Brake detection method of electro-tricycle

Abstract

The invention discloses a brake detection method for an electro-tricycle, and relates to the technical field of brake detection, and the method comprises the steps: taking the type of a to-be-detected electro-tricycle as a constraint, and collecting a historical brake accident data set; acquiring load constraint characteristics of the electro-tricycle to be detected; effective auxiliary data screening is carried out, and an effective braking accident data set is generated; configuring a road environment and a load based on the effective braking accident data set, and establishing an effective test scene; and based on the effective test scene, carrying out a joint test of brake timeliness and brake stability on the to-be-detected electro-tricycle, and generating a brake detection result of the to-be-detected electro-tricycle. The technical problems that in the prior art, the deviation of the evaluation result of the braking performance of the electro-tricycle is large, and the actual working condition is difficult to reflect accurately are solved, and the technical effect of improving the adaptation degree of the evaluation of the braking performance of the electro-tricycle and the actual operation working condition and the result accuracy is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of brake detection, and in particular to a brake detection method for an electric tricycle. Background Art

[0002] In the field of electric tricycle braking performance research, traditional testing methods often lack detailed consideration of vehicle type diversity and actual load conditions. Usually, the collection of test data is relatively general, and no special historical braking accident data is collected for a specific type of electric tricycle. Instead, a broad and general data source is used, which makes the data less consistent with the actual situation of the vehicle to be tested. At the same time, in terms of load, only the approximate load-bearing range of the vehicle is simply considered, and there is no in-depth analysis of the specific constraint characteristics of the front seat passenger weight and cargo compartment loading, such as the impact of different numbers of passengers and weight distribution in the front seat, different types of cargo in the cargo compartment, and changes in the center of gravity position on braking performance. This rough data collection and analysis method makes it impossible to accurately simulate various working conditions of the vehicle in real operation during the braking test, which in turn makes the braking performance evaluation results have large errors and uncertainties, making it difficult to effectively guide the optimization and improvement of the vehicle braking system, and unable to meet the growing driving safety needs of electric tricycles.

[0003] The existing technology has technical problems such as large deviation in the evaluation results of the braking performance of electric tricycles and difficulty in accurately reflecting the actual working conditions. Summary of the invention

[0004] The present application provides a brake detection method for an electric tricycle, which is used to solve the technical problem in the prior art that the evaluation results of the brake performance of the electric tricycle have large deviations and are difficult to accurately reflect the actual working conditions.

[0005] In view of the above problems, the present application provides a brake detection method for an electric tricycle, the method comprising: Taking the type of the electric tricycle to be tested as a constraint, a historical braking accident data set is collected; the load constraint characteristics of the electric tricycle to be tested are obtained, wherein the load constraint characteristics include front seat load constraints and cargo compartment load constraints; based on the front seat load constraints and the cargo compartment load constraints, effective auxiliary data of the historical braking accident data set is screened to generate an effective braking accident data set; based on the effective braking accident data set, road environment and load are configured to establish an effective test scenario; based on the effective test scenario, a joint test of braking timeliness and braking stability is performed on the electric tricycle to be tested to generate a braking test result of the electric tricycle to be tested.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: Taking the type of electric tricycle to be tested as a constraint, collect historical braking accident data sets; obtain the load constraint characteristics of the electric tricycle to be tested; perform effective auxiliary data screening on the historical braking accident data sets to generate effective braking accident data sets; configure the road environment and load to establish an effective test scenario; based on the effective test scenario, perform a joint test of braking timeliness and braking stability on the electric tricycle to be tested to generate the braking test results of the electric tricycle to be tested. The technical effect of improving the adaptability of the electric tricycle braking performance evaluation to the actual operating conditions and the accuracy of the results is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0008] Figure 1 A schematic flow chart of a brake detection method for an electric tricycle provided in an embodiment of the present application.

[0009] Figure 2 A schematic diagram of a flow chart for establishing an effective test scenario in a brake detection method for an electric tricycle provided in an embodiment of the present application. DETAILED DESCRIPTION

[0010] The present application provides a brake detection method for an electric tricycle, which is used to solve the technical problem in the prior art that the evaluation results of the brake performance of the electric tricycle have large deviations and are difficult to accurately reflect the actual working conditions.

[0011] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0012] Examples, such as Figure 1 As shown, the present application provides a brake detection method for an electric tricycle, the method comprising: Step S100: Taking the type of the electric tricycle to be detected as a constraint, a historical braking accident data set is collected.

[0013] Specifically, the specific type of electric tricycle to be tested is clarified, including key information such as the vehicle model, size, design purpose, and specifications of the brake system equipped. This information will serve as an important constraint for collecting historical brake accident data sets. Then, data collection is carried out through various channels, such as cooperating with the traffic management department to obtain detailed information on various types of brake accidents that have occurred in the past for this type of electric tricycle; at the same time, extensively review the relevant insurance company claims data to screen out case information involving brake accidents of this type of vehicle; in addition, in-depth research can be conducted on the maintenance records of vehicle repair shops to collect accident details of this type of vehicle that was repaired in the factory due to brake problems. During the collection process, not only basic information such as the time, location, and weather conditions of the accident should be recorded, but also key data such as the vehicle's driving speed, load, brake operation method, and consequences of the accident should be recorded in detail, so as to build a comprehensive, rich, and historical brake accident data set that is closely related to the type of electric tricycle to be tested, providing a solid data foundation for subsequent detection and analysis.

