Dynamic balance detection method and system of electro-tricycle body structure

Abstract

The invention provides a dynamic balance detection method and system for an electro-tricycle body structure, and relates to the technical field of dynamic balance testing, and the method comprises the steps: determining initial standard detection parameter distribution according to navigation route matching; correcting the initial standard detection parameter distribution according to the real-time load and the real-time vehicle speed; performing mapping judgment on balance sensing parameter distribution according to adaptive standard detection parameter distribution, and if parameter abnormity exists, performing continuous monitoring to obtain an abnormal feature distribution sequence; and carrying out similarity comparison on the driving behavior abnormal characteristics and the abnormal characteristic distribution sequence, and if a similarity coefficient is smaller than a similarity threshold value, carrying out vehicle imbalance early warning. According to the method, the problem that the timeliness and the accuracy of vehicle unbalance early warning are insufficient due to the phenomena that a traditional detection method cannot effectively adapt to a complex driving scene, response is slow, false alarm is frequent and the like can be solved; accurate unbalance monitoring can be realized in a complex driving environment, and the adaptability, timeliness and accuracy of unbalance early warning are remarkably improved.

Description

Technical Field

[0001] The present application relates to the technical field of dynamic balance testing, and in particular to a method and system for dynamic balance detection of an electric tricycle body structure. Background Art

[0002] Traditional electric tricycle dynamic balance detection methods usually set detection parameters based on the vehicle's driving behavior under standard conditions, such as flat road surface, uniform load, stable speed, etc. However, in actual driving, factors such as road type, slope changes, turning, acceleration and deceleration may greatly affect the balance of the vehicle. Traditional methods cannot effectively identify and adapt to these complex driving scenarios, resulting in inaccurate balance detection results, especially in complex environments such as ramps, slippery roads or sharp turns. It is difficult to adjust the detection standards in real time, which can easily lead to balance misjudgment.

[0003] On the other hand, some drivers may make sudden braking, sudden acceleration or quick turns during their normal driving behavior, which may cause large fluctuations in the dynamic characteristics of the vehicle in an instant. However, due to the lack of comprehensive judgment on the driver's driving habits, environmental factors, etc., the traditional balance detection system can easily misjudge these instantaneous changes as vehicle imbalance, thereby triggering false alarms.

[0004] In summary, the traditional dynamic balance detection method for tricycles cannot effectively adapt to complex driving scenarios, and has problems such as slow response and frequent false alarms, resulting in insufficient timeliness and accuracy of vehicle imbalance warning, affecting vehicle driving safety and stability. Summary of the invention

[0005] The purpose of this application is to provide a dynamic balance detection method and system for the body structure of an electric tricycle, so as to solve the technical problems that the traditional dynamic balance detection method of tricycle cannot effectively adapt to complex driving scenarios, has phenomena such as slow response and frequent false alarms, resulting in insufficient timeliness and accuracy of vehicle imbalance warning.

[0006] In view of the above problems, the present application provides a method and system for dynamic balance detection of the body structure of an electric tricycle.

[0007] In a first aspect, the present application provides a method for dynamic balance detection of an electric tricycle body structure, which is implemented by a dynamic balance detection system for an electric tricycle body structure, including: dividing and determining a plurality of road scenes with road type, road slope and turning angle as change factors, and performing dynamic balance detection standard analysis according to a reference load and reference speed of the tricycle under the plurality of road scenes, generating a plurality of standard detection parameter distributions, and mapping and constructing a scene parameter reference library; during the driving process of the tricycle, determining an initial standard detection parameter distribution according to a navigation route and matching the scene parameter reference library; monitoring and obtaining a real-time load and a real-time speed of the tricycle, mapping and correcting the initial standard detection parameter distribution according to the real-time load and the real-time speed, and obtaining an adapted standard detection parameter distribution; monitoring and obtaining a balance sensor parameter distribution of the tricycle according to a first frequency, mapping and judging the balance sensor parameter distribution according to the adapted standard detection parameter distribution, and if there is a parameter abnormality, continuously monitoring according to a second frequency to obtain an abnormal feature distribution sequence; performing a similarity comparison between the matched driving behavior abnormal feature and the abnormal feature distribution sequence, and if the comparison similarity coefficient is less than a similarity threshold, a vehicle imbalance warning is issued.

[0008] Optionally, the dynamic balance detection method for the body structure of an electric tricycle further includes: dividing the step size according to the road slope and the turning angle, and determining a plurality of road slope intervals and a plurality of turning angle intervals; randomly combining a predetermined road type and the plurality of road slope intervals and the plurality of turning angle intervals to generate a plurality of road scenes; randomly selecting a first road scene from the plurality of road scenes, performing a dynamic balance detection standard analysis according to a benchmark load and a benchmark speed of the tricycle, generating a first standard detection parameter distribution, and adding the first standard detection parameter distribution to the plurality of standard detection parameter distributions; and constructing the scene parameter reference library according to the mapping of the plurality of road scenes and the plurality of standard detection parameter distributions.

[0009] Optionally, the method for dynamic balance detection of the body structure of an electric tricycle further includes: obtaining a sensor layout array of the tricycle for dynamic balance detection, wherein the sensor layout array includes a plurality of layout positions, each layout position includes at least one sensor, and the sensor includes at least an acceleration sensor, a gyroscope sensor and a tilt sensor; constructing a dynamic balance test space by simulating the body structure, driving characteristics and the sensor layout array of the tricycle, and configuring parameters according to the first road scene, benchmark load and benchmark vehicle speed to generate a first dynamic balance test space; performing an iterative simulation test of the vehicle dynamic balance in the first dynamic balance test space, setting the maximum detection parameter when it is less than a preset vehicle imbalance ratio as a first standard detection parameter, and generating the first standard detection parameter distribution.

[0010] Optionally, the dynamic balance detection method for the body structure of an electric tricycle also includes: taking the tricycle body structure and sensor layout array as constraints, querying the networked driving logs of similar tricycles, collecting sample standard detection parameter distribution sets, sample vehicle load sets, sample vehicle speed sets and sample correction detection parameter distribution sets; constructing a detection parameter correction model based on a generative adversarial network, the detection parameter correction model including a detection parameter generator and a detection parameter discriminator; iteratively training the detection parameter generator and the detection parameter discriminator with the sample standard detection parameter distribution set, sample vehicle load set, sample vehicle speed set and sample correction detection parameter distribution set until the generation loss function and the discrimination loss function converge, and obtaining a trained detection parameter correction model; inputting the real-time load, real-time vehicle speed and initial standard detection parameter distribution into the detection parameter correction model for mapping correction, and outputting the adapted standard detection parameter distribution.

