Multi-condition shock absorption performance test method and system for electric tricycles

Abstract

The present invention provides a multi-condition shock absorption performance test method and system for an electric tricycle, which relates to the technical field of dynamic balance testing. The method includes: conducting a sensitive variable influence test on the tricycle to be tested to obtain multiple single-condition variable adjustment ranges; generating N multi-condition test parameters through redundant verification; conducting an in-vehicle test to obtain N measured shock absorption performance records; conducting a multi-condition multiple regression analysis to construct a shock absorption performance prediction network; performing population smoothing expansion on the N multi-condition test parameters to obtain a multi-condition parameter solution set; loading the solution set into the shock absorption performance prediction network to obtain a shock absorption performance prediction solution set; and performing multi-dimensional data visualization to output a multi-condition shock absorption performance cloud map. The present invention solves the technical problem that the existing test methods mainly focus on the shock absorption performance analysis under static conditions, ignoring the dynamic changes that the vehicle will experience during actual driving, resulting in insufficient versatility and reliability of the test results.

Description

Technical Field

[0001] The present invention relates to the technical field of dynamic balance testing, and particularly to a multi-condition shock absorption performance testing method and system for electric tricycles. Background Art

[0002] The prior art focuses on improving the shock absorption performance of electric tricycles in various operating environments, such as different road surface roughness, traffic congestion conditions, and different climate conditions, which is of great significance for enhancing driving comfort and vehicle durability. However, traditional shock absorption performance tests usually only consider single or limited working conditions, such as tests on standard roads or at specific speeds, which limits the versatility and reliability of test results. Moreover, many existing test methods mainly focus on the analysis of shock absorption performance under static conditions, ignoring the dynamic changes that the vehicle will experience during actual driving. Summary of the Invention

[0003] This application provides a multi-condition shock absorption performance testing method and system for electric tricycles, aiming to solve the technical problem that the existing test methods mainly focus on the analysis of shock absorption performance under static conditions, ignoring the dynamic changes that the vehicle will experience during actual driving, resulting in insufficient versatility and reliability of test results.

[0004] In the first aspect disclosed in this application, a multi-condition shock absorption performance testing method for electric tricycles is provided. The method includes: conducting a sensitive variable influence test on the tricycle to be tested to obtain multiple single-condition variable adjustment ranges, where the multiple single-condition variable adjustment ranges have multiple single-condition variable adjustment step identifiers, and the tricycle to be tested is an electric tricycle; using the multiple single-condition variable adjustment steps as test adjustment change constraints, and generating N multi-condition test parameters through redundancy verification within the multiple single-condition variable adjustment ranges; conducting an on-vehicle test on the tricycle to be tested using the N multi-condition test parameters to obtain N measured shock absorption performance records; constructing a shock absorption performance prediction network through multi-condition multiple regression analysis of the N measured shock absorption performance records and the N multi-condition test parameters; performing population smoothing expansion on the N multi-condition test parameters to obtain a multi-condition parameter solution set; loading the multi-condition parameter solution set into the shock absorption performance prediction network to obtain a shock absorption performance prediction solution set; and performing multi-dimensional data visualization on the multi-condition parameter solution set and the shock absorption performance prediction solution set to output a multi-condition shock absorption performance cloud map.

[0005] The second aspect disclosed in this application provides a multi-condition shock absorption performance test system for an electric tricycle. The system is used for the above-mentioned multi-condition shock absorption performance test method for an electric tricycle. The system includes: a sensitive variable influence test module, which is used to conduct a sensitive variable influence test on the tricycle to be tested and obtain multiple single-condition variable adjustment ranges. Among them, the multiple single-condition variable adjustment ranges have multiple single-condition variable adjustment step identifiers, and the tricycle to be tested is an electric tricycle; a redundancy check module, which is used to use the multiple single-condition variable adjustment steps as test adjustment change constraints, and generate N multi-condition test parameters through redundancy check within the multiple single-condition variable adjustment ranges; a real vehicle test module, which is used to conduct a real vehicle test on the tricycle to be tested using the N multi-condition test parameters and obtain N measured shock absorption performance records; a multiple regression analysis module, which is used to conduct a multi-condition multiple regression analysis on the N measured shock absorption performance records and the N multi-condition test parameters to construct a shock absorption performance prediction network; a population smoothing expansion module, which is used to conduct population smoothing expansion on the N multi-condition test parameters to obtain a multi-condition parameter solution set; a shock absorption performance prediction module, which is used to load the multi-condition parameter solution set into the shock absorption performance prediction network to obtain a shock absorption performance prediction solution set; a multi-dimensional data visualization module, which is used to conduct multi-dimensional data visualization on the multi-condition parameter solution set and the shock absorption performance prediction solution set and output a multi-condition shock absorption performance cloud map.

[0006] One or more technical solutions provided in this application have at least the following beneficial effects:

[0007] By conducting a sensitive variable influence test on the electric tricycle, the key variables affecting the shock absorption performance are accurately identified, and appropriate adjustment ranges and steps are determined for these variables. This not only improves the pertinence and efficiency of the test, but also ensures that all key performance change factors can be covered in subsequent multi-condition tests; the test parameters formulated through redundancy check ensure the effectiveness and comprehensiveness of the test data, and the results of the real vehicle test provide accurate measured data, which are the basis for constructing an efficient prediction model; by constructing a shock absorption performance prediction network through multi-condition multiple regression analysis, the shock absorption performance can be predicted based on the test parameters, which provides a scientific basis for optimizing the shock absorption design and enhances the accuracy and reliability of the prediction; after conducting population smoothing expansion on the test parameters, a shock absorption performance prediction solution set is obtained through the shock absorption performance prediction network, which increases the breadth and depth of the test, enables the performance prediction to not only be limited to the measured range, but also predict the conditions that have not been directly tested, and improves the flexibility and application range of the shock absorption system design; by conducting multi-dimensional data visualization on the shock absorption performance prediction solution set and the parameter solution set, a multi-condition shock absorption performance cloud map is generated, which provides an intuitive and easy-to-understand data display method for users. This visualization helps users better understand the performance changes under different conditions and optimize the design decision.

[0008] The above description is only an overview of the technical solution of this application. In order to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the specific implementation manners of this application are specifically given below. Description of the Drawings

[0009] Figure 1 It is a schematic flowchart of a multi-condition shock absorption performance test method for an electric tricycle provided by an embodiment of this application.

[0010] Figure 2 It is a schematic structural diagram of a multi-condition shock absorption performance test system for an electric tricycle provided by an embodiment of this application.