[0014] Step S200: Obtaining the load constraint characteristics of the electric tricycle to be detected, wherein the load constraint characteristics include front seat load constraints and cargo compartment load constraints.

[0015] Specifically, for the electric tricycle to be tested, obtaining its load constraint characteristics is a key link. For the front seat load constraint, it is necessary to consider in detail the number of people that can be carried and the per capita weight standard specified in the vehicle design, and at the same time, combine the different situations such as full load and partial load that may appear in the actual use scenario to determine the impact range of the front seat on the vehicle's braking performance under different load conditions. This includes analyzing the distribution of front seat passengers, changes in sitting posture, and possible center of gravity shifts. For the cargo compartment load constraint, it is necessary to accurately measure the volume and upper limit of the cargo compartment, and investigate the types of goods, weight distribution patterns, and cargo loading and unloading methods of this type of electric tricycle in common logistics distribution, cargo transportation and other practical applications. For example, whether it is evenly distributed loading or concentrated loading in a certain area, these different load conditions will have a significant impact on the center of gravity, inertia, etc. of the vehicle, and thus affect the braking effect. Through a comprehensive and detailed analysis of the front seat load constraint and the cargo compartment load constraint, it is possible to accurately grasp the trend of changes in the vehicle's braking performance under various load conditions, providing a key basis for subsequent data screening and test scenario construction.

[0016] Step S300: Screening the historical braking accident data set for effective auxiliary data based on the front seat load constraint and the cargo compartment load constraint to generate an effective braking accident data set.

[0017] Specifically, based on the determined front seat load constraints and cargo compartment load constraints, the collected historical braking accident data sets are screened for accurate and effective auxiliary data. First, the front seat passenger load and cargo compartment cargo load details of each data in the historical braking accident data set at the time of the accident are checked one by one, and the front seat load constraint and weight range, cargo compartment load constraint and distribution characteristics are carefully compared. For example, if the front seat load constraint stipulates that it can carry a maximum of two adults, the average weight of each person is calculated as 70 kg, and the cargo compartment load constraint is a maximum load of 500 kg and evenly distributed loading, then the actual accident data that meets this load condition or is close to it is screened. At the same time, factors such as the vehicle's driving speed, road slope, and braking operation details under these load conditions must also be considered to further exclude accident data caused by special situations that are significantly different from conventional usage scenarios. Through this rigorous screening process, data that does not meet the load constraint conditions are eliminated, thereby generating an effective braking accident data set that can truly reflect the braking performance of the electric tricycle to be tested under conventional load conditions, providing highly targeted data support for the subsequent establishment of effective test scenarios.

[0018] Step S400: configuring the road environment and load based on the effective braking accident data set to establish an effective test scenario.

[0019] Specifically, M pieces of braking accident data are extracted from the effective braking accident data set, each of which contains accident road environment characteristics and braking accident characteristics. Then, based on these characteristics, the M pieces of data are clustered and the scene reference value analysis is performed. By identifying the data with a road environment similarity greater than a preset value, N clusters are obtained by clustering. The proportion of data in each cluster in the M pieces of data is calculated to obtain the proportion coefficient. The distribution state of braking accident characteristics is analyzed to establish the accident typicality coefficient, and then the proportion coefficient and the accident typicality coefficient are weighted to obtain the scene value. Then, the effective accident road environment characteristics corresponding to the clusters whose scene value is greater than the preset threshold are screened out. Finally, according to these effective accident road environment characteristics and the front seat load constraints and cargo compartment load constraints taken into account, the road environment (such as slope, road conditions, etc.) and vehicle load (number and weight of front seat passengers, cargo conditions in the cargo compartment) in the test scene are accurately configured to establish an effective test scene.

[0020] Step S500: Based on the effective test scenario, a joint test of braking timeliness and braking stability is performed on the electric tricycle to be tested, and a braking test result of the electric tricycle to be tested is generated.

[0021] Specifically, the electric tricycle to be tested is placed in an effective test scene, and its braking timeliness and braking stability are jointly tested to obtain the braking test results. First, the vehicle is braked after reaching the predetermined test speed, and the braking response data is collected by the sensor module to form a braking test data set. Then the braking distance and braking response time are extracted from the data set to obtain the braking timeliness test results. Then the braking force distribution data of the front and rear wheels and the left and right wheels in the data set are analyzed, and the braking force offset vector is determined by comparing with the balanced braking force distribution characteristics to obtain the braking force balance test results. It is also necessary to extract the body motion posture data, analyze its pitch, roll and lateral motion postures, and obtain the braking stability test results. Finally, the braking timeliness, braking force balance and braking stability test results are combined to generate the braking test results of the electric tricycle to be tested.

[0022] In one possible implementation, Figure 2 As shown, step S400 also includes: Step S410: extracting M pieces of braking accident data from the valid braking accident data set, where M is the total number of accidents corresponding to the valid braking accident data set, wherein any piece of braking accident data includes accident road environment characteristics and braking accident characteristics.

[0023] Step S420: clustering the M pieces of braking accident data and analyzing the scene reference values ​​based on accident road environment characteristics and braking accident characteristics to obtain N clusters and N scene values, where N is a positive integer less than M.

[0024] Step S430: Based on the N clusters and the N scene values, the effective accident road environment features corresponding to the clusters whose scene values ​​are greater than a preset value threshold are screened.

[0025] Step S440: configuring a test scenario based on the effective accident road environment characteristics to establish the effective test scenario.