[0011] Optionally, the dynamic balance detection method for the body structure of an electric tricycle also includes: configuring a first frequency and a second frequency, collecting data on the sensor array according to the first frequency, and obtaining a balance sensor parameter distribution, wherein the second frequency is greater than the first frequency; mapping and judging the balance sensor parameter distribution according to the adaptation standard detection parameter distribution, if the sensor parameter does not meet the corresponding standard detection parameter, generating a parameter abnormality signal, and collecting data on the sensor array according to the second frequency, constructing an abnormal feature distribution, and obtaining an abnormal feature distribution sequence within a preset time zone.

[0012] Optionally, the dynamic balance detection method for the body structure of an electric tricycle also includes: monitoring and acquiring the navigation route and driving behavior data of the vehicle in the preset time zone, and performing driving feature analysis based on the navigation route and driving behavior data to construct a driving feature portrait; based on the second frequency, performing abnormal feature simulation analysis according to the navigation route and driving feature portrait to generate a simulated abnormal feature distribution sequence; performing similarity mapping comparison on the simulated abnormal feature distribution sequence and the abnormal feature distribution sequence, outputting multiple similarity coefficients, and obtaining the comparison similarity coefficient after average calculation.

[0013] Optionally, the dynamic balance detection method for the body structure of an electric tricycle also includes: dividing the navigation route based on the second frequency to determine multiple route segments; taking the tricycle body structure and sensor layout array as constraints, and according to historical networked driving records, collecting a sample route set and a sample driving feature portrait set, and marking the abnormal feature distributions under different sample routes and sample driving feature portraits to obtain a sample abnormal feature distribution set; using the sample route set, sample driving feature portrait set and sample abnormal feature distribution set to iteratively train the generative adversarial network until convergence, and obtaining an abnormal feature simulation plug-in; using the abnormal feature simulation plug-in to perform abnormal feature simulation analysis based on the multiple route segments and driving feature portraits, outputting multiple simulated abnormal feature distributions, and constructing the simulated abnormal feature distribution sequence.

[0014] In a second aspect, the present application further provides a dynamic balance detection system for an electric tricycle body structure, which is used to execute a dynamic balance detection method for an electric tricycle body structure as described in the first aspect, including: a dynamic balance detection standard analysis module, which is used to divide and determine multiple road scenes based on road type, road slope and turning angle as change factors, and perform dynamic balance detection standard analysis according to the benchmark load and benchmark speed of the tricycle under the multiple road scenes, generate multiple standard detection parameter distributions, and map and construct a scene parameter comparison library; an initial standard detection parameter distribution determination module, which is used to determine the initial standard detection parameter distribution according to the navigation route and the scene parameter comparison library during the driving of the tricycle; the initial standard A detection parameter distribution correction module is used to monitor and obtain the real-time load and real-time speed of the tricycle, and map and correct the initial standard detection parameter distribution according to the real-time load and real-time speed to obtain the adapted standard detection parameter distribution; an abnormal feature distribution sequence acquisition module is used to monitor and obtain the balance sensor parameter distribution of the tricycle according to the first frequency, and map and judge the balance sensor parameter distribution according to the adapted standard detection parameter distribution. If there is a parameter abnormality, continuous monitoring is performed according to the second frequency to obtain the abnormal feature distribution sequence; a vehicle imbalance warning module is used to perform a similarity comparison between the matched driving behavior abnormal features and the abnormal feature distribution sequence, and if the comparison similarity coefficient is less than the similarity threshold, a vehicle imbalance warning is issued.

[0015] One or more technical solutions provided in this application have at least the following technical effects or advantages: By taking the road type, road slope and turning angle as the variation factors, a plurality of road scenes are divided and determined, and in the plurality of road scenes, a dynamic balance detection standard analysis is performed according to the benchmark load and benchmark speed of the tricycle, a plurality of standard detection parameter distributions are generated, and a scene parameter reference library is mapped and constructed; then, during the driving process of the tricycle, an initial standard detection parameter distribution is determined by matching the navigation route with the scene parameter reference library; further, the real-time load and real-time speed of the tricycle are monitored and obtained, and the initial standard detection parameter distribution is mapped and corrected according to the real-time load and real-time speed to obtain an adapted standard detection parameter distribution; and then the balance of the tricycle is monitored and obtained according to the first frequency. Sensing parameter distribution, mapping and judging the balance sensing parameter distribution according to the adaptation standard detection parameter distribution, if there is a parameter abnormality, continuous monitoring is performed according to the second frequency to obtain the abnormal feature distribution sequence; finally, a similarity comparison is performed on the matched driving behavior abnormal characteristics and the abnormal feature distribution sequence, and if the comparison similarity coefficient is less than the similarity threshold, a vehicle imbalance warning is issued; that is to say, by introducing technical means such as dynamic adjustment of detection parameters, multi-frequency monitoring and driving feature similarity comparison, accurate imbalance monitoring can be achieved in complex driving environments, significantly improving the adaptability, timeliness and accuracy of vehicle imbalance warnings, thereby effectively improving the safety and driving stability of electric tricycles.

[0016] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and 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 specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0018] Figure 1 A schematic flow chart of a method for dynamic balance detection of an electric tricycle body structure according to the present application.

[0019] Figure 2 A schematic diagram of the process of constructing a scene parameter reference library in a dynamic balance detection method for an electric tricycle body structure in the present application.

[0020] Figure 3This is a structural schematic diagram of a dynamic balance detection system for an electric tricycle body structure according to the present application.