[0011] Description of the reference numerals: Sensitive variable influence test module 10, redundancy check module 20, real vehicle test module 30, multiple regression analysis module 40, population smoothing expansion module 50, shock absorption performance prediction module 60, multi-dimensional data visualization module 70. Specific Embodiments

[0012] By providing a multi-condition shock absorption performance test method and system for an electric tricycle, an embodiment of this application solves the technical problem that the existing test methods mainly focus on the shock absorption performance analysis under static conditions and ignore the dynamic changes that the vehicle will experience during actual driving, resulting in insufficient versatility and reliability of the test results.

[0013] After introducing the basic principle of this application, the various non-limiting implementation manners of this application will be specifically introduced below in conjunction with the drawings of the specification. It should be understood that the specific embodiments described here are only used to explain this application and are not used to limit this application.

[0014] In Embodiment 1, as Figure 1 shown, an embodiment of this application provides a multi-condition shock absorption performance test method for an electric tricycle, and the method includes:

[0015] Perform a sensitive variable influence test on the tricycle to be tested to obtain multiple single-condition variable adjustment ranges. Among them, the multiple single-condition variable adjustment ranges have multiple single-condition variable adjustment step identifiers, and the tricycle to be tested is an electric tricycle.

[0016] Perform a sensitive variable influence test on the tricycle to be tested. The goal is to identify the variables that have the greatest impact on the shock absorption performance of the electric tricycle by testing different condition combinations. The condition variables include vehicle speed, load, road surface friction coefficient, road surface roughness, driving acceleration, environmental temperature, humidity, visibility, etc. These variables will be adjusted one by one during the test to evaluate their sensitivity to the shock absorption performance.

[0017] Specifically, standard driving conditions are extracted from the tricycle design information to be tested. These standard driving conditions will serve as the initial reference conditions, and subsequent adjustments will be made around these conditions. According to the standard driving conditions, different condition variables are selected for single-condition testing to test the impact of these variables on the shock absorption performance. Single-condition testing means adjusting each condition variable one by one while keeping other condition variables unchanged. Each time a test is conducted, for each condition setting, indicators related to the shock absorption performance are measured and recorded, including vibration amplitude, frequency, shock absorption response duration, etc.

[0018] During the sensitive variable impact test, determine the adjustment range of each variable, which means determining the minimum and maximum values of each variable in the actual test. For example, the adjustment range of vehicle speed may be from 20 km / h to 60 km / h, and the adjustment range of load may be from 50 kg to 200 kg.

[0019] The adjustment step size refers to the amplitude of each adjustment during the variable adjustment process. For example, assuming the adjustment range of vehicle speed is from 20 km / h to 60 km / h and the initial step size is set to 10 km / h, then the vehicle speed will be tested at 20, 30, 40, 50, 60 km / h. Based on the test results of sensitive variables, the adjustment step size of each variable is dynamically generated. These step sizes reflect how each variable should be adjusted in the actual test to obtain the most accurate shock absorption performance data under different conditions. Specifically, if a variable has a greater impact on the shock absorption performance, a finer step size can be adopted within the adjustment range of this variable; if a variable has a smaller impact on the shock absorption performance, a larger step size can be adopted to reduce unnecessary repeated tests.

[0020] Take the multiple single-condition variable adjustment step sizes as test adjustment change constraints, and generate N multi-condition test parameters through redundancy verification within the multiple single-condition variable adjustment ranges.

[0021] Take the obtained multiple single-condition variable adjustment step sizes as test adjustment change constraints, which means that the change of each variable in the test will follow these step sizes to ensure that the test covers all important condition changes. The adjustment step size determines the amplitude of variable change, which helps to precisely control the test conditions. Redundancy verification is carried out within the multiple single-condition variable adjustment ranges. Specifically, check the points in the parameter space to ensure that their distribution is both uniform and representative, and at the same time delete those redundant or repeated condition points, so as to ensure that the generated test parameters can cover all important condition combinations and avoid repeated or meaningless condition combinations, improving the test efficiency.

[0022] Generate N multi-condition test parameters based on the test adjustment change constraints and the redundancy verification results. These parameters will be used for in-vehicle testing. Each set of parameters represents a specific test scenario, such as different combinations of speed, load, road conditions, etc.

[0023] Perform on-vehicle tests on the tricycle to be tested using the N multi-condition test parameters, and obtain N measured shock absorption performance records.

[0024] Set up the test environment using N multi-condition test parameters, such as setting corresponding road conditions, environmental temperature and humidity, etc., to meet the requirements of the predetermined test parameters. Install necessary measuring equipment on the vehicle, such as accelerometers, displacement sensors, etc. Execute the test according to the generated test parameters to ensure that each set of parameters is tested. During the test, record the shock absorption performance of the vehicle under each set of test parameters, such as vibration amplitude, vibration frequency, shock absorption response duration, etc. Organize the data collected during the test to form N measured shock absorption performance records, which reflect in detail the shock absorption performance of the vehicle under various working conditions.

[0025] Construct a shock absorption performance prediction network by performing multi-condition multiple regression analysis on the N measured shock absorption performance records and the N multi-condition test parameters.

[0026] Organize the N multi-condition test parameters and the corresponding N measured shock absorption performance records, use multiple regression analysis to explore and establish the mathematical relationship between the multi-condition test parameters and the measured shock absorption performance. Select a suitable multiple regression model, such as linear regression, polynomial regression, etc. Establish a regression equation based on the test parameters (independent variables) and the shock absorption performance indicators (dependent variables). Use the relationship obtained from the multiple regression analysis to construct a shock absorption performance prediction network, which can reliably predict the shock absorption performance under different working conditions.

[0027] Perform population smoothing expansion on the N multi-condition test parameters to obtain a multi-condition parameter solution set.

[0028] Perform population smoothing expansion on the N multi-condition test parameters. Specifically, perform data interpolation processing, that is, insert new data points between the original data points. Common interpolation methods include linear interpolation, spline interpolation, etc. Through these methods, new test condition points can be generated within the existing range of working condition test parameters; perform data smoothing processing, process the data through a smoothing algorithm to remove noise or outliers, and introduce more smoothness into the data, making the transition of the data under different working conditions more natural; perform data expansion processing, expand the original data through some numerical methods (such as Monte Carlo simulation, Latin hypercube sampling, etc.) to generate more sample points, so that the data covers a more comprehensive range of working conditions. Obtain a multi-condition parameter solution set through population smoothing expansion. This solution set contains the multi-condition test parameters that have been smoothed and expanded, and these parameters simulate various operating conditions that the electric tricycle may encounter, and can be used to simulate and predict the shock absorption performance of the electric tricycle under wider and more complex working conditions.

[0029] Load the multi-condition parameter solution set into the shock absorption performance prediction network to obtain the shock absorption performance prediction solution set.