[0026] Specifically, the effective braking accident data set is mined in depth. The value of M is clearly defined as the total number of accidents covered by the data set, and then M braking accident data are extracted from it. Each braking accident data contains important information, among which the accident road environment characteristics include the slope of the road, such as steep slope, gentle slope or flat ground; road surface smoothness characteristics, such as dry, wet, smooth (possibly due to water accumulation, oil pollution, etc.); and weather characteristics, such as sunny, rainy, snowy, foggy, etc. The braking accident characteristics reflect the severity of the braking problem, including the increase in braking distance and how much it may exceed the normal range; the angle and direction of the side bend of the tricycle, and the speed of the vehicle when the side bend occurs. By extracting these data, a detailed and critical data foundation is provided for subsequent analysis.

[0027] According to the characteristics of the accident road environment, similarity identification is performed on any two of the M data, and the two data with road environment similarity greater than the preset value are classified into one category. In this way, N clusters are gradually formed, where N must be less than M. Next, the proportion of the data in each cluster in the M braking accident data is calculated to obtain N proportion coefficients. At the same time, the distribution state of the braking accident characteristics corresponding to each cluster is deeply analyzed, and N accident typical coefficients are established based on these distribution states. Finally, the N proportion coefficients are weighted with the N accident typical coefficients to obtain N scene values. These scene values ​​and clusters will provide an important basis for the subsequent screening of effective accident road environment characteristics.

[0028] Clearly define the preset value threshold, which is a pre-set standard used to measure the importance and reference value of clusters. Then, check the scene values ​​corresponding to the N clusters one by one. For each cluster, compare its scene value with the preset value threshold. If the scene value of a cluster is greater than the preset value threshold, then the cluster is considered to meet our screening criteria. Next, find out the effective accident road environment features corresponding to these clusters that meet the screening criteria. These features are associated with the clusters in the previous analysis process, and they contain various information about the road environment, such as the slope of the road, road conditions, weather conditions, etc. Through such a screening process, those effective accident road environment features that are most helpful in establishing effective test scenarios are selected from a large number of clusters and related features, thereby improving the effectiveness and pertinence of the test scenarios, and providing a more reliable basis for the subsequent accurate evaluation of the braking performance of electric tricycles.

[0029] The test scenario is carefully configured based on the selected effective accident road environment characteristics to establish an effective test scenario. For the road slope in the road environment characteristics, the corresponding slope changes are set in the test site according to the slope data in the effective accident road environment characteristics, such as simulating steep slopes, gentle slopes and flat ground. For the road condition, according to the road smoothness and related information in the characteristics, the actual road surface is simulated by using special materials or equipment, such as using sprinklers to simulate wet roads, and using special coatings to simulate smooth roads. If weather characteristics are involved, the corresponding weather conditions are created using environmental simulation equipment, such as rain devices simulating rainy days and snowmaking equipment simulating snowy days. At the same time, combined with the load constraint characteristics of the electric tricycle, simulated loads are placed on the front seat and cargo compartment according to the specified load requirements to fully restore the scene when the actual accident occurred. Through such a detailed configuration, an effective test scenario that can accurately simulate the actual braking accident environment is established, providing a reliable environmental foundation for subsequent braking tests.

[0030] In a possible implementation, step S420 further includes: Step S421: performing similarity identification on any two pieces of the M braking accident data based on accident road environment characteristics, clustering any two pieces of data whose road environment similarity is greater than a preset similarity, and generating the N clusters.

[0031] Step S422: Calculate the proportion of the data in the clusters corresponding to the N clusters in the M pieces of braking accident data, and generate N proportion coefficients.

[0032] Step S423: Analyze the distribution states of braking accident characteristics corresponding to the N clusters, and establish N typical accident coefficients.

[0033] Step S424: weighting the N proportion coefficients and the N accident typicality coefficients to generate the N scene values.

[0034] Specifically, the similarity of any two data in the M braking accident data is identified based on the characteristics of the accident road environment. For the road slope information in each data, the ratio of the absolute value of the slope difference of the two data to the average slope can be calculated as the slope similarity index; for the road smoothness, the absolute value of the difference between the corresponding smoothness values ​​of the two data can be calculated by setting the values ​​corresponding to different smoothness levels as the smoothness similarity index; for weather characteristics, if the weather is the same, the similarity is 1, and if it is different, it is 0. Then, these three indicators are weighted and summed according to certain weights (such as the slope similarity weight is 0.4, the smoothness similarity weight is 0.3, and the weather similarity weight is 0.3) to obtain the comprehensive similarity. When the comprehensive similarity is greater than the preset similarity, the two data are clustered, and N clusters are finally generated through pairwise comparison and clustering operations on all data.

[0035] For the N clusters that have been generated, it is necessary to calculate the proportion of the data in each cluster in the M brake accident data. For each cluster, count the number of data contained in it, and then divide this number by the total number of M brake accident data. The result is the corresponding proportion coefficient of the cluster. Through this calculation method, a proportion coefficient is generated for each cluster. These proportion coefficients can intuitively reflect the proportion of each cluster in the entire data set, providing a quantitative basis for further analysis of the importance of the cluster.

