Multi-working-condition damping performance testing method and system for electro-tricycle

Abstract

The invention provides a multi-working-condition damping performance testing method and system for an electric tricycle, and relates to the technical field of dynamic balance testing, and the method comprises the steps: carrying out the sensitive variable influence testing of a to-be-tested tricycle, and obtaining a plurality of single-working-condition variable adjustment ranges; generating N multi-working-condition test parameters through redundancy check; carrying out a real vehicle test to obtain N actually measured damping performance records; working condition multiple regression analysis is carried out, and a damping performance prediction network is constructed; performing population smooth expansion on the N multi-working-condition test parameters to obtain a multi-working-condition parameter solution set; loading to a damping performance prediction network to obtain a damping performance prediction solution set; and carrying out multi-dimensional data visualization, and outputting a multi-working-condition damping performance cloud picture. The technical problem that a test method in the prior art mainly focuses on shock absorption performance analysis under a static condition and ignores dynamic changes experienced by a vehicle in actual driving, so that the universality and reliability of a test result are insufficient is solved.

Description

Technical Field

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

[0002] Existing technologies focus on improving the shock absorption performance of electric tricycles in various operating environments, such as different road roughness, traffic congestion and different climatic conditions, which is of great significance for improving driving comfort and vehicle durability. However, traditional shock absorption performance tests usually only consider a single or limited number of working conditions, such as tests on standard roads or at specific speeds, which limits the versatility and reliability of the test results. In addition, 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] The present application provides a multi-condition shock absorption performance test method and system for an electric tricycle, aiming to solve the technical problem that the testing methods in the prior art 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.

[0004] The first aspect disclosed in the present application provides a multi-operating-condition shock absorption performance test method for an electric tricycle, the method comprising: performing a sensitive variable influence test on the tricycle to be tested, obtaining a plurality of single-operating-condition variable adjustment ranges, wherein the plurality of single-operating-condition variable adjustment ranges have a plurality of single-operating-condition variable adjustment step length identifiers, and the tricycle to be tested is an electric tricycle; using the plurality of single-operating-condition variable adjustment step lengths as test adjustment change constraints, generating N multi-operating-condition test parameters through redundant verification within the plurality of single-operating-condition variable adjustment ranges; using the N multi-operating-condition test parameters; The three-wheeled vehicle to be tested is tested by using the multi-condition test parameters to obtain N measured shock absorption performance records; a shock absorption performance prediction network is constructed by performing condition multiple regression analysis on the N measured shock absorption performance records and the N multi-condition test parameters; a population smoothing expansion is performed on the N multi-condition test parameters to obtain a multi-condition parameter solution set; the multi-condition parameter solution set is loaded into the shock absorption performance prediction network to obtain a shock absorption performance prediction solution set; multi-dimensional data visualization is performed on the multi-condition parameter solution set and the shock absorption performance prediction solution set, and a multi-condition shock absorption performance cloud map is output.

[0005] The second aspect disclosed in the present application provides a multi-condition shock absorption performance test system for an electric tricycle, the system being used for the above-mentioned multi-condition shock absorption performance test method for an electric tricycle, the system comprising: a sensitive variable impact test module, used to perform a sensitive variable impact test on the tricycle to be tested, and obtain a plurality of single-condition variable adjustment ranges, wherein 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 redundant check module, used to use the plurality of single-condition variable adjustment step sizes as test adjustment change constraints, and generate N multi-condition test parameters through redundant check within the plurality of single-condition variable adjustment ranges; and a real vehicle test module, used to sample The three-wheeled vehicle to be tested is subjected to actual vehicle testing using the N multi-condition test parameters to obtain N measured shock absorption performance records; a multiple regression analysis module is used to construct a shock absorption performance prediction network by performing condition multiple regression analysis on the N measured shock absorption performance records and the N multi-condition test parameters; a population smoothing expansion module is used 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 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 is used 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.

[0006] One or more technical solutions provided in this application have at least the following beneficial effects: By testing the impact of sensitive variables on electric tricycles, the key variables that affect the shock absorption performance are accurately identified, and the appropriate adjustment range and step length 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 redundant verification ensure the validity and comprehensiveness of the test data, and the results of the actual vehicle test provide accurate measured data, which are the basis for building an efficient prediction model; the shock absorption performance prediction network is constructed through condition multiple regression analysis, which can predict the shock absorption performance based on the test parameters, which provides a basis for optimizing the shock absorption design. The design provides a scientific basis and enhances the accuracy and reliability of the prediction; after the test parameters are smoothly expanded, the shock absorption performance prediction solution set is obtained through the shock absorption performance prediction network, which increases the breadth and depth of the test, so that the performance prediction is not limited to the actual measurement range, but can also predict the working conditions that are not directly tested, thereby improving the flexibility and application scope of the shock absorption system design; through multi-dimensional data visualization of the shock absorption performance prediction solution set and the parameter solution set, a multi-condition shock absorption performance cloud map is generated, 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.

[0007] 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic flow chart of a method for testing the multi-condition shock absorption performance of an electric tricycle provided in an embodiment of the present application.

[0009] Figure 2 A schematic diagram of the structure of a multi-condition shock absorption performance testing system for an electric tricycle provided in an embodiment of the present application.

[0010] Explanation of the reference numerals: sensitive variable impact test module 10 , redundancy check module 20 , real vehicle test module 30 , multivariate regression analysis module 40 , population smoothing expansion module 50 , shock absorption performance prediction module 60 , multidimensional data visualization module 70 . DETAILED DESCRIPTION

[0011] The embodiments of the present application provide a multi-condition shock absorption performance test method and system for an electric tricycle, thereby solving the technical problem that the testing methods in the prior art mainly focus on shock absorption performance analysis under static conditions, ignoring the dynamic changes that the vehicle may experience during actual driving, resulting in insufficient versatility and reliability of the test results.