[0030] Load the multi-condition parameter solution set into the shock absorption performance prediction network. Use the shock absorption performance prediction network to predict each set of parameters in the solution set, calculate its corresponding shock absorption performance indicators, such as vibration amplitude, frequency, response duration, etc., and organize the prediction results into the shock absorption performance prediction solution set. Each set of input parameters corresponds to a set of predicted shock absorption performance data.

[0031] Perform multi-dimensional data visualization on the multi-condition parameter solution set and the shock absorption performance prediction solution set, and output the multi-condition shock absorption performance cloud map.

[0032] Select the performance indicators to be displayed, such as vibration amplitude, frequency, etc., and their performance in multi-condition tests. Use multi-dimensional data visualization techniques, such as parallel coordinate plots, radar charts, heat maps, etc., to map the multi-condition parameter solution set and the shock absorption performance prediction solution set to the visualization model. Design an interactive tool that enables users to filter and view shock absorption performance data according to different working conditions. This helps to deeply analyze the performance under specific conditions. The finally output multi-condition shock absorption performance cloud map can provide a comprehensive view to show the predicted shock absorption performance of the electric tricycle under different combinations of test parameters, which enhances the practicality and interactivity of the entire test and provides a scientific basis and intuitive tool for optimizing the shock absorption performance of the electric tricycle.

[0033] Furthermore, conduct a sensitive variable impact test on the tricycle to be tested to obtain multiple single-condition variable adjustment ranges. The method includes:

[0034] Extract the standard driving condition from the design information of the tricycle to be tested. The standard driving condition includes standard vehicle speed, standard load, standard road surface friction coefficient, standard road surface roughness, standard driving acceleration, standard driving environment temperature, standard driving environment humidity, and standard driving environment visibility; start from the standard driving condition for single-variable adjustment of the working condition to obtain multiple single-condition test sequences; use the multiple single-condition test sequences to conduct a sensitive variable impact test on the tricycle to be tested to obtain the multiple single-condition variable adjustment ranges; preset the step size proportionality coefficient; calculate the multiple single-condition variable adjustment step sizes according to the multiple single-condition variable adjustment ranges and the step size proportionality coefficient, and use the multiple single-condition variable adjustment step sizes to label the multiple single-condition variable adjustment ranges.

[0035] The standard driving conditions are extracted from the design information of the tricycle to be tested. This process converts non-quantifiable indicators such as road surface type, driving mode, and driving environment into operable and discretized parameters, and uses them as the dimensions of the particle space. These quantified condition variables can be used to perform condition combinations and simulation simulations in the particle space, so as to comprehensively evaluate and optimize the shock absorption performance of the vehicle.

[0036] Among them, the standard vehicle speed is the typical vehicle speed under normal operating conditions; the standard load is to set a standard load value based on the maximum load capacity of the vehicle; the standard road surface friction coefficient is a specific parameter for each road surface type. For example, the friction coefficient of a flat road surface is usually high, such as 0.8 - 1.0, and the friction coefficient of a bumpy road surface is medium, such as 0.5 - 0.7; the standard road surface roughness reflects the unevenness of the road surface and is a specific parameter for different types of road surfaces. For example, the road surface roughness of a flat road surface is usually low, such as 0 - 1, and the road surface roughness of a bumpy road surface is medium, such as 2 - 3; the standard driving acceleration reflects the driving mode, with a positive acceleration indicating acceleration and a negative acceleration indicating deceleration; the standard driving environment temperature is a preset temperature range, reflecting different temperature conditions; the standard driving environment humidity is a preset humidity range, reflecting different humidity conditions; the standard driving environment visibility is the visibility under different weather conditions (such as sunny, rainy, foggy).

[0037] Perform single-variable adjustment of the conditions. Specifically, select a variable, such as vehicle speed, while keeping all other variables unchanged, and gradually adjust the selected variable. For example, the vehicle speed is gradually increased from 30 km / h to 60 km / h, with an increase of 5 km / h each time. For each adjusted variable, generate a test sequence and record all combinations after each variable adjustment. These sequences will cover the range from the lowest to the highest possible adjustment range. When single-variable adjustment is completed for all standard driving conditions, multiple single-condition test sequences are obtained.

[0038] Use multiple single-condition test sequences to test the vehicle configuration in each sequence. Each test is carried out under the condition that a specific single variable changes, so as to observe the impact of this change on the shock absorption performance of the vehicle. Record key shock absorption performance indicators such as vibration amplitude, frequency, response time, etc. during the measurement process, analyze the changes in shock absorption performance when each variable changes, and determine which variable changes have a significant impact on the performance. These variables are regarded as sensitive variables. Based on the test results, determine the effective adjustment range of each sensitive variable. For example, if the vehicle speed has a significant impact on the shock absorption performance in the range of 30 to 50 km / h, then this range is determined as the sensitive adjustment range of the vehicle speed.

[0039] The preset step ratio coefficient, which is a preset constant used to determine the fineness of the variable adjustment step. For example, if the preset step ratio coefficient M = 10, it means that the adjustment range of each single-condition variable is divided into 10 equal parts, and each part is used as a separate step. The size of M depends on the test requirements and resources. A larger M value means a finer step, which can provide more detailed data, but at the same time, it will increase the number and complexity of tests.

[0040] According to the adjustment ranges and step ratio coefficients of multiple single-condition variables, systematically set the variable values in the test sequence to obtain the adjustment steps of multiple single-condition variables. This ensures that the test can cover the entire adjustment range while maintaining the consistency and repeatability of the steps. Use the obtained adjustment steps of multiple single-condition variables to identify the adjustment ranges of multiple single-condition variables, which helps to accurately follow the predetermined steps during the actual execution of the test and ensure the consistency and accuracy of the data.

[0041] Furthermore, taking the adjustment steps of the multiple single-condition variables as test adjustment change constraints, generate N multi-condition test parameters through redundancy verification within the adjustment ranges of the multiple single-condition variables. The method includes:

[0042] Call the test variables for the multiple single-condition test sequences to obtain multiple single-condition test indicators; construct a multi-dimensional working condition space through the multiple single-condition test indicators to obtain a working condition particle point space; select the test working condition space in the working condition particle point space according to the adjustment ranges of the multiple single-condition variables; use the adjustment steps of the multiple single-condition variables as constraints to perform redundancy verification of the test working conditions in the test working condition space until the N multi-condition test parameters are output.

[0043] Call each single-condition variable according to multiple single-condition test sequences. These single-condition variables cover all variable settings from the baseline to each preset limit. Integrate the variables extracted from each single-condition test sequence to obtain multiple single-condition test indicators.

[0044] Associate the collected test indicators to construct a multi-dimensional working condition space. In this space, each point (i.e., a working condition particle point) represents a specific working condition combination. By locating the points generated by each test indicator in the multi-dimensional space, a set of working condition particle points is formed. Each point is a possible working condition scenario, and its position is determined by its corresponding test indicator.