[0021] Description of reference numerals: Dynamic balance detection standard analysis module 11, initial standard detection parameter distribution determination module 12, initial standard detection parameter distribution correction module 13, abnormal feature distribution sequence acquisition module 14, vehicle imbalance warning module 15. DETAILED DESCRIPTION

[0022] This application provides a dynamic balance detection method and system for the body structure of an electric tricycle, which solves the technical problem that the traditional dynamic balance detection method for tricycles cannot effectively adapt to complex driving scenarios, has slow response, frequent false alarms, etc., resulting in insufficient timeliness and accuracy of vehicle imbalance warning. By introducing technical means such as dynamic adjustment of detection parameters, multi-frequency monitoring and driving characteristic similarity comparison, accurate imbalance monitoring can be achieved in complex driving environments, significantly improving the adaptability, timeliness and accuracy of vehicle imbalance warnings, thereby effectively improving the safety and driving stability of electric tricycles.

[0023] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0024] For example, please refer to the attached Figure 1 The present application provides a method for dynamic balance detection of an electric tricycle body structure, which is applied to a dynamic balance detection system for an electric tricycle body structure, and specifically comprises the following steps: S100: Taking road type, road slope and turning angle as variation factors, a plurality of road scenes are divided and determined, and in the plurality of road scenes, a dynamic balance detection standard analysis is performed according to a reference load and a reference speed of the tricycle, a plurality of standard detection parameter distributions are generated, and a scene parameter reference library is mapped and constructed.

[0025] Further, if Figure 2 As shown, step S100 of the present application also includes: S110: dividing the step length according to the road surface slope and the turning angle, dividing and determining a plurality of road surface slope intervals and a plurality of turning angle intervals; S120: randomly combining a predetermined road surface type and the plurality of road surface slope intervals and the plurality of turning angle intervals to generate a plurality of road surface scenes.

[0026] Specifically, the road slope is one of the important factors that affect the balance of tricycles. The change of slope will directly affect the center of gravity distribution and driving stability of the vehicle body. In order to adapt to driving conditions with different slopes, the road slope is first divided into multiple intervals according to the road slope division step length (such as setting the division step length to 5 degrees). The slope value difference of each interval maintains a certain step length, and each interval corresponds to a different detection standard. For example, the slope interval is divided as follows: interval 1, 0 degrees greater than or equal to the slope less than 5 degrees (flat road); interval 2, 5 degrees greater than or equal to the slope less than 10 degrees (slight uphill / downhill); interval 3, 10 degrees greater than or equal to the slope less than 15 degrees (medium slope). This division method can identify the dynamic balance characteristics of the vehicle under different slope conditions and provide accurate parameter settings for subsequent balance detection. Turning is one of the important factors affecting the balance of a tricycle, especially when making a sharp turn. The size of the turning angle directly affects the lateral force of the vehicle body. Therefore, it is necessary to divide different intervals according to the changes in different turning angles, and set corresponding detection standards for each interval; then divide the turning angle into multiple intervals according to the turning angle division step size (such as 15 degrees), for example, interval 1, 0 degrees is greater than or equal to the turning angle is less than 15 degrees (small turn); interval 2, 15 degrees is greater than or equal to the turning angle is less than 30 degrees (medium turn); interval 3, 30 degrees is greater than or equal to the turning angle is less than 45 degrees (large turn); obtain multiple road slope intervals and multiple turning angle intervals.

[0027] Then, a predetermined road surface type is obtained, such as asphalt road, gravel road, muddy road, concrete road, etc., which can be set according to the actual driving scene. Different road surface types will affect the friction between the wheels and the ground, thereby affecting the balance stability of the vehicle; then the predetermined road surface type and the multiple road surface slope intervals and the multiple turning angle intervals are randomly combined, as shown in Table 1, to generate multiple road surface scenes.

[0028]

[0029] Table 1 S130: Randomly select a first road scene from the multiple road scenes, perform a dynamic balance detection standard analysis based on a reference load and a reference speed of the tricycle, generate a first standard detection parameter distribution, and add the first standard detection parameter distribution to the multiple standard detection parameter distributions.

[0030] Furthermore, step S130 of the present application also includes: S131: Obtain a sensor array for dynamic balance detection of a tricycle, wherein the sensor array includes a plurality of locations, each location includes at least one sensor, and the sensors include at least an acceleration sensor, a gyroscope sensor and a tilt sensor; S132: Construct a dynamic balance test space by simulating the body structure, driving characteristics and the sensor array of the tricycle, and configure parameters according to the first road scene, benchmark load and benchmark vehicle speed to generate a first dynamic balance test space; S133: Perform an iterative simulation test of vehicle dynamic balance in the first dynamic balance test space, set the maximum detection parameter when it is less than a preset vehicle imbalance ratio as a first standard detection parameter, and generate the first standard detection parameter distribution.

[0031] Specifically, any one road surface scene is randomly selected from the plurality of road surface scenes and set as the first road surface scene.

[0032] A sensor array for dynamic balance detection of a tricycle is obtained, wherein the sensor array includes several locations, each of which includes at least one sensor (the same location may include multiple different types of sensors), and the sensors include at least an acceleration sensor, a gyroscope sensor, and a tilt sensor. The acceleration sensor is used to measure the acceleration change of the tricycle in three axes (X axis, Y axis, and Z axis), and the acceleration sensor can help the system determine the motion state and acceleration / deceleration of the vehicle; the gyroscope sensor is used to measure the rotation angular velocity of the tricycle, and can provide the rotation information of the vehicle body (such as the rotation change when turning); the tilt sensor is used to detect the tilt angle of the vehicle body, and help the system determine whether the tricycle is in a balanced state, especially when going uphill, downhill or turning; wherein, as shown in Table 2, the sensors should be arranged at key positions of the vehicle according to the structure of the tricycle to ensure that the dynamic balance information of the vehicle under various driving conditions can be accurately captured. By arranging acceleration sensors, gyroscope sensors, and tilt sensors at key positions of the tricycle, the balance state of the vehicle can be monitored in real time, and dynamic balance data can be collected, which provide support for subsequent dynamic balance detection.

