple historical nodes are collected. The data sources include data collected in real time by various sensors on the automobile under different driving conditions and data recorded during regular maintenance, repairs or professional inspections of the automobile. The record content covers various information such as the physical parameters of the shock absorber itself, vehicle driving road condition information, vehicle load conditions and driving speed. Historical records provide a rich data foundation for subsequent modules of the system, reflecting the changing trend of shock absorber performance over time. They are also of great significance to automobile maintenance and management decisions. They can assist in determining whether the shock absorber needs to be maintained or replaced in advance to ensure the safety and comfort of automobile driving.

[0017] The first monitoring record matching module 20 is used to match the first monitoring record under the first historical node in the multiple groups of monitoring records, and the first historical node refers to any one of the multiple historical nodes. Specifically, the first monitoring record matching module 20 plays a key screening role in the automobile shock absorber performance attenuation prediction system. From the multiple groups of monitoring records collected by the historical shock absorber monitoring record acquisition module, the first monitoring record under any one of the multiple historical nodes is matched. The first historical node can be selected according to specific needs or randomly, and after determination, the module searches and matches among numerous records based on information such as timestamps. The first monitoring record that is successfully matched contains various status information of the shock absorber at a specific moment and information related to vehicle driving, providing targeted data support for subsequent analysis, and ensuring the accuracy and reliability of performance attenuation prediction.

[0018] The first historical hardness and softness acquisition module 30 is used to analyze the first monitoring record according to a predetermined hardness and softness strategy to obtain the first historical hardness and softness at the first historical node. Specifically, the first historical hardness and softness acquisition module 30 analyzes the first monitoring record according to the predetermined hardness and softness strategy. The predetermined hardness and softness strategy is based on a large amount of experimental data, theoretical models and practical experience, and comprehensively considers various factors that affect the hardness and softness of the shock absorber. The first historical hardness and softness acquisition module 30 extracts specific parameters from the first monitoring record, such as the shock absorber pressure change range, displacement, vehicle speed and road smoothness, etc., and performs comprehensive processing through a specific calculation formula or algorithm to determine the hardness and softness state of the shock absorber at the first historical node. The hardness and softness value is used as a quantitative representation to reflect key performance indicators such as the elasticity and damping characteristics of the shock absorber. It is an important basic data for subsequent analysis and prediction, which can help understand the state of the shock absorber at a specific historical node, analyze the performance trend over time, and provide key clues for predicting performance degradation.

[0019] In one possible implementation, Figure 2 As shown, the first historical hardness and softness acquisition module 30 includes a predetermined characteristic indicator unit, which is used to store predetermined characteristic indicators in the predetermined hardness and softness strategy. Specifically, the predetermined characteristic indicators stored in the predetermined hardness and softness strategy serve as the foundation for the entire analysis process. Determined through extensive research and practice, these predetermined characteristic indicators reflect key factors of shock absorber hardness and softness, such as the range of variation of specific physical parameters of the shock absorber and specific indicators related to vehicle driving conditions. These predetermined characteristic indicators provide a clear direction and basis for subsequent analysis.

[0020] The first characteristic parameter set acquisition unit is used to traverse the predetermined characteristic indicators in the first monitoring record to obtain the first characteristic parameter set. Specifically, traversing the predetermined characteristic indicators in the first monitoring record reflects the screening and extraction of each data in the first monitoring record one by one. In this way, a data set corresponding to the predetermined characteristic indicators can be found, that is, the first characteristic parameter set. For example, if the predetermined characteristic indicators include specific pressure change ranges and displacement requirements, the pressure and displacement data that meet these requirements will be searched in the first monitoring record and combined into the first characteristic parameter set, providing a specific data basis for subsequent analysis.

[0021] The first feedback prediction value acquisition unit is configured to use the first characteristic parameter set as input information for a soft and hard feedback prediction model to obtain a first feedback prediction value. Specifically, the first characteristic parameter set is used as input information for the soft and hard feedback prediction model. The model analyzes and predicts based on the input characteristic parameter set, and calculates a first feedback prediction value. The feedback prediction value is a preliminary prediction result of the soft and hardness of the shock absorber, taking into account various factors in the first characteristic parameter set. For example, if the first characteristic parameter set includes a large pressure change and a specific displacement, the model will predict a corresponding soft and hard feedback value based on the parameters.