[0036] The distribution state of braking accident characteristics corresponding to N clusters is deeply analyzed. For each cluster, the frequency of occurrence of various braking accident characteristics is carefully counted, such as the number of occurrences of excessive braking distance, vehicle lateral bending, braking failure, etc. At the same time, considering the severity of these characteristics when they appear, they can be quantified by setting the values ​​corresponding to different severity levels. Then, based on these frequency and severity information, the characteristic value of each cluster is comprehensively calculated, and then the characteristic value is normalized, and finally N accident typicality coefficients are established. These accident typicality coefficients can accurately reflect the typicality and importance of each cluster in terms of braking accident characteristics.

[0037] In order to generate N scenario values, it is necessary to perform weighted operations on N proportion coefficients and N accident typicality coefficients. First, determine a reasonable weight distribution method. For example, different weight values ​​can be assigned to the proportion coefficient and the accident typicality coefficient according to actual conditions. Let the proportion coefficient weight be W1 and the accident typicality coefficient weight be W2. Then for each cluster, multiply its corresponding proportion coefficient by W1, multiply the accident typicality coefficient by W2, and then add the two products together. The result is the scenario value of the cluster. Through this weighted calculation method, the proportion of the cluster in the data set and the typicality of its braking accident characteristics are comprehensively considered, thereby generating N scenario values ​​that can comprehensively measure the comprehensive value of the cluster.

[0038] In a possible implementation, step S423 further includes: Step S4231: Statistically analyzing the braking accident features corresponding to the N clusters, respectively, to establish N braking accident feature distribution states, wherein any braking accident feature distribution state includes a proportional relationship between different types of braking accident features.

[0039] Step S4232: Obtain a preset mapping relationship, wherein the preset mapping relationship includes typical characteristic values ​​corresponding to various types of braking accident characteristics, and the typical characteristic values ​​are proportional to the severity of the accident.

[0040] Step S4233: Based on the preset mapping relationship, the characteristic value analysis of the N braking accident characteristic distribution states is performed and then normalized to generate the N accident typical coefficients.

[0041] Specifically, for each of the N clusters, statistical analysis of the braking accident characteristics is performed one by one. For each cluster, all the braking accident characteristics contained therein are carefully sorted out, such as different types of characteristics such as excessive braking distance, vehicle lateral bending, braking failure, and abnormal shaking during braking. By counting the frequency of each feature, its proportion in all braking accident characteristics in the cluster is calculated, so as to establish the distribution state of the braking accident characteristics corresponding to the cluster. This distribution state is presented in the proportional relationship of different types of braking accident characteristics, which clearly shows the composition of various types of braking accident characteristics in the cluster, providing an important data basis for subsequent analysis.

[0042] It is necessary to obtain a preset mapping relationship. This preset mapping relationship is a pre-set rule set that covers the correspondence between various types of braking accident characteristics and typical characteristic values. For each braking accident characteristic, there is a corresponding typical characteristic value. Moreover, this correspondence is set according to the severity of the accident, that is, the higher the severity of the accident, the larger the corresponding typical characteristic value. For example, a severely excessive braking distance may correspond to a higher typical characteristic value, while a slight braking tremor may correspond to a lower typical characteristic value. By obtaining such a preset mapping relationship, the necessary standards and basis are provided for the subsequent characteristic value analysis of the distribution state of braking accident characteristics.

[0043] According to the preset mapping relationship, the characteristic value analysis is carried out. For each braking accident characteristic distribution state, different types of braking accident characteristics are considered. For each characteristic, a comprehensive value is calculated according to its proportion in the distribution state and the corresponding typical characteristic value in the preset mapping relationship, that is, the typical characteristic value of each characteristic is multiplied by its proportion in the distribution state, and then all these products are added to obtain the comprehensive characteristic value of the distribution state. Next, normalization is performed. The comprehensive characteristic values ​​calculated from all braking accident characteristic distribution states are put together to form a set. For each of the comprehensive characteristic values, it is divided by the maximum value in this set. After this processing, each comprehensive characteristic value is mapped to a value between 0 and 1, which is the accident typicality coefficient of the corresponding cluster cluster obtained in the end. In this way, an accident typicality coefficient is generated for each cluster cluster, and these coefficients can accurately reflect the typicality and importance of each cluster cluster in terms of braking accident characteristics.

[0044] In a possible implementation, step S500 further includes: Step S510: Based on the effective test scenario, a braking test is performed on the electric tricycle to be tested, and a sensor module is connected to collect braking response data to obtain a braking test data set.

[0045] Step S520: extracting the braking distance and the braking response time based on the braking test data set, and generating a braking effectiveness test result.

[0046] Step S530: performing a braking force balance analysis based on the braking test data set to generate a braking force balance test result.

[0047] Step S540: performing a braking stability analysis based on the braking test data set to generate a braking stability test result.

[0048] Step S550: Generate the braking detection result based on the braking effectiveness test result, the braking force balance test result and the braking stability test result.

[0049] Specifically, we first use an effective test scenario that simulates the characteristics of the actual accident road environment, place the electric tricycle to be tested in it to prepare for the brake test, and connect professional sensor modules distributed in key parts such as wheels, brake systems, and body to accurately sense and record the changes in physical quantities during braking. When the vehicle brakes according to the predetermined process, the sensor module synchronously collects brake response data such as wheel speed changes, brake pedal travel, braking force, and vehicle acceleration in real time. After multiple test runs, these data are summarized and sorted to obtain a brake test data set for subsequent analysis of vehicle braking performance.