[0033]

[0034] Table 2 Next, the body structure characteristics (body length, width, center of gravity position, suspension system, wheel diameter and width, etc.) and driving characteristics (dynamic parameters, etc.) of the tricycle are obtained; then, according to the body structure, driving characteristics and sensor layout array of the tricycle, simulation modeling is performed in a three-dimensional simulation platform (such as MATLAB / Simulink platform) to construct a dynamic balance test space, which can simulate the dynamic behavior of the tricycle in various driving scenarios, help detect the balance state of the tricycle, and analyze possible imbalances. Then, the parameters of the dynamic balance test space are configured according to the first road scene, reference load and reference speed, wherein the reference load and reference speed can be set according to the actual driving scene, such as the reference load is set to a total vehicle mass of 300 kg (including the mass of the driver, body and basic components), and the reference speed is set to 30 kilometers per hour, to generate a first dynamic balance test space, which will simulate the dynamic balance performance of the vehicle in the first road scene, and help evaluate the balance ability of the tricycle in common driving scenarios.

[0035] Then, an iterative simulation test of the dynamic balance of the vehicle is performed in the first dynamic balance test space. By setting different detection parameters of the vehicle body (such as acceleration, tilt angle, rotation angular velocity, etc.), the balance state (balance or imbalance) of the vehicle is analyzed, and the imbalance ratio of the vehicle during simulation under different detection parameters is counted; considering the error during simulation, a preset vehicle imbalance ratio is configured, and the preset vehicle imbalance ratio is a preset threshold value, which is used to measure whether the vehicle is in an unbalanced state during the test, such as setting the vehicle imbalance ratio to 2%; an iterative simulation test is performed, and the maximum detection parameter when it is less than the preset vehicle imbalance ratio is set as the first standard detection parameter, through multiple iterative tests, the dynamic performance of the vehicle in different scenarios is simulated, and the standard detection parameters are gradually adjusted to obtain multiple first standard detection parameters (such as maximum tilt angle, maximum acceleration change, maximum angular velocity, etc.) under multiple layout positions; then, the multiple first standard detection parameters are arranged according to the multiple layout positions to construct a first standard detection parameter distribution, which includes standard detection parameters of multiple positions of the sensor layout array in the first road scene.

[0036] Using the same method of constructing the first standard detection parameter distribution, dynamic balance detection standard analysis is performed on other road scenes to obtain multiple standard detection parameter distributions corresponding to multiple road scenes.

[0037] S140: Constructing the scene parameter comparison library according to the multiple road scenes and multiple standard detection parameter distribution mappings.

[0038] Specifically, based on the mapping relationship between the road scene and the standard detection parameter distribution, the scene parameter comparison library is constructed according to the mapping between the multiple road scenes and the multiple standard detection parameter distribution. By constructing the scene parameter comparison library, the detection parameters can be adjusted in real time under different road conditions, the accuracy and reaction speed of the imbalance warning can be improved, and the stability and safety of the tricycle in various driving environments can be ensured.

[0039] S200: During the driving of the tricycle, an initial standard detection parameter distribution is determined according to the navigation route and the scene parameter reference library.

[0040] Specifically, during the driving process of the tricycle, the vehicle's position and driving path are obtained in real time through the GPS module or the vehicle navigation system; then, according to the real-time driving position of the vehicle, the navigation data is used to judge the current road scene, and according to the current road scene, the parameters matching the current road characteristics are searched in the scene parameter comparison library to obtain the initial standard detection parameter distribution. By combining the navigation route with the scene parameter comparison library, the real-time standard detection parameter distribution matching of the tricycle under different driving conditions can be achieved, and the appropriate standard detection parameters can be dynamically determined according to the real-time navigation data, thereby effectively improving the accuracy and response speed of imbalance detection.

[0041] S300: Monitor and obtain the real-time load and real-time speed of the tricycle, and perform mapping correction on the initial standard detection parameter distribution according to the real-time load and real-time speed to obtain the adapted standard detection parameter distribution.

[0042] Furthermore, step S300 of the present application also includes: S310: Taking the body structure and sensor layout array of the tricycle as constraints, query the networked driving logs of similar tricycles, collect sample standard detection parameter distribution sets, sample vehicle load sets, sample vehicle speed sets and sample correction detection parameter distribution sets; S320: Construct a detection parameter correction model based on a generative adversarial network, and the detection parameter correction model includes a detection parameter generator and a detection parameter discriminator; S330: Iteratively train the detection parameter generator and the detection parameter discriminator with the sample standard detection parameter distribution set, sample vehicle load set, sample vehicle speed set and sample correction detection parameter distribution set until the generation loss function and the discrimination loss function converge, and obtain the trained detection parameter correction model; S340: Input the real-time load, real-time vehicle speed and initial standard detection parameter distribution into the detection parameter correction model for mapping correction, and output the adapted standard detection parameter distribution.

[0043] Specifically, the networked driving logs of similar tricycles are queried based on the body structure and sensor layout array of the tricycle as retrieval constraints. The networked driving logs contain various dynamic data recorded during the actual use of the vehicle, including on-board sensors, GPS systems and other monitoring equipment. Sample standard detection parameter distribution sets, sample vehicle load sets and sample vehicle speed sets are collected, and sample corrected detection parameter distributions are obtained after adjusting the sample standard detection parameter distributions according to different sample vehicle loads and sample vehicle speeds, to obtain sample corrected detection parameter distribution sets.

[0044] Generative adversarial network is a deep learning model that can automatically generate appropriate detection parameters under different driving conditions and load conditions by using generative adversarial network to correct detection parameters. It can also continuously optimize the accuracy and adaptability of detection parameters through adversarial training with discriminators. Firstly, a detection parameter correction model is constructed based on generative adversarial network. The detection parameter correction model includes a detection parameter generator and a detection parameter discriminator. The task of the detection parameter generator is to generate appropriate detection parameters based on the input vehicle dynamic information (such as vehicle speed and load); the task of the detection parameter discriminator is to determine whether the detection parameters generated by the generator are reasonable and whether they can accurately reflect the dynamic balance characteristics of the vehicle under given conditions. Then, the sample standard detection parameter distribution set, sample vehicle load set, sample vehicle speed set and sample corrected detection parameter distribution set are used as training data to iteratively train the detection parameter generator and the detection parameter discriminator. First, by inputting the training data, the generator starts to generate detection parameters. The goal of the generator is to generate results that match the actual detection parameters as much as possible through continuous optimization; then, the discriminator compares the generated parameters with the actual parameters, calculates the loss and returns the feedback to the generator, and optimizes the loss functions of the generator and the discriminator, and uses the gradient descent method to adjust the weights of the generator and the discriminator, and gradually optimizes the generation effect of the detection parameters. When the loss functions of the generator and the discriminator tend to be stable and the loss values ​​no longer fluctuate greatly, the model considers that the training has been completed, and a trained detection parameter correction model is obtained. At this time, the detection parameter generator can accurately generate detection parameters that meet the actual dynamic balance characteristics, and the discriminator can effectively distinguish the difference between the generated parameters and the actual parameters.