[0012] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0013] Embodiment 1, as Figure 1 As shown, the embodiment of the present application provides a multi-condition shock absorption performance test method for an electric tricycle, the method comprising: A sensitive variable impact test is performed on the tricycle to be tested to obtain a plurality of single-operating-condition variable adjustment ranges, wherein the plurality of single-operating-condition variable adjustment ranges have a plurality of single-operating-condition variable adjustment step identifiers, and the tricycle to be tested is an electric tricycle.

[0014] The three-wheeled vehicle to be tested is tested for the impact of sensitive variables. The goal is to identify the variables that have the greatest impact on the shock absorption performance of the electric three-wheeled vehicle by testing different operating condition combinations. The operating condition variables include vehicle speed, load, road friction coefficient, road roughness, driving acceleration, ambient temperature, humidity, visibility, etc. These variables will be adjusted one by one in the test to evaluate their sensitivity to the shock absorption performance.

[0015] Specifically, standard driving conditions are extracted from the design information of the three-wheeled vehicle to be tested. These standard driving conditions will be used as 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 refers to adjusting each condition variable one by one, keeping other condition variables unchanged. During each test, for each condition setting, indicators related to shock absorption performance are measured and recorded, including vibration amplitude, frequency, shock absorption response time, etc.

[0016] During the sensitive variable impact test, the adjustment range of each variable is determined. This means that the minimum and maximum values ​​of each variable need to be determined in the actual test. For example, the adjustment range of vehicle speed may be from 20km / h to 60km / h, and the adjustment range of load may be from 50kg to 200kg.

[0017] The adjustment step size refers to the amplitude of each adjustment during the variable adjustment process. For example, assuming that the adjustment range of the vehicle speed is 20km / h to 60km / h, and the step size is initially set to 10km / h, the vehicle speed will be tested at 20, 30, 40, 50, and 60km / 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 actual tests in order to obtain the most accurate shock absorption performance data under different working conditions. Specifically, if a certain variable has a greater impact on the shock absorption performance, a finer step size can be used within the adjustment range of the variable; if a certain variable has a smaller impact on the shock absorption performance, a larger step size can be used to reduce unnecessary repeated tests.

[0018] The adjustment step sizes of the multiple single-operating-condition variables are used as test adjustment change constraints, and N multi-operating-condition test parameters are generated through redundancy verification within the adjustment ranges of the multiple single-operating-condition variables.

[0019] The obtained multiple single-operating condition variable adjustment steps are used as test adjustment change constraints, which means that the changes of each variable in the test will follow these steps to ensure that the test covers all important operating condition changes. The adjustment step determines the magnitude of the variable change, which helps to accurately control the test conditions. Redundancy checks are performed within the adjustment range of multiple single-operating condition variables. Specifically, the points in the parameter space are checked to ensure that their distribution is both uniform and representative, and those redundant or repeated operating condition points are deleted to ensure that the generated test parameters can cover all important operating condition combinations, while avoiding repeated or meaningless operating condition combinations and improving test efficiency.

[0020] Based on the test adjustment change constraints and redundant verification results, N multi-condition test parameters are generated. These parameters will be used for actual vehicle testing. Each set of parameters represents a specific test scenario, such as different combinations of speed, load, road conditions, etc.

[0021] The N multi-operating condition test parameters are used to perform actual vehicle tests on the three-wheeled vehicle to be tested, and N actual measured shock absorption performance records are obtained.

[0022] Use N multi-operating condition test parameters to set up the test environment, such as setting the corresponding road conditions, ambient temperature and humidity, etc., to make them meet the predetermined test parameter requirements, install necessary measuring equipment on the actual vehicle, such as accelerometers, displacement sensors, etc., perform the test according to the generated test parameters, and ensure that each set of parameters is tested. During the test, record the shock absorption performance of the actual vehicle under each set of test parameters, such as vibration amplitude, vibration frequency, shock absorption response time, 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 operating conditions.

[0023] A shock absorption performance prediction network is constructed by performing a working condition multiple regression analysis on the N actually measured shock absorption performance records and N multi-working condition test parameters.

[0024] Organize 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 appropriate multiple regression models, such as linear regression, polynomial regression, etc., establish regression equations according to test parameters (independent variables) and shock absorption performance indicators (dependent variables), and 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.

[0025] The N multi-operating condition test parameters are subjected to population smoothing expansion to obtain a multi-operating condition parameter solution set.

[0026] The N multi-condition test parameters are expanded with population smoothing. Specifically, data interpolation is performed, that is, new data points are inserted between the original data points. Common interpolation methods include silky interpolation and spline interpolation. Through these methods, new test condition points can be generated within the existing test condition parameter range. Data smoothing is performed, and the data is processed by a smoothing algorithm to remove noise or outliers, and more smoothness is introduced into the data, so that the transition of the data under different conditions is more natural. Data expansion is performed, and the original data is expanded by 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 conditions. The multi-condition parameter solution set is obtained through population smoothing expansion. This solution set contains the multi-condition test parameters that have been smoothly expanded. These parameters simulate the various operating conditions that electric tricycles may encounter, and can be used to simulate and predict the shock absorption performance of electric tricycles under wider and more complex conditions.

[0027] The multi-operating condition parameter solution set is loaded into the damping performance prediction network to obtain the damping performance prediction solution set.

[0028] The multi-condition parameter solution set is loaded into the shock absorption performance prediction network, and the shock absorption performance prediction network is used to predict each group of parameters in the solution set, and the corresponding shock absorption performance indicators, such as vibration amplitude, frequency, response time, etc., are calculated. The prediction results are organized into a shock absorption performance prediction solution set, and each group of input parameters corresponds to a set of predicted shock absorption performance data.

[0029] Multi-dimensional data visualization is performed on the multi-operating condition parameter solution set and the damping performance prediction solution set, and a multi-operating condition damping performance cloud map is output.

[0030] 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 plots, heat maps, etc. to map the multi-condition parameter solution set and the shock absorption performance prediction solution set to the visualization model. Design interactive tools to enable users to filter and view shock absorption performance data according to different working conditions, which helps to deeply analyze the performance under specific conditions. The final output multi-condition shock absorption performance cloud map can provide a comprehensive view, showing the predicted shock absorption performance of the electric tricycle under different test parameter combinations, which enhances the practicality and interactivity of the entire test and provides a scientific basis and intuitive tools for optimizing the shock absorption performance of electric tricycles.