[0050] For the extraction of braking distance, determine the data column related to wheel speed in the data set, and find the data of the time period when the wheel speed starts to drop from the stable value before braking to the complete stop. Based on the known parameter of wheel circumference, the product of wheel speed and time interval at each time point is integrated to obtain the distance traveled by the wheel during braking, which is the braking distance. For the extraction of braking response time, the time point when the brake pedal trigger signal is located in the data set, and the time point when the brake system pressure begins to rise significantly or the wheel begins to decelerate significantly, and the two time points are subtracted to obtain the braking response time. After that, the braking distance is compared with the preset standard braking distance range, and the corresponding score is assigned according to the degree of deviation; the braking response time is also taken in a similar way, and the score is given after comparison with the standard response time range. Finally, the two scores are weighted and summed according to a certain weight (such as the braking distance weight is 0.6, and the braking response time weight is 0.4), and the result is the braking time effectiveness test result, which can comprehensively and accurately measure the braking time effectiveness of the vehicle.

[0051] Deeply explore the braking test data set to accurately filter out the braking force data of each wheel during braking. For the analysis of the front and rear wheel braking forces, the average braking forces of the front and rear wheels during braking are calculated in detail, and then the ratio of the average braking forces of the front and rear wheels is calculated. Assuming that the front and rear wheel braking force ratio should be in the reasonable range of 0.7-1.3 when the vehicle brakes normally, if the actual calculated ratio exceeds this range, it means that there is an imbalance in the braking forces of the front and rear wheels. Then analyze the braking forces of the left and right wheels, calculate the difference between the braking forces of the left and right wheels at each sampling moment, and record the maximum value of these differences. If the maximum value of the difference between the left and right wheel braking forces exceeds 10% of the total braking force of the vehicle, it can be determined that the braking forces of the left and right wheels are unbalanced. After completing the separate analysis of the front and rear and left and right wheel braking forces, the results of these two aspects are combined and scored according to the pre-set braking force balance evaluation system. For example, if the front and rear wheel braking force ratio and the left and right wheel braking force difference are both within a reasonable range, full marks are given; if one of them exceeds the range, points are deducted accordingly according to the degree of excess; if neither of the two meets the requirements, a lower score is given. Finally, the braking force balance test result is generated based on the obtained score. This result can intuitively and accurately reflect the uniformity of the braking force when the vehicle brakes, and provide a key basis for evaluating the stability and safety of the vehicle during braking, so as to clearly judge whether the vehicle will face potential risks such as loss of control, deviation or skidding due to uneven braking force.

[0052] The longitudinal and lateral acceleration data of the vehicle during braking recorded by the acceleration sensor, as well as the change data of the vehicle pitch angle and roll angle monitored by the gyroscope, are extracted from the data set. For the longitudinal acceleration, by analyzing its change curve from the beginning to the end of braking, it is judged whether there is a sudden change or abnormal fluctuation. If the acceleration changes smoothly, it indicates good braking stability to a certain extent; on the contrary, if there is a large sudden change, it may indicate that the braking process is unstable. At the same time, pay close attention to the lateral acceleration data. If a large lateral acceleration value appears during braking and exceeds the normal range, it means that the vehicle has a tendency to run or skid during braking, thereby affecting the braking stability. For the changes in the pitch angle and roll angle, observe their change amplitude during the braking process. If the angle changes slightly and can quickly return to a stable state, it means that the vehicle posture is well maintained; on the contrary, if the angle changes too much and lasts for a long time, it reflects that braking has a greater impact on the stability of the vehicle posture. The analysis results of these acceleration and angle changes are combined, and according to the pre-set braking stability evaluation standards, quantitative scores are given according to the degree of deviation of various indicators, and finally the braking stability test results are generated. This can accurately determine the stability performance of the vehicle during braking, providing a key basis for vehicle braking safety evaluation.

[0053] The braking time effectiveness test results, braking force balance test results, and braking stability test results obtained previously are organically integrated to generate a complete braking test result. First, the braking time effectiveness test results are evaluated to see whether the vehicle's braking distance is within a reasonable range and whether the braking response time meets the safety standards. If the braking distance is short and the braking response is quick, a higher evaluation score can be given in this regard; otherwise, points will be deducted according to the degree of deviation from the standard value. Then the braking force balance test results are analyzed and scored according to the balance of the braking force of the front and rear wheels and the left and right wheels. The more balanced the braking force, the higher the score; if there is an obvious imbalance, the score is reduced because it will seriously affect the stability and safety of the vehicle during braking. Then consider the braking stability test results and quantify the score according to the stability of the vehicle's pitch, roll, and lateral movement during braking. The better the stability performance, the higher the score. Finally, according to the predetermined weight distribution scheme, the scores of the three test results are weighted and summed to obtain a comprehensive braking test result. This result can comprehensively and accurately reflect the braking performance of the electric tricycle to be tested, provide a key basis for the vehicle's safety assessment, and provide a clear direction for possible subsequent improvement measures.

[0054] In a possible implementation, step S530 further includes: Step S531: extracting the braking force distribution data of the front and rear wheels and the left and right wheels based on the braking test data set.

[0055] Step S532: Obtain the balanced braking force distribution characteristics of the front and rear wheels and the left and right wheels in the braking balance state.

[0056] Step S533: Compare the braking force distribution data with the balanced braking force distribution characteristics, determine the braking force offset vector, and generate the braking force balance test result.