[0045] Based on the determined adjustment ranges of multiple single-condition variables, a specific multi-condition working space is selected. This selection is based on previously tested data, preset importance, and engineering requirements to ensure that the tests focus on the most critical performance impact factors. The obtained multi-condition working space includes all possible variable combinations that may affect the shock absorption performance and is the main focus of the test plan. In the multi-condition working space, each working condition particle point represents a possible test scenario. Through these particle points, the test areas covering all key variable combinations can be identified.

[0046] Using the set adjustment steps of multiple single-condition variables as constraints, verification is carried out within the selected test working condition space. The step constraints ensure appropriate intervals between test points, maintaining the systematicness and coherence of the tests. Within the selected working condition space, each working condition point is checked one by one to confirm whether it is a redundant point, that is, whether there are already sufficiently similar test points included in the test plan. If a redundant point is found, it is excluded. After redundant verification, the remaining working condition points form the final N multi-condition test parameters, which are the basis for actual vehicle tests.

[0047] Furthermore, with the adjustment steps of the multiple single-condition variables as constraints, redundant test working condition verification is carried out in the test working condition space until the N multi-condition test parameters are output. The method includes:

[0048] Select H initial working condition particle points within the test working condition space; with the adjustment steps of the multiple single-condition variables as constraints, perform multiple rounds of random updates on the H initial working condition particle points to obtain H groups of updated working condition particle points; extract H groups of updated multi-dimensional test data of the H groups of updated working condition particle points in the test working condition space; perform network data calls according to the vehicle model information of the tricycle to be tested and the H groups of updated multi-dimensional test data to obtain H groups of historical shock absorption performance records, where the historical shock absorption performance records include historical vibration amplitude, historical vibration frequency, historical shock absorption response duration, historical vibration transmission rate, historical shock absorption offset, and historical vibration attenuation rate; perform redundant screening on the H groups of historical shock absorption performance records using a preset shock absorption performance similarity threshold, and call the N multi-condition test parameters from the H groups of updated multi-dimensional test data according to the screening results.

[0049] Randomly select H initial working condition particle points in the test working condition space, which serve as the basis for subsequent test updates.

[0050] Using multiple single-condition variable adjustment step sizes as constraints, for example, the adjustment step size of vehicle speed is 10 km / h. Then, in the update process, each change in vehicle speed should be an integer multiple of 10 km / h, ensuring that the update of each variable does not exceed its set step size, maintaining the systematicness and controllability of the test. Randomly update each initial condition particle point. In each round of update, randomly select one or more variables for adjustment, and the adjustment amplitude does not exceed the predetermined step size. The update can be an increase or decrease in the variable value, but the result must be ensured to be within the allowable range. Through this multi-round random update, multiple new condition particle points can be generated from each initial point, forming H groups of updated condition particle points. This method increases the diversity of test conditions and helps to more comprehensively explore various condition combinations that may affect the shock absorption performance.

[0051] In the test condition space, use the H groups of updated condition particle points as retrieval conditions to retrieve data and obtain the corresponding H groups of updated multi-dimensional test data.

[0052] Use the vehicle model information of the tricycle to be tested as the query condition to ensure that the historical data obtained matches the type of the vehicle to be tested. Determine the specific parameters of the required historical data, such as vibration amplitude, frequency, etc., based on the H groups of updated multi-dimensional test data. Connect to the database or cloud platform through the network, and retrieve the relevant historical performance data according to the vehicle model and the specified test parameters to obtain the relevant H groups of historical shock absorption performance records. Among them, the historical vibration amplitude is the vibration amplitude record of the vehicle under similar conditions in the past; the historical vibration frequency records the frequency of vibration events; the historical shock absorption response duration is the time from the start of vibration to the complete reaction of the shock absorption system; the historical vibration transmissibility reflects the efficiency of ground vibration transmitted to the vehicle. A lower vibration transmissibility means better shock absorption effect; the historical shock absorption offset refers to the maximum offset of the vehicle shock absorption system during vibration, usually measured at a certain point on the seat or vehicle body; the historical vibration attenuation rate is the attenuation speed of the vehicle vibration from the maximum amplitude to the final stable state, which represents the speed at which the shock absorption system consumes and attenuates the vibration energy.

[0053] Define a preset shock absorption performance similarity threshold for judging the similarity between historical shock absorption performance records. This similarity threshold is set based on key performance indicators such as vibration amplitude, vibration frequency, etc., and is used to determine which data are considered similar and which are unique.

[0054] Use statistical methods, such as Euclidean distance or other correlation metrics, to evaluate the similarity between different records. Check the H groups of historical shock absorption performance records using the preset shock absorption performance similarity threshold, identify and remove redundant records with similar performance. The purpose is to reduce the repetitive information in the dataset and retain unique data with different shock absorption characteristics for more effective testing and analysis.

[0055] According to the filtered historical shock absorption performance records, select the corresponding N multi-condition test parameters from the updated multi-dimensional test data in Group H. These parameters represent different test conditions, ensuring the breadth and depth of the test.

[0056] Furthermore, perform redundant screening on the historical shock absorption performance records in Group H using a preset shock absorption performance similarity threshold, and call the N multi-condition test parameters from the updated multi-dimensional test data in Group H according to the screening results. The method includes:

[0057] By deconstructing the historical shock absorption performance records in Group H, obtain R historical shock absorption performance records; perform combinatorial enumeration on the R historical shock absorption performance records to obtain groups of historical shock absorption performance records; use Euclidean distance calculation to obtain the historical performance similarities of the groups of historical shock absorption performance records; take the R historical shock absorption performance records as R topological nodes, and construct topological connections for the R topological nodes according to the groups of historical shock absorption performance records to generate a shock absorption performance topology; use the shock absorption performance similarity threshold to traverse the historical performance similarities to perform topological connection removal on the shock absorption performance topology, obtaining multiple isolated nodes; according to the mapping relationship between the multiple isolated nodes and the R historical shock absorption performance records, perform redundant screening on the updated multi-dimensional test data in Group H to obtain the N multi-condition test parameters.

[0058] Deconstruct the historical shock absorption performance records in Group H into more detailed individual record units, which means extracting each historical data item in the set separately. Each data item includes various performance indicators such as vibration amplitude, frequency, response duration, etc. Determine and mark the total number R of the deconstructed historical records, and these records will be used as the basic data for analysis and comparison.

[0059] Perform pairwise combination on the R historical shock absorption performance records. The total number of combinations can be calculated using combinatorial mathematics formulas, which is , which means that each pair of historical records will be compared once to identify and analyze the similarities between them. Through these combinatorial enumerations, the relationships between different historical records can be systematically analyzed, such as comparing performance changes under different test conditions.