[0031] Furthermore, a sensitive variable impact test is performed on the three-wheeled vehicle to be tested to obtain a plurality of single-operating condition variable adjustment ranges, and the method includes: A standard driving condition is obtained from the design information of the three-wheeled vehicle to be tested, wherein the standard driving condition includes a standard vehicle speed, a standard load, a standard road friction coefficient, a standard road roughness, a standard driving acceleration, a standard driving environment temperature, a standard driving environment humidity and a standard driving environment visibility; a single variable adjustment of the condition is performed with the standard driving condition as a starting point to obtain a plurality of single condition test sequences; a sensitive variable influence test is performed on the three-wheeled vehicle to be tested using the plurality of single condition test sequences to obtain the plurality of single condition variable adjustment ranges; a step length proportional coefficient is preset; the plurality of single condition variable adjustment step lengths are calculated based on the plurality of single condition variable adjustment ranges and the step length proportional coefficient, and the plurality of single condition variable adjustment step lengths are used to identify the plurality of single condition variable adjustment ranges.

[0032] The standard driving conditions are extracted from the design information of the three-wheeled vehicle to be tested. This process converts non-quantitative indicators such as road type, driving mode and driving environment into operational and discrete parameters, and uses them as dimensions of the particle space. These quantified operating condition variables can be used to combine and simulate operating conditions in the particle space, thereby comprehensively evaluating and optimizing the vehicle's shock absorption performance.

[0033] Among them, the standard vehicle speed is the typical vehicle speed under normal operating conditions; the standard load is a standard load value set based on the maximum load capacity of the vehicle; the standard road friction coefficient is a specific parameter for each road type, such as the friction coefficient of a flat road is usually high, such as 0.8-1.0, and the friction coefficient of a bumpy road is medium, such as 0.5-0.7; the standard road roughness reflects the unevenness of the road surface and is a specific parameter for different types of roads, such as the road roughness of a flat road is usually low, such as 0-1, and the road roughness of a bumpy road is medium, such as 2-3; the standard driving acceleration reflects the driving mode, a positive acceleration is acceleration, and a negative acceleration is 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, and foggy).

[0034] Perform single variable adjustment of the working condition. Specifically, select one variable, such as vehicle speed, and keep all other variables unchanged. Adjust the selected variable step by step, such as gradually increasing the vehicle speed from 30km / h to 60km / h, increasing by 5km / h each time. For each adjusted variable, generate a test sequence and record all combinations after each variable adjustment. These sequences will cover the possible adjustment range from the lowest to the highest. When single variable adjustment is completed for all standard driving conditions, multiple single condition test sequences are obtained.

[0035] Multiple single-condition test sequences are used to test the vehicle configuration in each sequence. Each test is performed when a specific single variable is changed to observe the effect of this change on the vehicle's shock absorption performance. Key shock absorption performance indicators such as vibration amplitude, frequency, response time, etc. are recorded during the measurement process. The changes in shock absorption performance when each variable is changed are analyzed to determine which variables have a significant effect on the performance. These variables are considered sensitive variables. Based on the test results, the effective adjustment range of each sensitive variable is determined. For example, if the vehicle speed has a significant effect on the shock absorption performance when it is in the range of 30 to 50 km / h, then this range is determined as the sensitive adjustment range of the vehicle speed.

[0036] The preset step size proportional coefficient is a preset constant used to determine the fineness of the variable adjustment step. For example, if the preset step size proportional coefficient M=10, it means that the adjustment range of each single working 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 needs and resources. A larger M value means a finer step size, which can provide more detailed data, but it will also increase the number and complexity of tests.

[0037] According to multiple single-operating condition variable adjustment ranges and step ratio coefficients, the variable values ​​in the test sequence are systematically set to obtain multiple single-operating condition variable adjustment steps, which ensures that the test can cover the entire adjustment range while maintaining the consistency and repeatability of the step. The multiple single-operating condition variable adjustment steps obtained are used to identify multiple single-operating condition variable adjustment ranges, which helps to accurately follow the predetermined step when actually executing the test, ensuring the consistency and accuracy of the data.

[0038] Further, the adjustment step lengths of the multiple single-operating-condition variables are used as test adjustment change constraints, and N multi-operating-condition test parameters are generated through redundancy verification within the adjustment ranges of the multiple single-operating-condition variables. The method includes: Test variables are called for the multiple single-condition test sequences to obtain multiple single-condition test indicators; a multidimensional condition space is constructed through the multiple single-condition test indicators to obtain a condition particle point space; a test condition space is selected in the condition particle point space according to the adjustment ranges of the multiple single-condition variables; and redundant test condition verification is performed in the test condition space with the adjustment step sizes of the multiple single-condition variables as constraints until the N multi-condition test parameters are output.

[0039] According to multiple single-condition test sequences, each single-condition variable is called, and these single-condition variables cover all variable settings from the baseline to each preset limit. The variables extracted from each single-condition test sequence are integrated to obtain multiple single-condition test indicators.

[0040] The collected test indicators are associated to construct a multidimensional working condition space. In this space, each point (i.e., working condition particle point) represents a specific working condition combination. By locating the points generated by each test indicator in the multidimensional space, a working condition particle point set is formed. Each point is a possible working condition scenario, and its position is determined by its corresponding test indicator.

[0041] According to the determined adjustment ranges of multiple single-condition variables, a specific multi-dimensional condition space is selected. This selection is based on the data from previous tests, the preset importance and engineering requirements to ensure that the test focuses on the most critical performance influencing factors. The obtained multi-dimensional condition space includes all variable combinations that may affect the shock absorption performance and is the main focus of the test plan. In the multi-dimensional condition space, each condition particle point represents a possible test scenario. Through these particle points, the test area covering all key variable combinations can be identified.