[0045] Then, during the driving process of the tricycle, the real-time load and real-time speed of the tricycle are monitored and obtained, such as using force sensors (such as strain gauges, piezoelectric sensors) installed at appropriate positions of the vehicle body (such as the connection between the frame, wheels and suspension system) to monitor the load changes in real time; using photoelectric sensors to measure the speed of wheel rotation to indirectly calculate the driving speed of the vehicle. Then, the real-time load, real-time speed and initial standard detection parameter distribution are input into the detection parameter correction model for mapping correction, and the corrected adaptation standard detection parameter distribution is output.

[0046] By real-time monitoring of the load and speed of the tricycle, combining these data with the initial standard detection parameter distribution, and inputting them into the detection parameter correction model for dynamic correction, it can be ensured that the system adapts to the actual working conditions of the vehicle in real time. The generated corrected detection parameters (such as maximum tilt angle, acceleration change, angular velocity, etc.) will be used for real-time dynamic balance detection to ensure accurate monitoring of the vehicle's balance under different loads, speeds and road conditions, thereby effectively improving the accuracy and adaptability of the dynamic balance detection system and improving the driving safety of the tricycle.

[0047] S400: Acquire the balance sensor parameter distribution of the tricycle according to the first frequency monitoring, map and judge the balance sensor parameter distribution according to the adaptation standard detection parameter distribution, and if there is a parameter abnormality, continuously monitor according to the second frequency to acquire an abnormal feature distribution sequence.

[0048] Furthermore, step S400 of the present application also includes: S410: Configure a first frequency and a second frequency, collect data on the sensor array according to the first frequency, and obtain a balanced sensor parameter distribution, wherein the second frequency is greater than the first frequency; S420: Map and judge the balanced sensor parameter distribution according to the adaptation standard detection parameter distribution, and if the sensor parameter does not meet the corresponding standard detection parameter, generate a parameter abnormality signal, and collect data on the sensor array according to the second frequency, construct an abnormal feature distribution, and obtain an abnormal feature distribution sequence within a preset time zone.

[0049] Specifically, first, configure the first frequency and the second frequency, the second frequency is greater than the first frequency, the first frequency is used for conventional real-time data collection, and a lower collection frequency can be set according to the driving state of the vehicle and the computing power of the system, such as collecting once every 1 minute; when the vehicle has potential balance abnormalities, more accurate data can be obtained by increasing the collection frequency, so the second frequency is set to be greater than the first frequency, such as collecting once every 10 seconds. This high-frequency data collection can detect and respond more quickly when an abnormality occurs. Then, during the driving process of the vehicle, the sensor array is collected according to the first frequency to obtain the balance sensor parameter distribution (including sensor collection data at multiple layout positions).

[0050] Then, the balance sensing parameter distribution is mapped and judged according to the adaptation standard detection parameter distribution, that is, the balance sensing parameter is compared with the adaptation standard detection parameter of the corresponding position. If the sensing parameter does not meet the corresponding standard detection parameter, a parameter abnormality signal is generated; then, data is collected on the sensor array according to the second frequency, and the abnormal parameter position in the balance sensing parameter distribution collected each time is marked, and its timestamp, parameter type, abnormal value and difference with the standard parameter are recorded to generate an abnormal feature distribution; continuous monitoring and data collection are performed according to the second frequency to obtain an abnormal feature distribution sequence within a preset time zone (such as within 3 minutes), and the preset time zone can be set according to actual conditions.

[0051] S500: performing a similarity comparison between the matched driving behavior abnormal feature and the abnormal feature distribution sequence, and if the comparison similarity coefficient is less than a similarity threshold, issuing a vehicle imbalance warning.

[0052] Furthermore, step S500 of the present application also includes: S510: Monitor and obtain the navigation route and driving behavior data of the vehicle in the preset time zone, and perform driving characteristic analysis based on the navigation route and driving behavior data to construct a driving characteristic portrait.

[0053] Specifically, the navigation route and driving behavior data of the vehicle in the preset time zone are monitored and obtained. The navigation route data of the vehicle is the trajectory information of the vehicle, which can be obtained through the GPS system and records the real-time position and driving path of the vehicle; the driving behavior data refers to various data reflecting the driver's operating habits and driving status, which are usually obtained through vehicle-mounted sensors (such as accelerometers, etc.), such as the driver's acceleration and braking behavior, the angle and speed changes of the driver when turning, the average driving speed, etc. Then, the driving characteristics analysis is performed according to the navigation route and driving behavior data, such as analyzing the speed changes of the vehicle in different sections, identifying whether the driver maintains a stable speed, especially the speed fluctuations on the slope or turning, and constructing a driving characteristic portrait. The driving characteristic portrait is formed by comprehensively analyzing the driver's performance under different road conditions and driving behaviors, forming a comprehensive description of the driver's behavior, including driving mode (such as stable driving turning habits, etc.), speed characteristics, etc. By generating a driving characteristic portrait, the driver's driving habits, driving safety and adaptability can be fully reflected, and the driver's behavior can be better understood, thereby providing more accurate balance detection and imbalance warning.

[0054] S520: Based on the second frequency, perform abnormal feature simulation analysis according to the navigation route and the driving feature portrait to generate a simulated abnormal feature distribution sequence.

[0055] Furthermore, step S520 of the present application also includes: S521: Divide the navigation route based on the second frequency to determine multiple route segments; S522: Take the body structure of the tricycle and the sensor layout array as constraints, collect sample route sets and sample driving feature portrait sets according to historical networked driving records, and annotate the abnormal feature distributions under different sample routes and sample driving feature portraits to obtain a sample abnormal feature distribution set; S523: Use the sample route set, sample driving feature portrait set and sample abnormal feature distribution set to iteratively train the generative adversarial network until convergence, and obtain an abnormal feature simulation plug-in; S524: Use the abnormal feature simulation plug-in to perform abnormal feature simulation analysis based on the multiple route segments and driving feature portraits, output multiple simulated abnormal feature distributions, and construct the simulated abnormal feature distribution sequence.