[0060] Use Euclidean distance to measure the similarity between each pair of historical shock absorption performance records. Euclidean distance is a commonly used measurement method applicable to the straight-line distance between two points in a multi-dimensional data space. For each pair of combined records, calculate their Euclidean distances on each shock absorption performance indicator (such as vibration amplitude, frequency, response duration, etc.). Each distance value reflects the similarity degree of the two groups of records in terms of performance. The smaller the distance, the higher the similarity, and vice versa. After calculation, obtain A historical performance similarity.

[0061] Regard each historical shock absorption performance record as a node in a topological graph. These nodes represent the shock absorption performance of different test scenarios. According to the calculated similarity, establish connections between the nodes. The strength or thickness of the connections can be represented based on the magnitude of the similarity. The connections between records with high similarity are thicker, indicating that they are very close in performance. In this way, all the nodes are connected through their similarities to form a complete shock absorption performance topological graph. This graph is used to visualize the relationships between different historical records and identify patterns in performance.

[0062] Pre - define a shock absorption performance similarity threshold to judge whether two performance records are similar enough. This similarity threshold helps determine whether to retain the connection between two nodes. Traverse all the connections and decide whether to remove the connections based on the comparison result of the similarity with the threshold. If the similarity between two nodes is higher than the threshold, then remove the connection between these two nodes. After the connection is removed, those nodes that are not connected to other nodes become isolated nodes. These nodes represent test conditions with unique performance characteristics, indicating that their shock absorption performance is significantly different from other conditions.

[0063] According to the mapping relationship between the isolated nodes and R historical shock absorption performance records, identify the historical records corresponding to the isolated nodes. These records represent test conditions that are unique and important because they demonstrate distinctive shock absorption performance. In the H - group updated multi - dimensional test data, exclude the data that does not correspond to the isolated nodes. This means removing the test data with similar performance or insufficient to provide new insights. Obtain N multi - condition test parameters from the remaining test data. These parameters are used for future actual tests to ensure that the selected conditions can comprehensively evaluate the shock absorption system of the tricycle.

[0064] Furthermore, by performing a multi - condition multiple regression analysis on the N measured shock absorption performance records and N multi - condition test parameters, construct a shock absorption performance prediction network. The method includes:

[0065] Extract N measured vibration amplitudes from the N measured shock absorption performance records; perform multi-condition multiple regression analysis on the N multi-condition test parameters and the N measured vibration amplitudes to obtain a vibration amplitude prediction function; and so on, perform multi-condition multiple regression analysis on the N measured shock absorption performance records and the N multi-condition test parameters to obtain a vibration frequency prediction function, a shock absorption response duration prediction function, a vibration transmission ratio prediction function, a shock absorption offset prediction function, and a vibration attenuation rate prediction function; construct a vibration frequency prediction channel, a shock absorption response duration prediction channel, a vibration transmission ratio prediction channel, a shock absorption offset prediction channel, and a vibration attenuation rate prediction channel based on the vibration frequency prediction function, the shock absorption response duration prediction function, the vibration transmission ratio prediction function, the shock absorption offset prediction function, and the vibration attenuation rate prediction function; and parallelize the vibration frequency prediction channel, the shock absorption response duration prediction channel, the vibration transmission ratio prediction channel, the shock absorption offset prediction channel, and the vibration attenuation rate prediction channel to complete the construction of the shock absorption performance prediction network.

[0066] From the completed N real vehicle tests, extract the vibration amplitude data corresponding to each test. These data are directly recorded from measurement devices (such as accelerometers or other vibration sensors). The vibration amplitude of each test is associated with its corresponding test parameters for accurate multiple regression analysis.

[0067] Select a multiple regression model to analyze the relationship between the test parameters and the vibration amplitude, including a linear regression model or other more complex statistical models, depending on the distribution of the data and the complexity of the relationship. Use the extracted N multi-condition test parameters as independent variables and the corresponding N measured vibration amplitudes as dependent variables to train the multiple regression model. This process includes parameter estimation, such as the calculation of regression coefficients, verifying the effectiveness of the model through statistical tests, evaluating the goodness of fit and prediction ability of the model. When the model converges, establish the corresponding vibration amplitude prediction function, which can predict the vibration amplitude based on the input multi-condition test parameters and is used to predict the vibration performance of future or untested conditions.

[0068] And so on, extract N measured vibration frequencies, N measured shock absorption response durations, N measured vibration transmission ratios, N measured shock absorption offsets, and N measured vibration attenuation rates from the N measured shock absorption performance records respectively. For each shock absorption performance index, use multiple regression analysis to study its relationship with the multi-condition test parameters, train the corresponding multiple regression model for each relationship respectively, generate the corresponding prediction function for each shock absorption performance index according to the training results, and obtain the vibration frequency prediction function, the shock absorption response duration prediction function, the vibration transmission ratio prediction function, the shock absorption offset prediction function, and the vibration attenuation rate prediction function. These functions can predict the corresponding performance based on new or untested condition parameters.

[0069] Based on the vibration frequency prediction function, shock absorption response duration prediction function, vibration transmission ratio prediction function, shock absorption offset prediction function, and vibration attenuation rate prediction function, corresponding vibration frequency prediction channels, shock absorption response duration prediction channels, vibration transmission ratio prediction channels, shock absorption offset prediction channels, and vibration attenuation rate prediction channels are constructed. Each prediction channel corresponds to a prediction function of a shock absorption performance index. These channels independently process the input test parameters and predict the corresponding performance outputs.

[0070] All the prediction channels are connected in parallel to form a comprehensive shock absorption performance prediction network. This parallel connection method allows the network to process multiple performance indexes simultaneously, providing a comprehensive performance prediction for each test condition. The shock absorption performance prediction network can integrate the individual prediction results to provide a multi-dimensional performance evaluation, including all important shock absorption performance indexes such as vibration frequency, response duration, transmission ratio, etc. This method of integrating multiple prediction models greatly improves the prediction accuracy and application flexibility, making the development of the shock absorption system more efficient and precise.

[0071] Furthermore, multi-dimensional data visualization is performed on the multi-condition parameter solution set and the shock absorption performance prediction solution set to output a multi-condition shock absorption performance cloud map. The method includes: locating a multi-condition particle point set in the test condition space according to the multi-condition parameter solution set; interactively obtaining the shock absorption performance identification rule; pre-constructing a standard shock absorption performance cloud map; using the shock absorption performance identification rule and the shock absorption performance prediction solution set to perform a heat map visualization orientation identification on the standard shock absorption performance cloud map to obtain multiple multi-dimensional performance mapping cloud maps; synchronizing the multiple multi-dimensional performance mapping cloud maps to the multi-condition particle point set to complete the construction of the multi-condition shock absorption performance cloud map.