[0057] Specifically, the braking test data set is comprehensively and carefully combed, and data records related to the braking force of the front and rear wheels are found from the data set. These records are collected from the pressure sensors, force sensors and other equipment installed in the braking system. For the front wheel braking force data, by identifying the data tags, the braking force values ​​generated by the front wheel at different times during the braking process are screened out, and arranged in chronological order to form the distribution data of the front wheel braking force over time. Similarly, for the rear wheel braking force, a similar method is used to accurately extract the braking force values ​​of the rear wheel at each time point from a large number of data, and then obtain the distribution of the rear wheel braking force. When extracting the left and right wheel braking force distribution data, according to the independent sensor data of the left and right wheels, such as the torque data when the wheel is braked, the friction data between the brake pad and the brake disc, etc., according to the same time series extraction principle, the values ​​of the left and right wheel braking force are sorted out separately, so as to completely obtain the distribution data of the left and right wheel braking force in the entire braking process, providing an accurate and detailed data basis for the subsequent evaluation of the balance of the braking force.

[0058] By consulting relevant vehicle braking technical standards, theoretical models, and referring to a large amount of experimental data of the same type of vehicles under ideal braking balance, the balanced braking force distribution characteristics of the front and rear wheels and the left and right wheels under the braking balance state are obtained. These characteristics cover the reasonable proportional relationship of the braking force of the front and rear wheels and the balanced requirements of the braking force of the left and right wheels, which are important bases for judging whether the current vehicle braking force is balanced.

[0059] The braking force distribution data of the front and rear wheels and the left and right wheels extracted from the braking test data set are carefully compared with the balanced braking force distribution characteristics under the previously obtained braking balance state. For the front and rear wheels, the difference between the actual braking force distribution data and the theoretical balanced braking force distribution characteristics in terms of braking force size and action time is calculated, and the degree of braking force offset in the front and rear directions is obtained by difference calculation. For example, if the actual braking force of the front wheel is larger than the theoretical value at a certain moment, while the rear wheel is smaller, there will be an imbalance in the braking force in the front and rear directions, and the braking force offset vector component in the front and rear directions is determined according to the size and direction of this imbalance. Similarly, for the left and right wheels, the braking force distribution data and balance characteristics are compared, and the difference in the braking force of the left and right wheels and the time stage when the difference occurs are considered to determine the degree of braking force offset in the left and right directions, and then the braking force offset vector component in the left and right directions is obtained. The braking force offset vector components in the front and rear and left and right directions are synthesized to obtain a complete braking force offset vector, which fully reflects the imbalance of the braking force. Finally, based on the pre-set braking force balance evaluation standard, combined with the size and direction of the braking force offset vector, the vehicle's braking force balance degree is quantified and scored, and the braking force balance test results are generated. This clearly presents the balanced performance of the braking force when the vehicle brakes, providing a key basis for the stability and safety evaluation of the vehicle's braking system.

[0060] In a possible implementation, step S540 further includes: Step S541: extracting vehicle body motion posture data based on the braking test data set, wherein the vehicle body motion posture data at least includes the pitch, roll and lateral motion postures of the electric tricycle to be tested during the braking process.

[0061] Step S542: Performing posture stability analysis based on the vehicle body motion posture data to generate the braking stability test result.

[0062] Specifically, the long short-term memory network (LSTM) machine learning algorithm is used to extract the body motion posture data. First, the braking test data set is preprocessed, and the raw data collected from the accelerometer, gyroscope, and wheel speed sensor are normalized so that their value range is within a reasonable range, which is convenient for the training and learning of the LSTM model. Then the LSTM model architecture is constructed, which includes an input layer, a hidden layer, and an output layer. The input layer receives the preprocessed sensor data sequence, which contains various dynamic information of the vehicle during the braking process, such as acceleration values, angular velocity values, and wheel speed values ​​at different times, and is input into the model in sequence according to the time step. The hidden layer consists of multiple LSTM units, which can effectively capture the time series characteristics and long-term dependencies in the data, and use the internal gating mechanism to determine which information needs to be retained, forgotten, or updated, thereby modeling the vehicle's motion state. In the model training phase, a large number of vehicle braking data samples with known motion postures are used to train the LSTM model. The weights and bias parameters of the model are continuously adjusted through the back-propagation algorithm, so that the output of the model can predict the pitch, roll and lateral motion posture of the vehicle as accurately as possible. For example, for the learning of pitch motion posture, the model gradually grasps the law of pitch angle change by analyzing the time series characteristics of the front and rear axle acceleration changes and vehicle height related data in the input data; for rolling motion posture, the rotation of the vehicle body around the longitudinal axis is learned based on the angular velocity data sequence of the gyroscope; for lateral motion posture, the lateral movement trend of the vehicle is understood by combining the time series of data such as lateral acceleration and wheel speed difference. When the model training is completed, the braking test data of the electric tricycle to be tested is input into the trained LSTM model. The model can output the corresponding body motion posture data according to the learned features and patterns, including precise pitch angle values, roll angle values, and lateral displacement, speed, acceleration and other parameters, thereby completely extracting the vehicle's body motion posture data during braking, providing a comprehensive and accurate data basis for subsequent braking stability analysis, effectively improving the accuracy and efficiency of vehicle motion posture data extraction, and better adapting to complex and changeable actual working conditions compared to traditional methods.