[0056] Specifically, the navigation route is divided according to the second frequency to obtain multiple route segments, each of which is the distance interval under the adjacent second frequency. Then, the body structure and sensor layout array of the tricycle are used as retrieval constraints, and according to the historical networked driving records, a sample route set and a sample driving feature portrait set are collected; then, the abnormal feature distribution under different sample routes and sample driving feature portraits is annotated, that is, when the vehicle's balance sensor data (such as acceleration, tilt angle, angular velocity, etc.) exceeds the set standard value, the system will mark the data point as abnormal, and these abnormal data points will be marked as abnormal features to obtain a sample abnormal feature distribution set.

[0057] The generative adversarial network consists of a generator and a discriminator. The purpose is to enable the generator to generate abnormal feature distribution data that is as realistic as possible through adversarial training. The generator generates simulated abnormal feature distribution through a neural network based on the input route segment and driving feature portrait; the discriminator is used to evaluate the similarity between the abnormal feature distribution output by the generator and the real data. Then, the sample route set, sample driving feature portrait set and sample abnormal feature distribution set are used as training data to iteratively train the generative adversarial network, and continuously optimize the parameters of the generator and the discriminator until the generator can generate abnormal feature data close to the real one, and the discriminator cannot distinguish between the generated data and the real data, until the loss function converges, and a trained abnormal feature simulation plug-in is obtained. The abnormal feature simulation plug-in can generate corresponding abnormal feature distribution data based on the input route segment and driving feature portrait.

[0058] The multiple route segments and driving feature portraits are further combined to obtain multiple input data, which are sequentially input into the abnormal feature simulation plug-in for abnormal feature simulation analysis, and multiple simulated abnormal feature distributions are output to construct a simulated abnormal feature distribution sequence.

[0059] S530: Perform similarity mapping comparison on the simulated abnormal feature distribution sequence and the abnormal feature distribution sequence, output a plurality of similarity coefficients, and obtain the comparison similarity coefficient after average calculation.

[0060] Specifically, a similarity mapping comparison is performed on the simulated abnormal feature distribution sequence and the abnormal feature distribution sequence, that is, a similarity analysis is performed on the simulated abnormal feature distribution and the abnormal feature distribution at the same time node. The cosine similarity algorithm can be used to calculate the similarity. The cosine similarity measures the similarity of two vectors by calculating the angle between them. The value range is from -1 (completely opposite) to 1 (completely identical), and 0 represents dissimilarity. Each pair of data point comparison will output a similarity coefficient, which represents the similarity between the simulated data and the actual data, and multiple similarity coefficients are obtained. Then, the multiple similarity coefficients are averaged to obtain a comparison similarity coefficient, which reflects the overall similarity between the simulated data and the actual data.

[0061] Configure a similarity threshold, which is used to determine whether the difference between the simulated data and the actual data exceeds the tolerance range. It can be set according to the actual scenario. For example, if the similarity threshold is set to 60%, when the comparison similarity coefficient is less than the preset similarity threshold, it means that the difference between the simulated data and the actual data is large, that is, the current abnormality is not caused by the driver's driving behavior, and a vehicle imbalance warning is issued; when the comparison similarity coefficient is greater than or equal to the preset similarity threshold, it means that the difference between the simulated data and the actual data is small, and it is generally believed that the current abnormal feature is caused by the driver's driving behavior, and no vehicle imbalance warning is issued.

[0062] By configuring similarity thresholds and comparing simulated data with actual data, it is possible to determine whether the anomaly is caused by driver behavior, thereby avoiding misjudgment of imbalance caused by different driving characteristics and effectively improving the accuracy and reliability of vehicle imbalance warnings.

[0063] In summary, the dynamic balance detection method of the electric tricycle body structure provided by the present application has the following technical effects: By introducing technical means such as dynamically adjusting detection parameters, multi-frequency monitoring and driving characteristic similarity comparison, it is possible to solve the technical problems that traditional detection methods cannot effectively adapt to complex driving scenarios, have slow responses, frequent false alarms, and lead to insufficient timeliness and accuracy of vehicle imbalance warnings; it can achieve accurate imbalance monitoring in complex driving environments, significantly improve the adaptability, timeliness and accuracy of vehicle imbalance warnings, thereby effectively improving the safety and driving stability of electric tricycles.

[0064] Embodiment 2: Based on the dynamic balance detection method of the body structure of an electric tricycle in the above embodiment, the present application also provides a dynamic balance detection system for the body structure of an electric tricycle, as shown in the attached Figure 3 , including: a dynamic balance detection standard analysis module 11, which is used to divide and determine multiple road scenes based on road type, road slope and turning angle as change factors, and perform dynamic balance detection standard analysis according to the reference load and reference speed of the tricycle under the multiple road scenes, generate multiple standard detection parameter distributions, and map and construct a scene parameter reference library; an initial standard detection parameter distribution determination module 12, which is used to determine the initial standard detection parameter distribution according to the navigation route and the scene parameter reference library during the driving of the tricycle; an initial standard detection parameter distribution correction module 13, which is used to monitor and obtain the real-time load and real-time speed of the tricycle, The initial standard detection parameter distribution is mapped and corrected according to the real-time load and the real-time vehicle speed to obtain the adapted standard detection parameter distribution; the abnormal feature distribution sequence acquisition module 14 is used to monitor and obtain the balance sensor parameter distribution of the tricycle according to the first frequency, and map and judge the balance sensor parameter distribution according to the adapted standard detection parameter distribution. If there is a parameter abnormality, continuous monitoring is performed according to the second frequency to obtain the abnormal feature distribution sequence; the vehicle imbalance warning module 15 is used to perform a similarity comparison between the matched driving behavior abnormal features and the abnormal feature distribution sequence. If the comparison similarity coefficient is less than the similarity threshold, a vehicle imbalance warning is issued.