[0042] Using the set multiple single-operating condition variable adjustment step sizes as constraints, verification is performed within the selected test operating condition space. The step size constraint ensures the appropriate intervals between test points and maintains the systematicness and continuity of the test. Within the selected operating condition space, each operating point is checked one by one to confirm whether it is a redundant point, that is, sufficiently similar test points have been included in the test plan. If redundant points are found, they are excluded. After redundancy verification, the remaining operating points form the final N multi-operating condition test parameters, which are the basis for actual vehicle testing.

[0043] Furthermore, taking the adjustment step sizes of the multiple single-operating-condition variables as constraints, performing redundant test condition verification in the test condition space until the N multi-operating-condition test parameters are output, the method includes: H initial working condition particle points are selected in the test working condition space; with the adjustment step sizes of the multiple single working condition variables as constraints, the H initial working condition particle points are randomly updated for multiple rounds of working conditions to obtain H groups of updated working condition particle points; H groups of updated multi-dimensional test data of the H groups of updated working condition particle points are extracted in the test working condition space; networked data is called according to the vehicle model information of the three-wheeled vehicle to be tested and the H groups of updated multi-dimensional test data to obtain H groups of historical shock absorption performance records, wherein the historical shock absorption performance records include historical vibration amplitude, historical vibration frequency, historical shock absorption response time, historical vibration transmission rate, historical shock absorption offset and historical vibration attenuation rate; a preset shock absorption performance similarity threshold is used to perform redundant screening on the H groups of historical shock absorption performance records, and the N multi-working condition test parameters are called from the H groups of updated multi-dimensional test data according to the screening results.

[0044] In the test condition space, H initial condition particle points are randomly selected, and these points serve as the basis for subsequent test updates.

[0045] Use multiple single-condition variable adjustment steps as constraints. For example, if the adjustment step of vehicle speed is 10 km / h, each change in vehicle speed during the update process should be an integer multiple of 10 km / h to ensure that the update of each variable does not exceed its set step size. Maintain the systematicness and controllability of the test, and randomly update each initial condition particle point. In each round of update, randomly select one or more variables for adjustment. The adjustment range does not exceed the predetermined step size. The update can be to increase or decrease the variable value, but the result must be within the allowable range. Through this multiple rounds of random update, each initial point can generate multiple new condition particle points to form H groups of updated condition particle points. This method increases the diversity of test conditions and helps to more comprehensively explore various combinations of conditions that may affect shock absorption performance.

[0046] In the test condition space, data retrieval is performed with H groups of updated condition particle points as retrieval conditions to obtain the corresponding H groups of updated multi-dimensional test data.

[0047] The vehicle model information of the three-wheeled vehicle to be tested is used as the query condition to ensure that the historical data obtained matches the type of vehicle to be tested. The specific parameters of the required historical data, such as vibration amplitude, frequency, etc., are determined based on the H group updated multi-dimensional test data. The database or cloud platform is connected to the network, and the relevant historical performance data is retrieved according to the vehicle model and the specified test parameters to obtain the relevant H group historical shock absorption performance records, wherein the historical vibration amplitude is the vibration amplitude record of the vehicle under similar working conditions in the past; the historical vibration frequency records the frequency of vibration events; the historical shock absorption response time is the time from the start of vibration to the complete response of the shock absorption system; the historical vibration transmission rate reflects the efficiency of ground vibration transmission to the vehicle, and a lower vibration transmission rate means a better shock absorption effect; the historical shock absorption offset refers to the maximum offset of the vehicle shock absorption system during the vibration process, which is usually measured at a certain point on the seat or body; the historical vibration attenuation rate is the attenuation rate of the vehicle vibration from the maximum amplitude to the final stable state, which indicates the speed at which the shock absorption system consumes and attenuates the vibration energy.

[0048] A preset shock absorption performance similarity threshold is defined to determine the similarity between historical shock absorption performance records. The similarity threshold is set based on key performance indicators such as vibration amplitude, vibration frequency, etc., and is used to determine which data is considered similar and which is unique.

[0049] Use statistical methods, such as Euclidean distance or other correlation metrics, to evaluate the similarity between different records. Use a preset shock absorption performance similarity threshold to check the historical shock absorption performance records of group H, identify and remove redundant records with similar performance, with the aim of reducing duplicate information in the data set and retaining unique data that exhibits different shock absorption characteristics for more effective testing and analysis.

[0050] According to the screened historical shock absorption performance records, corresponding N multi-condition test parameters are selected from the H group of updated multi-dimensional test data. These parameters represent different test conditions to ensure the breadth and depth of the test.

[0051] Further, the H group of historical damping performance records are redundantly screened using a preset damping performance similarity threshold, and the N multi-condition test parameters are called from the H group of updated multi-dimensional test data according to the screening result. The method includes: By deconstructing the H groups of historical shock absorption performance records, R historical shock absorption performance records are obtained; and the R historical shock absorption performance records are combined and enumerated to obtain The historical shock absorption performance records of the group are obtained by using Euclidean distance calculation Set of historical shock absorption performance records The R historical damping performance records are used as R topological nodes according to the The historical shock absorption performance records are grouped to construct the topological connections of the R topological nodes to generate the shock absorption performance topology; the shock absorption performance similarity threshold is used to traverse the The topological connections of the shock absorption performance topology are removed based on the historical performance similarities to obtain multiple isolated nodes; and according to the mapping relationship between the multiple isolated nodes and the R historical shock absorption performance records, the H groups of updated multidimensional test data are redundantly screened to obtain the N multi-condition test parameters.

[0052] Deconstructing the historical shock absorption performance records of group H into more detailed individual record units means extracting each historical data in the set separately. Each data includes multiple performance indicators such as vibration amplitude, frequency, response time, etc., and determining and marking the total number of historical records R after deconstruction. These records will serve as the basic data for analysis and comparison.

[0053] The total number of combinations of R historical shock absorption performance records can be calculated by the combinatorial mathematical formula, 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 relationship between different historical records can be systematically analyzed, such as comparing performance changes under different test conditions.