[0063] The pitch attitude stability is analyzed, and the pitch angle change curve of the vehicle during braking is obtained based on the extracted body motion attitude data. The rate of change of the pitch angle is calculated by taking the first-order derivative of the pitch angle with time. If the peak value of the rate of change exceeds the preset safety threshold, it indicates that the vehicle has excessive nodding during braking, which will affect the vehicle's handling and stability, because excessive pitching movement will cause a large change in the load on the front and rear axles of the vehicle, resulting in uneven tire adhesion, which in turn increases the risk of loss of control. At the same time, observe the recovery of the pitch angle after braking. If it cannot quickly recover to a level close to the initial level, it also indicates that there is a problem with the adjustment of the vehicle's suspension system or braking system, which affects the braking stability. The pitch attitude stability is scored and evaluated accordingly. Then analyze the rolling attitude stability and check the rolling angle change data of the vehicle. The maximum value of the rolling angle and its duration are counted. If the maximum rolling angle exceeds the safety range allowed by the vehicle design, or the rolling angle is maintained at a large value for a long time, it means that the vehicle may face the risk of rollover during braking, reflecting the imbalance of the braking force of the left and right wheels or the insufficient ability of the suspension system to resist rollover, which will also have a serious negative impact on the braking stability of the vehicle. According to these situations, the rolling posture stability is given a corresponding score. For the lateral motion posture stability, the focus is on the lateral displacement and lateral acceleration data of the vehicle, and the standard deviation of the lateral displacement is calculated to measure the discrete degree of the lateral swing of the vehicle during braking. The larger the standard deviation, the more unstable the vehicle's driving trajectory. At the same time, check the peak value of the lateral acceleration. If it exceeds a certain limit, it may mean that the vehicle is disturbed by a large lateral force, or the wheel is in an unstable state of alternating locking and sliding. These will reduce the braking stability of the vehicle, and quantitative scoring is performed based on the performance of various indicators of the lateral motion posture. Finally, the scoring results of pitch, roll and lateral motion posture stability are integrated and weighted summed according to the predetermined weight distribution scheme to generate the braking stability test result. This result can accurately reflect the overall stability performance of the vehicle during braking, provide a key basis for evaluating the braking safety of the vehicle, and help determine whether the vehicle can maintain a stable driving posture under braking operations to avoid dangerous conditions such as skidding and rollover.

[0064] In a possible implementation, step S300 further includes: Step S310: extracting historical load record data corresponding to any piece of historical braking accident data from the historical braking accident data set.

[0065] Step S320: Based on the historical load record data, historical braking accident data whose load characteristics satisfy the front seat load constraint and the cargo compartment load constraint are screened in the historical braking accident data set to generate the effective braking accident data set.

[0066] Specifically, the data set that specifically stores the historical braking accident data of electric tricycles is processed. The data set is stored in a database or data storage system using a specific data format and structure. Its data sources cover multiple channels, such as accident records of traffic management departments, fault reports of vehicle repair shops, and claims data of relevant insurance institutions, etc., to ensure the comprehensiveness and diversity of the data. With the help of professional data extraction tools and technologies, the target historical braking accident data is quickly located through the data indexing mechanism. These data are presented in a structured form. For example, each data contains multiple fields such as the time and location of the accident, the vehicle driving status, the braking system parameters, and the load information unique to the electric tricycle. Then, the load-related record part, that is, the historical load record data, is accurately separated from the historical braking accident data. For electric tricycles, the historical load record data includes the weight information of the passengers on the front seat (recorded or estimated by the seat pressure sensor) and the detailed data such as the weight, volume and center of gravity position of the cargo in the cargo compartment (obtained by the cargo compartment load sensor or the estimation model based on the cargo type and loading method). During the extraction process, data cleaning and preprocessing techniques are used to handle possible data missing, errors or outliers. For example, if the front seat load data is missing, it can be reasonably filled in by referring to the average front seat load data of the same type of electric tricycles in similar accident scenarios; if the cargo compartment load data is obviously abnormal, such as exceeding the design load limit of the vehicle model, it is verified and corrected in combination with the cargo list, transportation records and actual use of the vehicle to ensure that the extracted historical load record data is accurate, providing a solid and reliable foundation for the subsequent screening of effective braking accident data based on load characteristics, so as to support the accurate evaluation and analysis of the braking performance of electric tricycles and ensure their safety and stability in actual operation.

[0067] Based on the historical load record data obtained, the specific range of the front seat load constraint and cargo compartment load constraint of the electric tricycle is clarified. The front seat load constraint is usually determined based on the vehicle's design load capacity, safety standards and ergonomic principles. For example, by testing and analyzing the seat structure strength and vehicle handling stability under different passenger weights, a reasonable upper and lower limit range of the front seat passenger weight is set; the cargo compartment load constraint comprehensively considers factors such as the vehicle's frame strength, suspension system load capacity, and dynamic performance under cargo conditions to determine the maximum safe weight of the cargo compartment, the center of gravity position limit, and the reasonable range of cargo distribution. Then, the entire historical braking accident data set is traversed, and the load characteristics in each data are compared with the above constraints one by one. For the front seat load, check whether its recorded value is within the preset front seat load constraint range; for the cargo compartment load, it is necessary not only to determine whether the cargo weight meets the cargo compartment load constraint, but also to evaluate whether the center of gravity position of the cargo and the uniformity of the cargo distribution meet the requirements. Through this meticulous screening process, the historical braking accident data whose load characteristics fully meet the front seat load constraints and cargo compartment load constraints are selected and integrated into a new data set, namely the effective braking accident data set. This data set excludes invalid data that may cause deviations in braking accident results due to abnormal loads, making the subsequent braking performance analysis based on these data more scientific and accurate, and can more realistically reflect the braking conditions of electric tricycles under normal load conditions, providing a reliable basis for evaluating and improving the braking system of electric tricycles, thereby effectively improving the braking safety and stability of vehicles in actual operation and reducing the risk of accidents caused by braking failures.