[0065] Furthermore, the dynamic balance detection system for the body structure of an electric tricycle is also used to: divide the step size according to the road slope and the turning angle, and determine a plurality of road slope intervals and a plurality of turning angle intervals; randomly combine a predetermined road type and the plurality of road slope intervals and the plurality of turning angle intervals to generate a plurality of road scenes; randomly select a first road scene from the plurality of road scenes, perform a dynamic balance detection standard analysis according to a benchmark load and a benchmark speed of the tricycle, generate a first standard detection parameter distribution, and add the first standard detection parameter distribution to the plurality of standard detection parameter distributions; and construct the scene parameter reference library according to the mapping of the plurality of road scenes and the plurality of standard detection parameter distributions.

[0066] Furthermore, the dynamic balance detection system for the body structure of an electric tricycle is also used to: obtain a sensor layout array for dynamic balance detection of the tricycle, wherein the sensor layout array includes a plurality of layout positions, each layout position includes at least one sensor, and the sensor includes at least an acceleration sensor, a gyroscope sensor and a tilt sensor; construct a dynamic balance test space by simulating the body structure, driving characteristics and the sensor layout array of the tricycle, and configure parameters according to the first road scene, benchmark load and benchmark vehicle speed to generate a first dynamic balance test space; perform an iterative simulation test of the vehicle dynamic balance in the first dynamic balance test space, set the maximum detection parameter when it is less than a preset vehicle imbalance ratio as a first standard detection parameter, and generate the first standard detection parameter distribution.

[0067] Furthermore, the dynamic balance detection system for the body structure of an electric tricycle is also used to: query the networked driving logs of similar tricycles based on the tricycle body structure and sensor layout array as constraints, collect sample standard detection parameter distribution sets, sample vehicle load sets, sample vehicle speed sets and sample correction detection parameter distribution sets; construct a detection parameter correction model based on a generative adversarial network, the detection parameter correction model includes a detection parameter generator and a detection parameter discriminator; iteratively train the detection parameter generator and the detection parameter discriminator with the sample standard detection parameter distribution set, sample vehicle load set, sample vehicle speed set and sample correction detection parameter distribution set until the generation loss function and the discrimination loss function converge, and obtain a trained detection parameter correction model; input the real-time load, real-time vehicle speed and initial standard detection parameter distribution into the detection parameter correction model for mapping correction, and output the adapted standard detection parameter distribution.

[0068] Furthermore, the dynamic balance detection system for the body structure of an electric tricycle is also used to: configure a first frequency and a second frequency, collect data on the sensor array according to the first frequency, and obtain a balance sensor parameter distribution, wherein the second frequency is greater than the first frequency; map and judge the balance sensor parameter distribution according to the adaptation standard detection parameter distribution, if the sensor parameter does not meet the corresponding standard detection parameter, generate a parameter abnormality signal, and collect data on the sensor array according to the second frequency, construct an abnormal feature distribution, and obtain an abnormal feature distribution sequence within a preset time zone.

[0069] Furthermore, the dynamic balance detection system for the body structure of an electric tricycle is also used to: monitor and obtain the navigation route and driving behavior data of the vehicle in the preset time zone, and perform driving feature analysis based on the navigation route and driving behavior data to construct a driving feature portrait; based on the second frequency, perform abnormal feature simulation analysis based on the navigation route and driving feature portrait to generate a simulated abnormal feature distribution sequence; perform similarity mapping comparison on the simulated abnormal feature distribution sequence and the abnormal feature distribution sequence, output multiple similarity coefficients, and obtain the comparison similarity coefficient after average calculation.

[0070] Furthermore, the dynamic balance detection system for the body structure of an electric tricycle is also used to: divide the navigation route based on the second frequency to determine multiple route segments; take the tricycle body structure and sensor layout array as constraints, collect sample route sets and sample driving feature portrait sets according to historical networked driving records, and annotate the abnormal feature distributions under different sample routes and sample driving feature portraits to obtain a sample abnormal feature distribution set; use the sample route set, sample driving feature portrait set and sample abnormal feature distribution set to iteratively train the generative adversarial network until convergence, and obtain an abnormal feature simulation plug-in; use the abnormal feature simulation plug-in to perform abnormal feature simulation analysis based on the multiple route segments and driving feature portraits, output multiple simulated abnormal feature distributions, and construct the simulated abnormal feature distribution sequence.

[0071] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The dynamic balance detection method and specific examples of an electric tricycle body structure in the aforementioned embodiment 1 are also applicable to a dynamic balance detection system for an electric tricycle body structure in this embodiment. Through the aforementioned detailed description of the dynamic balance detection method for an electric tricycle body structure, those skilled in the art can clearly know the dynamic balance detection system for an electric tricycle body structure in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the system disclosed in the 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.

[0072] 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.

[0073] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A method for detecting the dynamic balance of an electric tricycle body structure, characterized in that: Methods include: Taking road type, road slope and turning angle as change factors, multiple road scenes are divided and determined, and under the multiple road scenes, dynamic balance detection standard analysis is performed according to the reference load and reference speed of the tricycle, multiple standard detection parameter distributions are generated, and a scene parameter comparison library is mapped and constructed; During the driving of the tricycle, the initial standard detection parameter distribution is determined according to the navigation route and the scene parameter reference library; Monitor and obtain the real-time load and real-time speed of the tricycle, and perform mapping correction on the initial standard detection parameter distribution according to the real-time load and real-time speed to obtain the adapted standard detection parameter distribution; Acquire the balance sensor parameter distribution of the tricycle according to the first frequency monitoring, map and judge the balance sensor parameter distribution according to the adaptation standard detection parameter distribution, and if there is a parameter abnormality, continuously monitor according to the second frequency to acquire an abnormal feature distribution sequence; A similarity comparison is performed on the matched driving behavior abnormal features and the abnormal feature distribution sequence. If the comparison similarity coefficient is less than a similarity threshold, a vehicle imbalance warning is issued.

2. The dynamic balance detection method of the electric tricycle body structure according to claim 1 is characterized in that: Taking the road type, road slope and turning angle as the variation factors, multiple road scenes are divided and determined, and in the multiple road scenes, dynamic balance detection standard analysis is performed according to the reference load and reference speed of the tricycle, multiple standard detection parameter distributions are generated, and a scene parameter comparison library is mapped and constructed, including: According to the road surface slope and the turning angle, the step length is divided to determine a plurality of road surface slope intervals and a plurality of turning angle intervals; After randomly combining the predetermined road surface type and the plurality of road surface slope intervals and the plurality of turning angle intervals, a plurality of road surface scenes are generated; Randomly selecting a first road scene from the multiple road scenes, performing a dynamic balance detection standard analysis according to a reference load and a reference speed of the tricycle, generating a first standard detection parameter distribution, and adding the first standard detection parameter distribution to the multiple standard detection parameter distributions; The scene parameter comparison library is constructed according to the multiple road scenes and multiple standard detection parameter distribution mappings.