[0072] In the multi-dimensional test condition space, the corresponding particle points are located according to the multi-condition parameter solution set to obtain a multi-condition particle point set. Each particle point represents a group of specific test conditions and contains all relevant test parameters.

[0073] By interacting with the user, determine how to represent different levels of shock absorption performance in the visualization, including determining which parameter or result combinations are labeled as excellent, average, or poor shock absorption performance. Specifically, according to the user feedback and technical requirements, define a set of criteria to automatically identify and classify different levels of shock absorption performance to obtain the shock absorption performance identification rule.

[0074] Design a basic cloud map, which will be used as a platform to display the multi-condition test results. The cloud map is usually a heat map or other type of high-dimensional visualization chart that can show the changes in shock absorption performance under different test conditions, including color coding, labels, legends, etc. These elements help to explain the data points and performance levels in the figure.

[0075] Apply the defined shock absorption performance identification rules to the shock absorption performance prediction solution set, classify the predicted performance data according to these rules, and assign specific colors or marks to different performance levels. Use the marked data in the completed prediction solution set to generate a heat map on the standard shock absorption performance cloud map. The heat map represents different shock absorption performance levels through the depth of color, where the darker the color, the higher the performance, and vice versa. In this way, clearly mark the performance levels of each working condition in the cloud map to ensure clear and readable information. After identification, obtain multiple multi-dimensional performance mapping cloud maps.

[0076] Synchronize multiple multi-dimensional performance mapping cloud maps with the multi-condition particle point set, which means that each particle point is associated with a specific cloud map. When the user clicks on any particle point, the system can display the specific shock absorption performance cloud map under that working condition. Through the interactivity of the cloud map, the user can quickly view the shock absorption performance under different working conditions by clicking on different particle points. This kind of cloud map provides an intuitive platform where the user can not only see the performance under a single working condition but also compare the performance differences under different working conditions, providing a powerful analysis and display tool for the shock absorption performance evaluation and optimization of electric tricycles.

[0077] In summary, the multi-condition shock absorption performance test method for electric tricycles provided by the embodiments of the present application has the following technical effects:

[0078] By conducting sensitive variable impact tests on electric tricycles, accurately identify the key variables affecting shock absorption performance, and determine appropriate adjustment ranges and step sizes for these variables. This not only improves the pertinence and efficiency of the test but also ensures that all key performance change factors can be covered in subsequent multi-condition tests; the test parameters formulated through redundant verification ensure the effectiveness and comprehensiveness of the test data, and the results of the on-vehicle test provide accurate measured data, which are the basis for constructing an efficient prediction model; construct a shock absorption performance prediction network through multi-condition multiple regression analysis, which can predict shock absorption performance based on test parameters, providing a scientific basis for optimizing shock absorption design and enhancing the accuracy and reliability of prediction; after population smoothing expansion of the test parameters, obtain the shock absorption performance prediction solution set through the shock absorption performance prediction network, increasing the breadth and depth of the test, enabling performance prediction not only within the measured range but also for working conditions not directly tested, improving the flexibility and application scope of shock absorption system design; through multi-dimensional data visualization of the shock absorption performance prediction solution set and parameter solution set, generate multi-condition shock absorption performance cloud maps, providing users with an intuitive and easy-to-understand data display method. This visualization helps users better understand the performance changes under different working conditions and optimize design decisions.

[0079] Embodiment 2, based on the same inventive concept as the multi-condition shock absorption performance test method for electric tricycles in the foregoing embodiment, as Figure 2As shown in the figure, the embodiment of the present application provides a multi-condition shock absorption performance test system for an electric tricycle, and the system includes:

[0080] A sensitive variable influence test module 10, configured to perform a sensitive variable influence test on a tricycle to be tested, and obtain a plurality of single-condition variable adjustment ranges. Among them, the plurality of single-condition variable adjustment ranges have a plurality of single-condition variable adjustment step identifiers, and the tricycle to be tested is an electric tricycle; a redundancy check module 20, configured to use the plurality of single-condition variable adjustment steps as test adjustment change constraints, and generate N multi-condition test parameters through redundancy check within the plurality of single-condition variable adjustment ranges; a real vehicle test module 30, configured to perform a real vehicle test on the tricycle to be tested by using the N multi-condition test parameters, and obtain N measured shock absorption performance records; a multiple regression analysis module 40, configured to construct a shock absorption performance prediction network by performing a multi-condition multiple regression analysis on the N measured shock absorption performance records and the N multi-condition test parameters; a population smoothing expansion module 50, configured to perform population smoothing expansion on the N multi-condition test parameters to obtain a multi-condition parameter solution set; a shock absorption performance prediction module 60, configured to load the multi-condition parameter solution set into the shock absorption performance prediction network to obtain a shock absorption performance prediction solution set; a multi-dimensional data visualization module 70, configured to perform multi-dimensional data visualization on the multi-condition parameter solution set and the shock absorption performance prediction solution set, and output a multi-condition shock absorption performance cloud map.

[0081] Furthermore, the sensitive variable influence test module 10 includes:

[0082] A standard driving condition acquisition unit, configured to extract a standard driving condition from the design information of the tricycle to be tested, and the standard driving condition includes a standard vehicle speed, a standard load, a standard road surface friction coefficient, a standard road surface roughness, a standard driving acceleration, a standard driving environment temperature, a standard driving environment humidity, and a standard driving environment visibility; a single-condition variable adjustment unit, configured to perform single-condition variable adjustment starting from the standard driving condition to obtain a plurality of single-condition test sequences; a sensitive variable influence test unit, configured to perform a sensitive variable influence test on the tricycle to be tested by using the plurality of single-condition test sequences to obtain the plurality of single-condition variable adjustment ranges; a step ratio coefficient preset unit, configured to preset a step ratio coefficient; a variable adjustment range identification unit, configured to calculate the plurality of single-condition variable adjustment steps according to the plurality of single-condition variable adjustment ranges and the step ratio coefficient, and use the plurality of single-condition variable adjustment steps to identify the plurality of single-condition variable adjustment ranges.

[0083] Furthermore, the redundancy check module 20 includes:

[0084] A test variable calling unit for calling test variables for the multiple single-condition test sequences to obtain multiple single-condition test metrics; a multi-dimensional working condition space construction unit for constructing a multi-dimensional working condition space through the multiple single-condition test metrics to obtain a working condition particle point space; a test working condition space selection unit for selecting a test working condition space in the working condition particle point space according to the multiple single-condition variable adjustment ranges; a redundant test working condition verification unit for performing redundant test working condition verification in the test working condition space with the multiple single-condition variable adjustment steps as a constraint until the N multi-condition test parameters are output.