[0054] The Euclidean distance is used to measure the similarity between each pair of historical shock absorption performance records. The Euclidean distance is a commonly used measurement method that is applicable to the straight-line distance between two points in a multidimensional data space. For each pair of combined records, their Euclidean distances on various shock absorption performance indicators (such as vibration amplitude, frequency, response time, etc.) are calculated. Each distance value reflects the degree of similarity between the two sets of records in terms of performance. The smaller the distance, the higher the similarity, and vice versa. Historical performance similarity.

[0055] Each historical shock absorption performance record is regarded as a node in the topological graph. These nodes represent the shock absorption performance of different test situations. According to the calculated similarity, connections are established for the nodes. The strength or thickness of the connection can be represented based on the size of the similarity. The connection between records with high similarity is thicker, indicating that they are very close in performance. In this way, all nodes are connected through their similarities to form a complete shock absorption performance topological graph. This graph is used to visualize the relationship between different historical records and identify performance patterns.

[0056] A shock absorption performance similarity threshold is predefined to determine whether two performance records are similar enough. The similarity threshold helps determine whether to retain the connection between two nodes, traverse all connections, and decide whether to remove the connection based on the comparison result of the similarity and the threshold. If the similarity between two nodes is higher than the threshold, the connection between the two nodes is removed. 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.

[0057] According to the mapping relationship between isolated nodes and R historical shock absorption performance records, the historical records corresponding to the isolated nodes are identified. The test conditions represented by these records are unique and important because they show distinctive shock absorption performance. In the H group of updated multi-dimensional test data, those data that do not correspond to the isolated nodes are excluded, which means removing those test data with similar performance or insufficient to provide new insights. N multi-condition test parameters are obtained 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.

[0058] Furthermore, by performing a multivariate regression analysis on the N measured shock absorption performance records and the N multi-operation test parameters, a shock absorption performance prediction network is constructed, and the method includes: Extract N measured vibration amplitudes from the N measured shock absorption performance records; perform 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 similarly, perform 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 time prediction function, a vibration transmissibility prediction function, a shock absorption offset prediction function, and a vibration attenuation prediction function; construct a vibration frequency prediction channel, a shock absorption response time prediction channel, a vibration transmissibility prediction channel, a shock absorption offset prediction channel, and a vibration attenuation prediction channel based on the vibration frequency prediction function, the shock absorption response time prediction function, the vibration transmissibility prediction function, the shock absorption offset prediction function, and the vibration attenuation prediction function; connect the vibration frequency prediction channel, the shock absorption response time prediction channel, the vibration transmissibility prediction channel, the shock absorption offset prediction channel, and the vibration attenuation prediction channel in parallel to complete the construction of the shock absorption performance prediction network.

[0059] From the N completed vehicle tests, the vibration amplitude data corresponding to each test is extracted. These data are recorded directly from the measuring equipment (such as accelerometers or other vibration sensors). The vibration amplitude of each test is associated with its corresponding test parameters to facilitate accurate multivariate regression analysis.

[0060] 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. The effectiveness of the model is verified through statistical tests to evaluate the model's fit and predictive ability. When the model converges, establish a 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.

[0061] By analogy, N measured vibration frequencies, N measured shock absorption response times, N measured vibration transmissibility, N measured shock absorption offsets, and N measured vibration attenuation rates are extracted from N measured shock absorption performance records respectively. For each shock absorption performance indicator, multivariate regression analysis is used to study the relationship between it and multi-condition test parameters. For each relationship, a corresponding multivariate regression model is trained respectively. According to the training results, a corresponding prediction function is generated for each shock absorption performance indicator to obtain a vibration frequency prediction function, a shock absorption response time prediction function, a vibration transmissibility prediction function, a shock absorption offset prediction function, and a vibration attenuation rate prediction function. These functions can predict the corresponding performance based on new or untested operating parameters.

[0062] Based on the vibration frequency prediction function, shock absorption response time prediction function, vibration transmission rate prediction function, shock absorption offset prediction function and vibration attenuation rate prediction function, corresponding vibration frequency prediction channel, shock absorption response time prediction channel, vibration transmission rate prediction channel, shock absorption offset prediction channel and vibration attenuation rate prediction channel 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 output.

[0063] All prediction channels are connected in parallel to form a comprehensive shock absorption performance prediction network. This parallel connection allows the network to process multiple performance indicators at the same time and provide comprehensive performance predictions for each test condition. The shock absorption performance prediction network can integrate each individual prediction result to provide a multi-dimensional performance evaluation, including all important shock absorption performance indicators, such as vibration frequency, response time, transmission rate, etc. This method of integrating multiple prediction models greatly improves the accuracy of the prediction and the flexibility of application, making the development of the shock absorption system more efficient and accurate.

[0064] Furthermore, multi-dimensional data visualization is performed on the multi-condition parameter solution set and the shock absorption performance prediction solution set, and a multi-condition shock absorption performance cloud map is output. 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 shock absorption performance identification rules; pre-constructing a standard shock absorption performance cloud map; using the shock absorption performance identification rules and the shock absorption performance prediction solution set to perform thermal map visualization and directional 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.

[0065] 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 specific set of test conditions and contains all relevant test parameters.

[0066] Through interaction with the user, it is determined how to represent different levels of shock absorption performance in visualization, including determining which parameters or result combinations are identified as excellent, average or poor shock absorption performance. Based on user feedback and technical requirements, a set of criteria is defined to automatically identify and classify different levels of shock absorption performance and obtain shock absorption performance identification rules.

[0067] Design a basic cloud map, which will serve as a platform for displaying 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. It includes color coding, labels, legends, etc. These elements help explain the data points and performance levels in the graph.

[0068] The defined damping performance identification rules are applied to the damping performance prediction solution set. The prediction performance data are classified according to these rules, and specific colors or marks are assigned to different performance levels. The data in the marked prediction solution set are used to generate a heat map on the standard damping performance cloud map. The heat map represents different damping performance levels by the depth of color. The darker the color, the higher the performance, and vice versa. In this way, the performance level of each working condition is clearly marked in the cloud map to ensure that the information is clear and readable. After the identification is completed, multiple multi-dimensional performance mapping cloud maps are obtained.