[0022] A feedback adjustment unit is configured to perform feedback adjustment on the hardness / softness benchmark of the preset shock absorber based on the first feedback prediction value to obtain the first historical hardness / softness. Specifically, feedback adjustment is performed on the hardness / softness benchmark of the preset shock absorber based on the first feedback prediction value. The hardness / softness benchmark is a reference value. By comparing and adjusting the first feedback prediction value with the hardness / softness benchmark, a more accurate first historical hardness / softness can be obtained. The initial reference value is corrected based on actual conditions so that the resulting first historical hardness / softness is more consistent with actual conditions. For example, if the first feedback prediction value indicates that the shock absorber is harder than the hardness / softness benchmark under the current circumstances, the hardness / softness benchmark is adjusted accordingly to obtain a more accurate first historical hardness / softness.

[0023] In one possible implementation, the first characteristic parameter set acquisition unit further includes a predetermined characteristic index composition subunit, wherein the predetermined characteristic index composition subunit is configured to include a predetermined hardening characteristic index and a predetermined softening characteristic index. Specifically, clarifying that the predetermined characteristic index is composed of the predetermined hardening characteristic index and the predetermined softening characteristic index provides a specific classification direction for subsequent analysis, allowing the analysis of shock absorber hardness to consider factors that cause hardening and softening respectively.

[0024] The characteristic information acquisition subunit is used to sequentially traverse the predetermined hardening characteristic index and the predetermined softening characteristic index in the first monitoring record to obtain first hardening characteristic information and first softening characteristic information, respectively. Specifically, the predetermined hardening characteristic index is first considered, such as oil oxidation or contamination. In the first monitoring record, if it is found that the oil-related parameters in the shock absorber show that the oil may have been oxidized or contaminated over time, such as changes in the chemical properties of the oil, increases in impurity content, etc., it can be determined that there is a factor causing the shock absorber to harden, thereby obtaining the oil oxidation or contamination part in the first hardening characteristic information. For internal parts wear, in the first monitoring record, it is possible to check the wear degree parameters of the shock absorber's internal piston, sealing ring and other parts, such as part size changes, surface roughness, etc., to determine whether there is a situation where the shock absorber hardens due to internal parts wear, and then obtain the corresponding first hardening characteristic information. In terms of temperature changes, by monitoring the temperature of the shock absorber at different stages of use and the corresponding shock absorber performance changes. Parameters. If it is found that the temperature rises and the oil viscosity increases after long-term use, determine this factor that causes the shock absorber to harden and include it in the first hardening characteristic information. In the case of changes in internal gas pressure, check the internal gas pressure parameters of the inflatable shock absorber in the first monitoring record. If the pressure is too high and the damping increases, it indicates the existence of this hardening feature and add it to the first hardening characteristic information. In the case of improper adjustment, analyze the adjustment setting parameters of the adjustable shock absorber in the first monitoring record. If improper setting causes the shock absorber to harden, include it as part of the first hardening characteristic information. In the case of manufacturing defects, by checking the relevant information in the first monitoring record such as the shock absorber's production parameters and quality inspection records, determine whether there is a manufacturing defect that causes the shock absorber to harden. If so, include it in the first hardening characteristic information, and then consider the predetermined softening characteristic indicators, such as oil leakage.In the first monitoring record, if it is found that the flow rate, pressure and other parameters of the oil inside the shock absorber show that there is oil leakage, that is, the damping capacity is reduced, the factor that causes the shock absorber to soften is determined, and the oil leakage part in the first softening characteristic information is obtained. When internal parts are damaged, by checking the working status parameters of internal parts such as pistons and sealing rings in the first monitoring record, such as the sealing of parts, smoothness of movement, etc., it is determined whether there is internal parts damage that leads to poor oil flow and reduced damping, and then the corresponding first softening characteristic information is obtained. In terms of gas leakage, for inflatable shock absorbers, check the internal gas pressure and flow parameters in the first monitoring record. If gas leakage is found and the damping is reduced, it indicates the existence of this softening characteristic. , add it to the first softening characteristic information. When the temperature change causes the shock absorber to become soft, by monitoring the performance parameters of the shock absorber and the changes in oil viscosity at extremely low temperatures, if the oil viscosity decreases and the shock absorber feels soft, determine this factor and include it in the first softening characteristic information. In the case of improper adjustment, analyze the adjustment setting parameters of the adjustable shock absorber in the first monitoring record. If adjustment to a lower damping setting causes the shock absorber to become soft, use it as part of the first softening characteristic information. In the case of wear, by checking the wear degree parameters of the internal parts of the shock absorber after long-term use in the first monitoring record, such as the amount of parts loss and the degree of performance degradation, determine whether the damping capacity is reduced and the shock absorber becomes soft due to wear. If so, include it in the first softening characteristic information.