[0068] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0069] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0070] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A brake detection method for an electric tricycle, characterized in that: include: Taking the type of electric tricycle to be detected as a constraint, a historical braking accident dataset is collected; Acquire the load constraint characteristics of the electric tricycle to be detected, wherein the load constraint characteristics include front seat load constraint and cargo compartment load constraint; Performing effective auxiliary data screening on the historical braking accident data set based on the front seat load constraint and the cargo compartment load constraint to generate an effective braking accident data set; configuring the road environment and load based on the effective braking accident data set to establish an effective test scenario; Based on the effective test scenario, a joint test of braking timeliness and braking stability is performed on the electric tricycle to be tested, and a braking test result of the electric tricycle to be tested is generated.

2. The brake detection method for an electric tricycle as claimed in claim 1, characterized in that: Based on the effective braking accident data set, the road environment and load are configured to establish an effective test scenario, including: Extracting M pieces of braking accident data from the valid braking accident data set, where M is the total number of accidents corresponding to the valid braking accident data set, wherein any piece of braking accident data includes accident road environment characteristics and braking accident characteristics; Based on the accident road environment characteristics and the braking accident characteristics, the M braking accident data are clustered and the scene reference value is analyzed to obtain N clusters and N scene values, where N is a positive integer less than M; Based on the N clusters and the N scene values, screening effective accident road environment features corresponding to clusters whose scene values ​​are greater than a preset value threshold; The test scenario is configured based on the effective accident road environment characteristics to establish the effective test scenario.

3. A brake detection method for an electric tricycle as claimed in claim 2, characterized in that: Based on the accident road environment characteristics and the braking accident characteristics, the M braking accident data are clustered and the scene reference value is analyzed to obtain N clusters and N scene values, including: Based on the accident road environment characteristics, similarity identification is performed on any two pieces of the M brake accident data, and any two pieces of data whose road environment similarity is greater than a preset similarity are clustered to generate the N clusters; Calculating the proportion of the data in the clusters respectively corresponding to the N clusters in the M pieces of braking accident data, and generating N proportion coefficients; Analyze the distribution states of braking accident characteristics corresponding to the N clusters, and establish N typical accident coefficients; The N proportion coefficients and the N typical accident coefficients are weighted to generate the N scene values.

4. A brake detection method for an electric tricycle as claimed in claim 3, characterized in that: Analyze the distribution states of braking accident characteristics corresponding to the N clusters, and establish N typical accident coefficients, including: Performing statistical analysis on the braking accident features corresponding to the N clusters respectively, and establishing N braking accident feature distribution states, wherein any braking accident feature distribution state includes a proportional relationship of different types of braking accident features; Acquire a preset mapping relationship, wherein the preset mapping relationship includes typical characteristic values ​​corresponding to various types of braking accident characteristics, and the typical characteristic values ​​are proportional to the severity of the accident; Based on the preset mapping relationship, characteristic value analysis is performed on the N braking accident characteristic distribution states and then normalized to generate the N accident typical coefficients.

5. The brake detection method for an electric tricycle as claimed in claim 1, characterized in that: Based on the effective test scenario, a joint test of braking timeliness and braking stability is performed on the electric tricycle to be tested, and a braking test result of the electric tricycle to be tested is generated, including: Based on the effective test scenario, a braking test is performed on the electric tricycle to be tested, and a sensor module is connected to collect braking response data to obtain a braking test data set; Extracting braking distance and braking response time based on the braking test data set to generate a braking timeliness test result; Performing a braking force balance analysis based on the braking test data set to generate a braking force balance test result; Performing a braking stability analysis based on the braking test data set to generate a braking stability test result; The braking detection result is generated based on the braking effectiveness test result, the braking force balance test result and the braking stability test result.

6. A brake detection method for an electric tricycle as claimed in claim 5, characterized in that: Performing a braking force balance analysis based on the braking test data set to generate a braking force balance test result includes: Extracting braking force distribution data of front and rear wheels and left and right wheels based on the braking test data set; Obtaining the balanced braking force distribution characteristics of the front and rear wheels and the left and right wheels under the braking balance state; The braking force distribution data is compared with the balanced braking force distribution characteristics to determine the braking force offset vector and generate the braking force balance test result.

7. A brake detection method for an electric tricycle as claimed in claim 5, characterized in that: Performing a braking stability analysis based on the braking test data set to generate a braking stability test result includes: Extracting body motion posture data based on the braking test data set, wherein the body motion posture data at least includes the pitch, roll and lateral motion postures of the electric tricycle to be tested during the braking process; Performing posture stability analysis based on the vehicle body motion posture data to generate the braking stability test result.

8. The brake detection method for an electric tricycle as claimed in claim 1, characterized in that: The effective auxiliary data of the historical braking accident data set is screened based on the front seat load constraint and the cargo compartment load constraint to generate an effective braking accident data set, including: Extracting historical load record data corresponding to any piece of historical braking accident data from the historical braking accident data set; Based on the historical load record data, the historical braking accident data whose load characteristics satisfy the front seat load constraint and the cargo compartment load constraint are screened in the historical braking accident data set to generate the effective braking accident data set.

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