[0069] Synchronizing multiple multi-dimensional performance mapping cloud graphs with multi-operating condition particle point sets means that each particle point is associated with a specific cloud graph. When the user clicks on any particle point, the system can display the specific shock absorption performance cloud graph under that operating condition. Through the interactivity of the cloud graph, the user can quickly view the shock absorption performance under different operating conditions by clicking on different particle points. This cloud graph provides an intuitive platform where users can not only see the performance under a single operating condition, but also compare the performance differences under different operating conditions, providing a powerful analysis and display tool for shock absorption performance evaluation and optimization of electric tricycles.

[0070] In summary, the multi-operating-condition shock absorption performance testing method for an electric tricycle provided in the embodiments of the present application has the following technical effects: By testing the impact of sensitive variables on electric tricycles, the key variables that affect the shock absorption performance are accurately identified, and the appropriate adjustment range and step length 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 redundant verification ensure the validity and comprehensiveness of the test data, and the results of the actual vehicle test provide accurate measured data, which are the basis for building an efficient prediction model; the shock absorption performance prediction network is constructed through condition multiple regression analysis, which can predict the shock absorption performance based on the test parameters, which provides a basis for optimizing the shock absorption design. The design provides a scientific basis and enhances the accuracy and reliability of the prediction; after the test parameters are smoothly expanded, the shock absorption performance prediction solution set is obtained through the shock absorption performance prediction network, which increases the breadth and depth of the test, so that the performance prediction is not limited to the actual measurement range, but can also predict the working conditions that are not directly tested, thereby improving the flexibility and application scope of the shock absorption system design; through multi-dimensional data visualization of the shock absorption performance prediction solution set and the parameter solution set, a multi-condition shock absorption performance cloud map is generated, 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.

[0071] Embodiment 2 is based on the same inventive concept as the multi-condition shock absorption performance testing method for an electric tricycle in the previous embodiment. Figure 2As shown, the embodiment of the present application provides a multi-condition shock absorption performance testing system for an electric tricycle, the system comprising: The sensitive variable impact test module 10 is used to perform a sensitive variable impact test on the tricycle to be tested, and obtain multiple single-operating condition variable adjustment ranges, wherein the multiple single-operating condition variable adjustment ranges have multiple single-operating condition variable adjustment step identifiers, and the tricycle to be tested is an electric tricycle; the redundancy check module 20 is used to use the multiple single-operating condition variable adjustment step as a test adjustment change constraint, and generate N multi-operating condition test parameters through redundant check within the multiple single-operating condition variable adjustment ranges; the actual vehicle test module 30 is used to perform an actual vehicle test on the tricycle to be tested using the N multi-operating condition test parameters to obtain N measured shock absorption Performance records; a multiple regression analysis module 40, used to construct a shock absorption performance prediction network by performing condition multiple regression analysis on the N measured shock absorption performance records and N multi-condition test parameters; a population smoothing expansion module 50, used 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, 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 70, used 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.

[0072] Furthermore, the sensitive variable impact test module 10 includes: A standard driving condition acquisition unit is used to extract standard driving conditions from the design information of the three-wheeled vehicle to be tested, wherein the standard driving conditions include standard vehicle speed, standard load, standard road friction coefficient, standard road roughness, standard driving acceleration, standard driving environment temperature, standard driving environment humidity and standard driving environment visibility; a condition single variable adjustment unit is used to perform condition single variable adjustment based on the standard driving condition as the starting point to obtain multiple single condition test sequences; a sensitive variable impact test unit is used to perform sensitive variable impact test on the three-wheeled vehicle to be tested using the multiple single condition test sequences to obtain the multiple single condition variable adjustment ranges; a step length proportional coefficient preset unit is used to preset the step length proportional coefficient; a variable adjustment range identification unit is used to calculate the multiple single condition variable adjustment steps according to the multiple single condition variable adjustment ranges and the step length proportional coefficient, and use the multiple single condition variable adjustment steps to identify the multiple single condition variable adjustment ranges.

[0073] Furthermore, the redundancy check module 20 includes: A test variable calling unit is used to call test variables for the multiple single-condition test sequences to obtain multiple single-condition test indicators; a multidimensional condition space construction unit is used to construct a multidimensional condition space through the multiple single-condition test indicators to obtain a condition particle point space; a test condition space selection unit is used to select a test condition space in the condition particle point space according to the adjustment ranges of the multiple single-condition variables; a redundant test condition verification unit is used to perform redundant test condition verification in the test condition space with the adjustment step sizes of the multiple single-condition variables as constraints until the N multi-condition test parameters are output.

[0074] Furthermore, the redundancy check module 20 includes: An initial working condition particle point selection unit is used to select H initial working condition particle points in the test working condition space; a working condition random update unit is used to perform multiple rounds of working condition random updates on the H initial working condition particle points with the adjustment step sizes of the multiple single working condition variables as constraints, and obtain H groups of updated working condition particle points; an updated multi-dimensional test data extraction unit is used to 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; a networked data calling unit is used to perform networked data calling according to the vehicle model information of the three-wheeled vehicle to be tested and the H groups of updated multi-dimensional test data, and obtain H groups of historical shock absorption performance records, wherein the historical shock absorption performance records include historical vibration amplitude, historical vibration frequency, historical shock absorption response time, historical vibration transmission rate, historical shock absorption offset and historical vibration attenuation rate; a redundant screening unit is used to 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-working condition test parameters from the H groups of updated multi-dimensional test data according to the screening results.