[0025] The first characteristic parameter set composition subunit is used to combine the first hardening characteristic information and the first softening characteristic information into the first characteristic parameter set. Specifically, the first hardening characteristic information and the first softening characteristic information obtained through the above process are combined to form the first characteristic parameter set. This parameter set integrates information on various specific factors that contribute to the hardening and softening of the shock absorber, providing a comprehensive data foundation for subsequent analysis of the shock absorber's hardness and softness.

[0026] In one possible implementation, the first feedback prediction value acquisition unit further includes a soft and hard feedback prediction model composition subunit, configured to include a hardening feedback prediction channel and a softening feedback prediction channel in the soft and hard feedback prediction model. Specifically, specifying that the soft and hard feedback prediction model is composed of the hardening feedback prediction channel and the softening feedback prediction channel provides a structural foundation for subsequent analysis of characteristic information that causes the shock absorber to harden and soften.

[0027] A first hardening feedback prediction value acquisition subunit is configured to analyze the first hardening characteristic information through the hardening degree feedback prediction channel to obtain a first hardening feedback prediction value. Specifically, the first hardening characteristic information is analyzed through the hardening degree feedback prediction channel. As an analysis module specifically targeting shock absorber hardening characteristics, the hardening degree feedback prediction channel utilizes its internal algorithms and models to process various factors in the first hardening characteristic information. For example, if the first hardening characteristic information includes specific parameters corresponding to factors such as oil oxidation or contamination, internal component wear, etc., the hardening degree feedback prediction channel will use these parameters to evaluate their impact on the shock absorber's hardening degree, thereby obtaining a first hardening feedback prediction value, which represents a quantitative prediction of the shock absorber's hardening status.

[0028] A first softening feedback prediction value acquisition subunit is configured to analyze the first softening characteristic information through the softening degree feedback prediction channel to obtain a first softening feedback prediction value. Specifically, the first softening characteristic information is analyzed through the softening degree feedback prediction channel. The softening degree feedback prediction channel processes parameters corresponding to factors such as oil leakage and internal component damage in the first softening characteristic information based on the shock absorber softening characteristics. By analyzing the relationship between these parameters and the degree of shock absorber softening, the first softening feedback prediction value is obtained, which quantifies the softening condition of the shock absorber.

[0029] The absolute value summation subunit is configured to calculate the absolute value of the sum of the first hardening feedback prediction value and the first softening feedback prediction value as the first feedback prediction value. Specifically, the sum of the absolute values of the first hardening feedback prediction value and the first softening feedback prediction value comprehensively considers the hardening and softening conditions of the shock absorber, resulting in a first feedback prediction value that comprehensively reflects the changing trend of the shock absorber's hardness. By taking the absolute value, positive and negative values can be prevented from canceling each other out, ensuring that the effects of both hardening and softening are fully reflected in the value.