[0085] Furthermore, the redundancy verification module 20 includes:

[0086] An initial working condition particle point selection unit for selecting H initial working condition particle points in the test working condition space; a working condition random update unit for performing multiple rounds of working condition random updates on the H initial working condition particle points with the multiple single-condition variable adjustment steps as a constraint to obtain H groups of updated working condition particle points; an updated multi-dimensional test data extraction unit for extracting H groups of updated multi-dimensional test data of the H groups of updated working condition particle points in the test working condition space; a network data calling unit for performing network data calling according to the vehicle model information of the tricycle to be tested and the H groups of updated multi-dimensional test data to obtain H groups of historical shock absorption performance records, where the historical shock absorption performance records include historical vibration amplitude, historical vibration frequency, historical shock absorption response duration, historical vibration transmission rate, historical shock absorption offset, and historical vibration attenuation rate; a redundancy screening unit for performing redundancy screening on the H groups of historical shock absorption performance records using a preset shock absorption performance similarity threshold and calling the N multi-condition test parameters from the H groups of updated multi-dimensional test data according to the screening result.

[0087] Furthermore, the redundancy verification module 20 includes:

[0088] A historical shock absorption performance record deconstruction unit for deconstructing the H groups of historical shock absorption performance records to obtain R historical shock absorption performance records; a combination enumeration unit for performing combination enumeration on the R historical shock absorption performance records to obtain groups of historical shock absorption performance records; a historical performance similarity calculation unit for calculating the using the Euclidean distance for the groups of historical shock absorption performance records historical performance similarities; a shock absorption performance topology generation unit for using the R historical shock absorption performance records as R topological nodes and constructing topological connections of the R topological nodes according to the groups of historical shock absorption performance records to generate a shock absorption performance topology; a topological connection removal unit for traversing the The historical performance similarity is used to perform topological connection removal on the shock absorption performance topology to obtain multiple isolated nodes; a redundancy screening unit is used to perform redundancy screening on the H groups of updated multi-dimensional test data according to the mapping relationship between the multiple isolated nodes and the R historical shock absorption performance records, so as to obtain the N multi-condition test parameters.

[0089] Furthermore, the multiple regression analysis module 40 includes:

[0090] An actual vibration amplitude extraction unit is used to extract N actual vibration amplitudes from the N actual shock absorption performance records; a working condition multiple regression analysis unit is used to perform a working condition multiple regression analysis on the N multi-condition test parameters and the N actual vibration amplitudes to obtain a vibration amplitude prediction function; a prediction function acquisition unit is used to, by analogy, perform a working condition multiple regression analysis on the N actual shock absorption performance records and the N multi-condition test parameters to obtain a vibration frequency prediction function, a shock absorption response duration prediction function, a vibration transmission ratio prediction function, a shock absorption offset prediction function, and a vibration attenuation rate prediction function; a prediction channel construction unit is used to construct a vibration frequency prediction channel, a shock absorption response duration prediction channel, a vibration transmission ratio prediction channel, a shock absorption offset prediction channel, and a vibration attenuation rate prediction channel based on the vibration frequency prediction function, the shock absorption response duration prediction function, the vibration transmission ratio prediction function, the shock absorption offset prediction function, and the vibration attenuation rate prediction function; a shock absorption performance prediction network construction unit is used to connect the vibration frequency prediction channel, the shock absorption response duration prediction channel, the vibration transmission ratio prediction channel, the shock absorption offset prediction channel, and the vibration attenuation rate prediction channel in parallel to complete the construction of the shock absorption performance prediction network.

[0091] Furthermore, the multi-dimensional data visualization module 70 includes:

[0092] A multi-condition particle point set positioning unit is used to position a multi-condition particle point set in the test working condition space according to the multi-condition parameter solution set; a shock absorption performance identification rule interaction unit is used to interactively obtain a shock absorption performance identification rule; a standard shock absorption performance cloud map construction unit is used to pre-construct a standard shock absorption performance cloud map; a heat map visualization directional identification unit is used to perform heat map visualization directional identification on the standard shock absorption performance cloud map by using the shock absorption performance identification rule and the shock absorption performance prediction solution set to obtain multiple multi-dimensional performance mapping cloud maps; a multi-condition shock absorption performance cloud map construction unit is used to synchronize the multiple multi-dimensional performance mapping cloud maps to the multi-condition particle point set to complete the construction of the multi-condition shock absorption performance cloud map.

[0093] Through the foregoing detailed description of the multi-condition shock absorption performance test method for electric tricycles, those skilled in the art can clearly understand the multi-condition shock absorption performance test system for electric tricycles in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For related parts, reference can be made to the description in the method section.

[0094] The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-condition shock absorption performance test method for an electric tricycle, characterized in that The method includes: Performing a sensitive variable influence test on the tricycle to be tested, obtaining multiple single-condition variable adjustment ranges, wherein the multiple single-condition variable adjustment ranges have multiple single-condition variable adjustment step identifiers, and the tricycle to be tested is an electric tricycle; Taking the multiple single-condition variable adjustment steps as test adjustment change constraints, and generating N multi-condition test parameters through redundancy verification within the multiple single-condition variable adjustment ranges; Performing an on-vehicle test on the tricycle to be tested using the N multi-condition test parameters, obtaining N measured shock absorption performance records; Constructing a shock absorption performance prediction network by performing a multi-condition multiple regression analysis on the N measured shock absorption performance records and the N multi-condition test parameters; Performing population smoothing expansion on the N multi-condition test parameters to obtain a multi-condition parameter solution set; Loading the multi-condition parameter solution set into the shock absorption performance prediction network to obtain a shock absorption performance prediction solution set; Performing multi-dimensional data visualization on the multi-condition parameter solution set and the shock absorption performance prediction solution set, and outputting a multi-condition shock absorption performance cloud map.

2. The multi-condition shock absorption performance test method for an electric tricycle according to claim 1, wherein, Performing a sensitive variable influence test on the tricycle to be tested, obtaining multiple single-condition variable adjustment ranges, the method includes: Extracting a standard driving condition from the design information of the tricycle to be tested, where the standard driving condition includes standard vehicle speed, standard load, standard road surface friction coefficient, standard road surface roughness, standard driving acceleration, standard driving environment temperature, standard driving environment humidity, and standard driving environment visibility; Performing single-variable adjustment of the working condition starting from the standard driving condition to obtain multiple single-condition test sequences; Performing a sensitive variable influence test on the tricycle to be tested using the multiple single-condition test sequences to obtain the multiple single-condition variable adjustment ranges; Presetting a step ratio coefficient; Calculating the multiple single-condition variable adjustment steps according to the multiple single-condition variable adjustment ranges and the step ratio coefficient, and using the multiple single-condition variable adjustment steps to label the multiple single-condition variable adjustment ranges.