[0075] Furthermore, the redundancy check module 20 includes: A historical shock absorption performance record deconstruction unit is used to obtain R historical shock absorption performance records by deconstructing the H groups of historical shock absorption performance records; a combination enumeration unit is used to combine and enumerate the R historical shock absorption performance records to obtain A historical performance similarity calculation unit for obtaining the historical performance similarity using Euclidean distance calculation. Set of historical shock absorption performance records a shock absorption performance topology generation unit, which is used to use the R historical shock absorption performance records as R topological nodes, according to the The historical shock absorption performance records are used to construct the topological connections of the R topological nodes to generate the shock absorption performance topology; the topological connection elimination unit is used to traverse the shock absorption performance similarity threshold The topological connection of the shock absorption performance topology is removed according to the historical performance similarity to obtain multiple isolated nodes; a redundant screening unit is used to perform redundant screening on the H groups of updated multidimensional test data according to the mapping relationship between the multiple isolated nodes and R historical shock absorption performance records to obtain the N multi-condition test parameters.

[0076] Furthermore, the multivariate regression analysis module 40 includes: a measured vibration amplitude extraction unit, used to extract N measured vibration amplitudes from the N measured shock absorption performance records; a working condition multiple regression analysis unit, used to perform 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; a prediction function acquisition unit, used to perform working condition multiple regression analysis on the N measured shock absorption performance records and the N multi-working condition test parameters, and obtain a vibration frequency prediction function, a shock absorption response time prediction function, a vibration transmission rate prediction function, a shock absorption offset prediction function and a vibration attenuation rate ... multi-working condition test parameters, and obtain a vibration frequency prediction function, a shock absorption response time prediction function, a shock absorption transmission rate prediction function, a shock absorption offset prediction function and a vibration attenuation rate prediction function; a prediction function acquisition unit, used to perform working condition multiple regression analysis on the N multi-working condition test parameters, and obtain a vibration frequency prediction function, a shock absorption response time prediction function, a shock absorption transmission rate prediction function, a shock absorption offset prediction function and a vibration attenuation rate prediction function; a prediction function acquisition unit, used to perform working condition multiple regression analysis on the N multi-working condition test parameters, and obtain a vibration frequency prediction function, a shock absorption response time prediction function, a shock absorption transmission rate prediction function, a shock absorption offset prediction function and a vibration attenuation rate prediction function; a prediction function acquisition unit, used to perform working condition multiple regression analysis on the N multi-working condition test parameters, and obtain a vibration frequency prediction function, a shock absorption response A channel construction unit is used to construct a vibration frequency prediction channel, a shock absorption response time prediction channel, a vibration transmissibility 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 time prediction function, the vibration transmissibility 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 time prediction channel, the vibration transmissibility 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.

[0077] Furthermore, the multidimensional data visualization module 70 includes: A multi-condition particle point set positioning unit is used to position the multi-condition particle point set in the test condition space according to the multi-condition parameter solution set; a damping performance identification rule interaction unit is used to interactively obtain the damping performance identification rule; a standard damping performance cloud map construction unit is used to pre-construct a standard damping performance cloud map; a thermal map visualization directional identification unit is used to use the damping performance identification rule and the damping performance prediction solution set to perform thermal map visualization directional identification on the standard damping performance cloud map to obtain multiple multi-dimensional performance mapping cloud maps; a multi-condition damping 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 damping performance cloud map.

[0078] Through the above-mentioned detailed description of the multi-condition shock absorption performance testing method for an electric tricycle, those skilled in the art can clearly understand the multi-condition shock absorption performance testing system for an electric tricycle in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0079] The above 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 may 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 conform to 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 comprises: Performing a sensitive variable impact test on the tricycle to be tested, obtaining a plurality of single-operating-condition variable adjustment ranges, wherein the plurality of single-operating-condition variable adjustment ranges have a plurality of single-operating-condition variable adjustment step length identifiers, and the tricycle to be tested is an electric tricycle; Using the adjustment step lengths of the multiple single-operating-condition variables as test adjustment change constraints, and generating N multi-operating-condition test parameters through redundancy verification within the adjustment ranges of the multiple single-operating-condition variables; Using the N multi-operating condition test parameters to conduct a real vehicle test on the three-wheeled vehicle to be tested, and obtain N actual measured shock absorption performance records; By performing a multivariate regression analysis on the N measured shock absorption performance records and the N multi-condition test parameters, a shock absorption performance prediction network is constructed; Performing population smoothing expansion on the N multi-operating condition test parameters to obtain a multi-operating condition parameter solution set; Loading the multi-operating condition parameter solution set into the damping performance prediction network to obtain a damping performance prediction solution set; Multi-dimensional data visualization is performed on the multi-operating condition parameter solution set and the damping performance prediction solution set, and a multi-operating condition damping performance cloud map is output.

2. The multi-operating-condition shock absorption performance testing method for an electric tricycle according to claim 1, characterized in that: A sensitive variable impact test is performed on the tricycle to be tested to obtain a plurality of single-operating condition variable adjustment ranges, the method comprising: Extracting standard driving conditions from the design information of the three-wheeled vehicle to be tested, wherein the standard driving conditions include standard vehicle speed, standard load, standard road friction coefficient, standard road roughness, standard driving acceleration, standard driving environment temperature, standard driving environment humidity and standard driving environment visibility; Taking the standard driving condition as a starting point, single variable adjustment of the condition is performed to obtain multiple single condition test sequences; Using the multiple single-operating-condition test sequences to perform a sensitive variable impact test on the three-wheeled vehicle to be tested, and obtaining the multiple single-operating-condition variable adjustment ranges; Preset step size ratio; The multiple single operating condition variable adjustment steps are calculated according to the multiple single operating condition variable adjustment ranges and step ratio coefficients, and the multiple single operating condition variable adjustment steps are used to identify the multiple single operating condition variable adjustment ranges.

3. The multi-operating-condition shock absorption performance testing method for an electric tricycle as claimed in claim 2, characterized in that: The adjustment step lengths of the multiple single-operating-condition variables are used as test adjustment change constraints, and N multi-operating-condition test parameters are generated through redundancy verification within the adjustment ranges of the multiple single-operating-condition variables. The method includes: Calling test variables on the multiple single-operating-condition test sequences to obtain multiple single-operating-condition test indicators; Constructing a multi-dimensional working condition space through the multiple single working 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 adjustment ranges of the multiple single working condition variables; With the adjustment step sizes of the multiple single-operating-condition variables as constraints, redundant test condition verification is performed in the test condition space until the N multi-operating-condition test parameters are output.