[0030] The hardening feedback prediction channel supervised learning subunit is used in which the hardening feedback prediction channel is an intelligent model obtained by supervised learning of the first training data group, the first training data group includes the training hardening feature information and the training hardening feedback value of the training shock absorber, and the training hardening feedback value refers to the ratio of the first training hardness to the training hardness benchmark corresponding to the training shock absorber. Specifically, the hardening feedback prediction channel is an intelligent model obtained by supervised learning of the first training data group, the first training data group includes the training hardening feature information and the training hardening feedback value of the training shock absorber, and the training hardening feedback value is the ratio of the first training hardness to the training hardness benchmark corresponding to the training shock absorber, and the ratio reflects the degree of hardening of the training shock absorber relative to the benchmark. During the supervised learning process, the model continuously adjusts its own parameters according to the training hardening feature information and the corresponding training hardening feedback value to improve the accuracy of the prediction of the shock absorber hardening situation.

[0031] The softness feedback prediction channel supervised learning subunit is used for the softness feedback prediction channel, which is an intelligent model obtained by supervised learning of the second training data group. The second training data group includes the training softening feature information and the training softening feedback value of the training shock absorber. The training softening feedback value refers to the ratio of the second training softness and hardness to the training softness and hardness benchmark. Specifically, the softness feedback prediction channel is also an intelligent model obtained by supervised learning of the second training data group. The second training data group includes the training softening feature information and the training softening feedback value of the training shock absorber. The training softening feedback value is the ratio of the second training softness and hardness to the training softness and hardness benchmark, which reflects the degree of softening of the training shock absorber relative to the benchmark. Through supervised learning, the softness feedback prediction channel continuously optimizes itself in order to more accurately predict the softening of the shock absorber.

[0032] A first correspondence analysis module 40 is configured to analyze the first correspondence between the first historical node and the first historical softness and hardness to obtain the real-time softness and hardness of the preset shock absorber. Specifically, the first correspondence analysis module 40 analyzes the correspondence between the first historical node and the first historical softness and hardness. Using regression analysis, trend analysis, and other methods, the module fits the data points to determine the functional relationship between time and hardness. The module also considers the effects of vehicle driving conditions (e.g., high-speed driving, sudden braking, sharp turns, etc.) and road conditions (e.g., flat roads, bumpy roads, mountain roads, etc.) on hardness. Ultimately, the module calculates the real-time softness and hardness of the preset shock absorber, providing critical data for subsequent evaluation of shock absorber performance degradation.

[0033] In one possible implementation, the first correspondence analysis module 40 includes a regression fitting unit configured to perform polynomial regression fitting on the first corresponding scatter plot obtained according to the first correspondence to obtain a first fitting formula. Specifically, a first corresponding scatter plot can be obtained based on the first correspondence between the first historical node and the first historical softness and hardness. The scatter plot displays the softness and hardness data at different historical nodes in the form of points on a two-dimensional plane, wherein the horizontal axis generally represents time or historical nodes and the vertical axis represents the softness and hardness values. The scatter plot is processed using a polynomial regression fitting method. Polynomial regression fitting is a mathematical method that finds a suitable polynomial function to approximate these scatter points as much as possible. Based on the distribution of the scatter points, the degree and coefficient of the polynomial are determined so that the fitted function can best reflect the trend of the data, thereby obtaining a first fitting formula that can predict the corresponding softness and hardness values based on the input historical node values.

[0034] A real-time hardness and softness acquisition unit is configured to obtain the real-time hardness and softness at a real-time node based on the first fitting formula. Specifically, the real-time hardness and softness at a real-time node is obtained based on the first fitting formula. After determining the current real-time node (which may be the current time point or a specific operating state point), the value of this real-time node is substituted into the first fitting formula. The first fitting formula is used to calculate a predicted hardness and softness value of the shock absorber at this real-time node, i.e., the real-time hardness and softness. The real-time hardness and softness is a quantitative estimate of the current state of the shock absorber, providing important data for subsequent performance degradation analysis and decision-making.