3. The multi-condition shock absorption performance test method for an electric tricycle according to claim 2, characterized in that, Taking the multiple single-condition variable adjustment steps as test adjustment change constraints, and generating N multi-condition test parameters through redundancy verification within the multiple single-condition variable adjustment ranges, the method includes: Invoking test variables for the multiple single-condition test sequences to obtain multiple single-condition test indicators; Constructing a multi-dimensional working condition space through the multiple single-condition test indicators to obtain a working condition particle point space; Selecting a test working condition space in the working condition particle point space according to the multiple single-condition variable adjustment ranges; Taking the multiple single-condition variable adjustment steps as constraints, performing redundant test working condition verification in the test working condition space until the N multi-condition test parameters are output.

4. The multi-condition shock absorption performance test method for an electric tricycle according to claim 3, characterized in that Taking the multiple single-condition variable adjustment steps as constraints, performing redundant test working condition verification in the test working condition space until the N multi-condition test parameters are output, the method includes: Selecting H initial working condition particle points in the test working condition space; Taking the multiple single-condition variable adjustment steps as constraints, performing multiple rounds of random working condition updates on the H initial working condition particle points to obtain H groups of updated working condition particle points; Extract the H groups of updated multi-dimensional test data of the H groups of updated working condition particles in the test working condition space; Perform network data calls based on the vehicle model information of the tricycle to be tested and the H groups of updated multi-dimensional test data to obtain H groups of historical shock absorption performance records, where the historical shock absorption performance records include historical vibration amplitude, historical vibration frequency, historical shock absorption response duration, historical vibration transmission rate, historical shock absorption offset, and historical vibration attenuation rate; Redundantly screen the H groups of historical shock absorption performance records using a preset shock absorption performance similarity threshold, and call the N multi-working condition test parameters from the H groups of updated multi-dimensional test data according to the screening results.

5. The multi-condition shock absorption performance test method for an electric tricycle according to claim 4, wherein, Redundantly screen the H groups of historical shock absorption performance records using a preset shock absorption performance similarity threshold, and call the N multi-working condition test parameters from the H groups of updated multi-dimensional test data. The method includes: Obtain R historical shock absorption performance records by deconstructing the H groups of historical shock absorption performance records; Combining and enumerating the R historical shock absorption performance records to obtain groups of historical shock absorption performance records; The historical shock absorption performance records of the group are obtained by calculating the Euclidean distance to obtain the historical performance similarity; Taking the R historical shock absorption performance records as R topological nodes, according to the group of historical shock absorption performance records to construct the topological connections of the R topological nodes, generating a shock absorption performance topology; Traverse the shock absorption performance similarity threshold to traverse the historical performance similarity pairs are used to perform topological connection removal on the shock absorption performance topology to obtain multiple isolated nodes; Redundantly screen the H groups of updated multi-dimensional test data according to the mapping relationship between the multiple isolated nodes and the R historical shock absorption performance records to obtain the N multi-working condition test parameters.

6. The multi-condition shock absorption performance test method for an electric tricycle according to claim 1, characterized in that Construct a shock absorption performance prediction network by performing multi-working condition multiple regression analysis on the N measured shock absorption performance records and the N multi-working condition test parameters. The method includes: Extract N measured vibration amplitudes from the N measured shock absorption performance records; Perform multi-working condition multiple regression analysis on the N multi-working condition test parameters and the N measured vibration amplitudes to obtain a vibration amplitude prediction function; And so on, perform multi-working condition multiple regression analysis on the N measured shock absorption performance records and the N multi-working condition test parameters to obtain a vibration frequency prediction function, a shock absorption response duration prediction function, a vibration transmission rate prediction function, a shock absorption offset prediction function, and a vibration attenuation rate prediction function; Construct a vibration frequency prediction channel, a shock absorption response duration prediction channel, a vibration transmission rate prediction channel, a shock absorption offset prediction channel, and a vibration attenuation rate prediction channel based on the vibration frequency prediction function, the shock absorption response duration prediction function, the vibration transmission rate prediction function, the shock absorption offset prediction function, and the vibration attenuation rate prediction function; Parallel the vibration frequency prediction channel, the shock absorption response duration prediction channel, the vibration transmission rate prediction channel, the shock absorption offset prediction channel, and the vibration attenuation rate prediction channel to complete the construction of the shock absorption performance prediction network.

7. The multi-condition shock absorption performance testing method for an electric tricycle according to claim 3, characterized in that, Perform multi-dimensional data visualization on the multi-working condition parameter solution set and the shock absorption performance prediction solution set, and output a multi-working condition shock absorption performance cloud map. The method includes: Locate the multi-working condition particle point set in the test working condition space according to the multi-working condition parameter solution set; Interactively obtain the shock absorption performance identification rule; Pre-construct a standard shock absorption performance cloud map; Use the shock absorption performance identification rule and the shock absorption performance prediction solution set to perform heat map visualization orientation identification on the standard shock absorption performance cloud map to obtain multiple multi-dimensional performance mapping cloud maps; Synchronize the multiple multi-dimensional performance mapping cloud maps to the multi-working condition particle point set to complete the construction of the multi-working condition shock absorption performance cloud map.

8. A multi-condition shock absorption performance test system for an electric tricycle, characterized in that, For implementing the multi-condition shock absorption performance test method for an electric tricycle described in any one of claims 1-7, the system includes: A sensitive variable influence test module for performing a sensitive variable influence test on the tricycle to be tested, obtaining multiple single-condition variable adjustment ranges, wherein the multiple single-condition variable adjustment ranges have multiple single-condition variable adjustment step identifiers, and the tricycle to be tested is an electric tricycle; A redundancy check module for using the multiple single-condition variable adjustment steps as test adjustment change constraints, and generating N multi-condition test parameters through redundancy check within the multiple single-condition variable adjustment ranges; A real vehicle test module for performing a real vehicle test on the tricycle to be tested using the N multi-condition test parameters, obtaining N measured shock absorption performance records; A multiple regression analysis module for constructing a shock absorption performance prediction network by performing multi-condition multiple regression analysis on the N measured shock absorption performance records and the N multi-condition test parameters; A population smoothing expansion module for performing population smoothing expansion on the N multi-condition test parameters to obtain a multi-condition parameter solution set; A shock absorption performance prediction module for loading the multi-condition parameter solution set into the shock absorption performance prediction network to obtain a shock absorption performance prediction solution set; A multi-dimensional data visualization module for performing multi-dimensional data visualization on the multi-condition parameter solution set and the shock absorption performance prediction solution set, and outputting a multi-condition shock absorption performance cloud map.

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