4. The multi-operating-condition shock absorption performance testing method for an electric tricycle as claimed in claim 3, characterized in that: Taking the adjustment step lengths of the multiple single-operating-condition variables as constraints, performing redundant test condition verification in the test condition space until the N multi-operating-condition test parameters are output, the method comprising: Selecting H initial working condition particle points in the test working condition space; Taking the adjustment step lengths of the multiple single operating condition variables as constraints, performing multiple rounds of random operating condition updates on the H initial operating condition particle points to obtain H groups of updated operating condition particle points; 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; Performing online data call according to the vehicle model information of the three-wheeled vehicle to be tested and the H group updated multi-dimensional test data to obtain the H group historical shock absorption performance record, wherein the historical shock absorption performance record includes historical vibration amplitude, historical vibration frequency, historical shock absorption response time, historical vibration transmission rate, historical shock absorption offset and historical vibration attenuation rate; The H group of historical shock absorption performance records are redundantly screened using a preset shock absorption performance similarity threshold, and the N multi-condition test parameters are called from the H group of updated multi-dimensional test data according to the screening result.

5. The multi-operating-condition shock absorption performance testing method for an electric tricycle as claimed in claim 4, characterized in that: The H group of historical damping performance records are redundantly screened using a preset damping performance similarity threshold, and the N multi-condition test parameters are called from the H group of updated multi-dimensional test data according to the screening result. The method includes: By deconstructing the H group of historical shock absorption performance records, R historical shock absorption performance records are obtained; The R historical shock absorption performance records are combined and enumerated to obtain Set historical shock absorption performance records; The Euclidean distance is used to calculate the Historical shock absorption performance records Historical performance similarity; The R historical shock absorption performance records are used as R topological nodes, according to the The historical shock absorption performance records are combined to construct topological connections of the R topological nodes to generate a shock absorption performance topology; The shock absorption performance similarity threshold is used to traverse the The topological connection of the damping performance topology is removed based on the historical performance similarity to obtain multiple isolated nodes; According to the mapping relationship between the plurality of isolated nodes and the R historical shock absorption performance records, the H groups of updated multi-dimensional test data are screened for redundancy to obtain the N multi-condition test parameters.

6. The multi-operating-condition shock absorption performance testing method for an electric tricycle according to claim 1, characterized in that: By performing a multivariate regression analysis on the N measured shock absorption performance records and the N multi-operation test parameters, a shock absorption performance prediction network is constructed, and the method includes: Extracting N measured vibration amplitudes from the N measured vibration reduction performance records; Performing a condition multiple regression analysis on the N multi-condition test parameters and the N measured vibration amplitudes to obtain a vibration amplitude prediction function; Similarly, a multivariate regression analysis is performed on the N measured shock absorption performance records and the N multi-operation test parameters to obtain a vibration frequency prediction function, a shock absorption response time prediction function, a vibration transmissibility prediction function, a shock absorption offset prediction function, and a vibration attenuation rate prediction function; Constructing a vibration frequency prediction channel, a shock absorption response time prediction channel, a vibration transmissibility 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 time prediction function, the vibration transmissibility prediction function, the shock absorption offset prediction function and the vibration attenuation rate prediction function; The vibration frequency prediction channel, the shock absorption response time prediction channel, the vibration transmissibility prediction channel, the shock absorption offset prediction channel and the vibration attenuation rate prediction channel are connected in parallel to complete the construction of the shock absorption performance prediction network.

7. The multi-operating-condition shock absorption performance testing method for an electric tricycle as claimed in claim 3, characterized in that: Performing multi-dimensional data visualization on the multi-condition parameter solution set and the damping performance prediction solution set, and outputting a multi-condition damping performance cloud map, the method comprising: Positioning a multi-operating condition particle point set in the test operating condition space according to the multi-operating condition parameter solution set; Interactively obtain shock absorption performance identification rules; Pre-built standard shock absorption performance cloud map; Using the damping performance identification rule and the damping performance prediction solution set, the standard damping performance cloud map is subjected to thermal map visualization and directional identification to obtain a plurality of multi-dimensional performance mapping cloud maps; The multiple multi-dimensional performance mapping cloud graphs are synchronized to the multi-working condition particle point set to complete the construction of the multi-working condition shock absorption performance cloud graph.

8. A multi-condition shock absorption performance test system for electric tricycles, characterized in that: The system is used to implement the multi-operating-condition shock absorption performance testing method for an electric tricycle according to any one of claims 1 to 7, and comprises: A sensitive variable impact test module is used to perform a sensitive variable impact test on the tricycle to be tested, and obtain a plurality of single-operating-condition variable adjustment ranges, wherein the plurality of single-operating-condition variable adjustment ranges have a plurality of single-operating-condition variable adjustment step length identifiers, and the tricycle to be tested is an electric tricycle; A redundancy check module, used for taking the adjustment step lengths of the multiple single-operating-condition variables as test adjustment change constraints, and generating N multi-operating-condition test parameters through redundancy check within the adjustment ranges of the multiple single-operating-condition variables; A real vehicle test module, used to perform a real vehicle test on the three-wheeled vehicle to be tested using the N multi-operating condition test parameters to obtain N actual measured shock absorption performance records; A multiple regression analysis module, used to construct a shock absorption performance prediction network by performing a working condition multiple regression analysis on the N measured shock absorption performance records and N multi-working condition test parameters; A population smoothing expansion module, used for performing population smoothing expansion on the N multi-operating condition test parameters to obtain a multi-operating condition parameter solution set; A damping performance prediction module, used for loading the multi-operating condition parameter solution set into the damping performance prediction network to obtain a damping performance prediction solution set; The multi-dimensional data visualization module is used to perform multi-dimensional data visualization on the multi-operating condition parameter solution set and the shock absorption performance prediction solution set, and output a multi-operating condition shock absorption performance cloud map.

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