[0035] The real-time performance attenuation index acquisition module 50 is used to analyze the real-time hardness deviation between the real-time hardness and the hardness benchmark to obtain a real-time performance attenuation index. Specifically, the real-time performance attenuation index acquisition module 50 is used to determine the degree of real-time performance attenuation of the shock absorber. With the hardness benchmark as a reference, the hardness benchmark can be a standard value in a brand new state or the average hardness value of a large number of normal shock absorbers. By comparing the real-time hardness of the preset shock absorber with the hardness benchmark, the real-time hardness deviation is calculated, which reflects the current degree of deviation of the shock absorber from the benchmark state. Then, the real-time hardness deviation is deeply analyzed, and factors such as usage time, mileage, and working environment are comprehensively considered, and it is converted into a real-time performance attenuation index using specific algorithms and models, which provides an important basis for determining whether the shock absorber needs maintenance, replacement or adjustment.

[0036] In one possible implementation, the real-time performance degradation index acquisition module 50 further includes a difference calculation unit configured to record the difference between the real-time hardness and the hardness reference as the real-time hardness deviation. Specifically, the difference calculation unit is first tasked with determining the difference between the real-time hardness and the hardness reference. The hardness reference, as a reference value, represents the hardness of the shock absorber under ideal conditions. The real-time hardness is the actual hardness of the shock absorber at the current moment, as determined through the previous analysis. The difference between the two values is subtracted, resulting in the real-time hardness deviation. The deviation reflects the degree of deviation between the current state of the shock absorber and the ideal state and serves as the basis for subsequent analysis.

[0037] The first performance index extraction unit is configured to extract a first performance index from predetermined shock absorber performance indexes. The first performance index is identified by a first correlation coefficient. Specifically, the first performance index extraction unit extracts the first performance index from the predetermined shock absorber performance indexes. The predetermined performance index is used to evaluate multiple aspects of shock absorber performance. The first performance index is identified by a first correlation coefficient, indicating a certain correlation with the shock absorber's performance degradation. By extracting this specific performance index, further analysis can be performed on its relationship with the softness / hardness deviation.

[0038] The first performance attenuation index weighted calculation unit is used to weight the first correlation coefficient and the normalized real-time soft and hard deviation to obtain a first performance attenuation index. Specifically, after obtaining the first performance index and the real-time soft and hard deviation, the first performance attenuation index weighted calculation unit performs weighted calculation. First, the real-time soft and hard deviation is normalized so that it is within a specific range for better calculation and comparison. The first correlation coefficient and the normalized real-time soft and hard deviation are weighted. The purpose of weighting is to give different weights according to the importance of the two, thereby obtaining a comprehensive first performance attenuation index. The index reflects the attenuation degree of the shock absorber under this performance index.

[0039] A comparative analysis unit is configured to comparatively analyze the first performance degradation index to obtain the real-time performance degradation index. Specifically, the comparative analysis unit finally performs comparative analysis on the first performance degradation index, comparing the first performance degradation index obtained under different conditions, or comparing it with other relevant indicators. Through comparative analysis, the performance degradation of the shock absorber under different conditions can be determined, thereby obtaining a more accurate real-time performance degradation index. The index can comprehensively reflect the degree of real-time performance degradation of the shock absorber, providing an important basis for determining the status of the shock absorber and taking appropriate maintenance measures.

[0040] In one possible implementation, the comparative analysis unit further includes a descending order subunit configured to arrange the first performance degradation index in descending order to obtain a performance degradation sequence. Specifically, the descending order subunit processes the first performance degradation index, which is calculated in the previous steps and reflects the degree of attenuation of the shock absorber under different performance indicators. Arranging the index in descending order, i.e., sorting them from largest to smallest, is intended to better identify the performance indicator with the most severe attenuation. By arranging in descending order, the differences in attenuation levels of various performance indicators can be clearly seen, providing an orderly data foundation for the subsequent extraction of the top performance degradation coefficient.

[0041] The first performance attenuation coefficient extraction subunit is used to take the first performance attenuation coefficient of the first performance in the performance attenuation sequence as the real-time performance attenuation index. Specifically, according to the principle of the wooden barrel, the performance with the most serious attenuation is the shortest board of the shock absorber performance, which can represent the performance attenuation of the entire shock absorber. After the performance attenuation sequence is obtained by arranging in descending order, the first performance attenuation coefficient extraction subunit selects the first performance attenuation coefficient of the first performance from this sequence. The coefficient corresponds to the performance indicator with the greatest attenuation degree, which is used as the real-time performance attenuation index. Because the weakest link often plays a decisive role in evaluating the overall performance attenuation of the shock absorber, by extracting the first performance attenuation coefficient, the actual performance attenuation of the shock absorber can be more accurately reflected, providing a key evaluation indicator for determining whether the shock absorber needs maintenance, replacement or adjustment.

[0042] In a possible implementation, the first performance indicator extraction unit further includes: a predetermined shock absorber performance indicator composition subunit, wherein the predetermined shock absorber performance indicator composition subunit is used for the predetermined shock absorber performance indicators including damping effect, handling stability and tire grip. Specifically, the predetermined shock absorber performance index component sub-unit clarifies that the predetermined shock absorber performance indicators include damping effect, handling stability and tire grip. Damping effect is the key indicator. By measuring parameters such as the damping force and change speed of the shock absorber under different working conditions, a good damping effect can make the vehicle drive more smoothly. Handling stability affects the vehicle's posture when turning, accelerating and braking. By measuring parameters such as roll, pitch and yaw of the vehicle under various driving conditions, a shock absorber with good performance can improve handling confidence. Tire grip is closely related to shock absorber performance. The working state of the shock absorber affects the contact between the tire and the ground. Factors such as the shock absorber's load distribution on the tire and the change in friction under different road conditions are considered during the evaluation. When the shock absorber performance declines, it may cause uneven contact between the tire and the ground and reduce grip. The indicators provide a specific direction and basis for the subsequent analysis of shock absorber performance attenuation, and can more comprehensively and accurately evaluate the shock absorber performance status.

[0043] The embodiment of the present application obtains historical monitoring records of a preset shock absorber, matches records under a first historical node from multiple groups of records, obtains the first historical hardness under the node according to a predetermined hardness and softness strategy analysis, and then analyzes the correspondence between the first historical node and the hardness and softness to obtain the real-time hardness and softness. Finally, the real-time performance attenuation index is obtained by analyzing the deviation between the real-time hardness and the hardness and softness benchmark. This achieves the technical effect of accurately predicting the performance attenuation of the automobile shock absorber in real time and improving the safety of automobile driving by using hardness and softness as the key factors to characterize the state of the shock absorber.

[0044] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0045] The above specific embodiments do not constitute a limitation to the scope of protection of this 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 replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do 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 prediction system for automobile shock absorber performance degradation, characterized in that: include: A historical shock absorber monitoring record acquisition module, which is used to acquire historical shock absorber monitoring records, wherein the historical shock absorber monitoring records include multiple groups of monitoring records of preset shock absorbers at multiple historical nodes; a first monitoring record matching module, configured to match a first monitoring record under a first historical node in the plurality of monitoring records, where the first historical node refers to any one of the plurality of historical nodes; A first historical hardness and softness acquisition module, configured to analyze the first monitoring record according to a predetermined hardness and softness strategy to obtain a first historical hardness and softness at the first historical node; a first correspondence analysis module, configured to analyze a first correspondence between the first historical node and the first historical softness or hardness to obtain a real-time softness or hardness of the preset shock absorber; a real-time performance attenuation index acquisition module, configured to analyze a deviation between the real-time hardness and the real-time hardness of the shock absorber and a hardness benchmark to obtain a real-time performance attenuation index, wherein the hardness benchmark is a reference value representing the hardness of the shock absorber under ideal conditions; The real-time performance decay index acquisition module includes: a difference calculation unit, configured to record a difference between the real-time softness and the softness reference as the real-time softness and hardness deviation; a first performance index extraction unit, the first performance index extraction unit being configured to extract a first performance index from predetermined shock absorber performance indexes, the first performance index being identified by a first correlation coefficient; a first performance decay index weighted calculation unit, configured to weight the first correlation coefficient and the normalized real-time soft and hard deviation to obtain a first performance decay index; a comparison and analysis unit, configured to compare and analyze the first performance degradation index to obtain the real-time performance degradation index; The comparison and analysis unit includes: a descending order arrangement subunit, the descending order arrangement subunit being configured to arrange the first performance decay indexes in descending order to obtain a performance decay sequence; a first performance decay coefficient extraction subunit, the first performance decay coefficient extraction subunit being used to take the first performance decay coefficient of the first performance in the performance decay sequence as the real-time performance decay index; The first performance indicator extraction unit includes: The predetermined shock absorber performance index composition subunit is used for the predetermined shock absorber performance index including damping effect, handling stability and tire grip.

2. The prediction system for automobile shock absorber performance attenuation according to claim 1, characterized in that: The first historical softness and hardness acquisition module includes: A predetermined characteristic indicator unit, wherein the predetermined characteristic indicator unit is used to store predetermined characteristic indicators in the predetermined soft and hard strategy; a first characteristic parameter set acquisition unit, configured to traverse the predetermined characteristic indicator in the first monitoring record to obtain a first characteristic parameter set; a first feedback prediction value acquisition unit, configured to use the first feature parameter set as input information of a soft and hard feedback prediction model to obtain a first feedback prediction value; A feedback adjustment unit is configured to perform feedback adjustment on a softness or hardness reference of the preset shock absorber according to the first feedback prediction value to obtain the first historical softness or hardness.

3. The prediction system for automobile shock absorber performance attenuation according to claim 2, characterized in that: The first feature parameter set acquisition unit includes: A predetermined characteristic index composition subunit, wherein the predetermined characteristic index composition subunit is used for the predetermined characteristic index including a predetermined hardening characteristic index and a predetermined softening characteristic index; a characteristic information acquisition subunit, the characteristic information acquisition subunit being configured to sequentially traverse the predetermined hardening characteristic index and the predetermined softening characteristic index in the first monitoring record to obtain first hardening characteristic information and first softening characteristic information, respectively; The first characteristic parameter set composition subunit is used for the first hardening characteristic information and the first softening characteristic information to form the first characteristic parameter set.

4. The system for predicting automobile shock absorber performance attenuation according to claim 3, characterized in that: The first feedback prediction value acquisition unit includes: A soft and hard feedback prediction model component subunit, wherein the soft and hard feedback prediction model component subunit is used for the soft and hard feedback prediction model to include a hardening degree feedback prediction channel and a softening degree feedback prediction channel; a first hardening feedback prediction value acquisition subunit, configured to analyze the first hardening feature information through the hardening degree feedback prediction channel to obtain a first hardening feedback prediction value; a first softening feedback prediction value obtaining subunit, configured to analyze the first softening characteristic information through the softening degree feedback prediction channel to obtain a first softening feedback prediction value; an absolute value summing subunit, configured to obtain the absolute value of the sum of the first hardening feedback prediction value and the first softening feedback prediction value as the first feedback prediction value; a hardness feedback prediction channel supervised learning subunit, wherein the hardness feedback prediction channel is an intelligent model obtained by supervised learning of a first training data set, wherein the first training data set includes training hardening feature information and a training hardening feedback value of a training shock absorber, wherein the training hardening feedback value refers to the ratio of a first training soft hardness to a training soft hardness benchmark corresponding to the training shock absorber; A softness feedback prediction channel supervised learning subunit, wherein the softness feedback prediction channel is used as an intelligent model obtained by supervised learning of a second training data group, wherein the second training data group includes training softening feature information and training softening feedback value of the training shock absorber, and the training softening feedback value refers to the ratio of the second training softness to the training softness benchmark.

5. The prediction system for automobile shock absorber performance attenuation according to claim 1, characterized in that: The first correspondence analysis module includes: A regression fitting unit, configured to perform polynomial regression fitting on a first corresponding scatter plot obtained according to the first corresponding relationship to obtain a first fitting formula; A real-time softness and hardness obtaining unit is configured to obtain the real-time softness and hardness of a real-time node based on the first fitting formula.

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