3. The dynamic balance detection method of the electric tricycle body structure according to claim 2 is characterized in that: A dynamic balance detection standard analysis is performed according to the reference load and reference speed of the tricycle to generate a first standard detection parameter distribution, including: Acquire a sensor arrangement array for dynamic balance detection of a tricycle, wherein the sensor arrangement array includes a plurality of arrangement positions, each arrangement position includes at least one sensor, and the sensor includes at least an acceleration sensor, a gyroscope sensor, and a tilt sensor; A dynamic balance test space is constructed by simulating the body structure, driving characteristics and the sensor arrangement array of the tricycle, and parameters are configured according to the first road scene, reference load and reference vehicle speed to generate a first dynamic balance test space; An iterative simulation test of vehicle dynamic balance is performed in the first dynamic balance test space, a maximum detection parameter when it is less than a preset vehicle imbalance ratio is set as a first standard detection parameter, and the first standard detection parameter distribution is generated.

4. The dynamic balance detection method of the electric tricycle body structure according to claim 3 is characterized in that: Mapping and correcting the initial standard detection parameter distribution according to the real-time load and the real-time vehicle speed to obtain an adapted standard detection parameter distribution includes: Taking the tricycle body structure and sensor layout array as constraints, query the networked driving logs of similar tricycles to collect sample standard detection parameter distribution sets, sample vehicle load sets, sample vehicle speed sets and sample correction detection parameter distribution sets; Constructing a detection parameter correction model based on a generative adversarial network, wherein the detection parameter correction model includes a detection parameter generator and a detection parameter discriminator; Iteratively training the detection parameter generator and the detection parameter discriminator with the sample standard detection parameter distribution set, the sample vehicle load set, the sample vehicle speed set and the sample correction detection parameter distribution set until both the generation loss function and the discrimination loss function converge, thereby obtaining a trained detection parameter correction model; The real-time load, real-time vehicle speed and initial standard detection parameter distribution are input into the detection parameter correction model for mapping correction, and the adapted standard detection parameter distribution is output.

5. The dynamic balance detection method of the electric tricycle body structure according to claim 3 is characterized in that: The balance sensor parameter distribution of the tricycle is acquired according to the first frequency monitoring, and the balance sensor parameter distribution is mapped and judged according to the adaptation standard detection parameter distribution. If there is a parameter abnormality, continuous monitoring is performed according to the second frequency to acquire an abnormal feature distribution sequence, including: configuring a first frequency and a second frequency, collecting data on the sensor array according to the first frequency, and obtaining a balanced sensing parameter distribution, wherein the second frequency is greater than the first frequency; The balanced sensing parameter distribution is mapped and judged according to the adaptation standard detection parameter distribution. If the sensing parameter does not meet the corresponding standard detection parameter, a parameter abnormality signal is generated, and data is collected on the sensor array according to the second frequency to construct an abnormal feature distribution and obtain an abnormal feature distribution sequence within a preset time zone.

6. The dynamic balance detection method of the electric tricycle body structure according to claim 5 is characterized in that: Performing a similarity comparison between the matched driving behavior abnormal features and the abnormal feature distribution sequence, including: Monitoring and acquiring the navigation route and driving behavior data of the vehicle in the preset time zone, and performing driving characteristic analysis based on the navigation route and driving behavior data to construct a driving characteristic profile; Based on the second frequency, performing abnormal feature simulation analysis according to the navigation route and the driving feature portrait to generate a simulated abnormal feature distribution sequence; A similarity mapping comparison is performed on the simulated abnormal feature distribution sequence and the abnormal feature distribution sequence, and a plurality of similarity coefficients are output. The comparison similarity coefficient is obtained after the average is calculated.

7. The method for dynamic balance detection of the body structure of an electric tricycle according to claim 6, characterized in that: Based on the second frequency, performing abnormal feature simulation analysis according to the navigation route and the driving feature portrait to generate a simulated abnormal feature distribution sequence, including: dividing the navigation route based on the second frequency to determine a plurality of route segments; Taking the tricycle body structure and sensor layout array as constraints, according to historical networked driving records, a sample route set and a sample driving feature portrait set are collected, and abnormal feature distributions under different sample routes and sample driving feature portraits are annotated to obtain a sample abnormal feature distribution set; Iteratively train the generative adversarial network using the sample route set, the sample driving feature portrait set, and the sample abnormal feature distribution set until convergence, and obtain an abnormal feature simulation plug-in; The abnormal feature simulation plug-in is used to perform abnormal feature simulation analysis according to the multiple route segments and driving feature portraits, output multiple simulated abnormal feature distributions, and construct the simulated abnormal feature distribution sequence.

8. A dynamic balance detection system for an electric tricycle body structure, characterized in that: The steps for implementing the dynamic balance detection method of the electric tricycle body structure according to any one of claims 1 to 7 include: A dynamic balance detection standard analysis module is used to determine a plurality of road scenes based on the road type, road slope and turning angle as the variation factors, and to perform a dynamic balance detection standard analysis according to the reference load and reference speed of the tricycle under the plurality of road scenes, generate a plurality of standard detection parameter distributions, and map and construct a scene parameter comparison library; An initial standard detection parameter distribution determination module is used to determine the initial standard detection parameter distribution according to the navigation route and the scene parameter reference library during the driving process of the tricycle; An initial standard detection parameter distribution correction module is used to monitor and obtain the real-time load and real-time speed of the tricycle, and perform mapping correction on the initial standard detection parameter distribution according to the real-time load and real-time speed to obtain an adapted standard detection parameter distribution; An abnormal characteristic distribution sequence acquisition module is used to monitor and acquire the balance sensor parameter distribution of the tricycle according to the first frequency, map and judge the balance sensor parameter distribution according to the adaptation standard detection parameter distribution, and if there is a parameter abnormality, continuously monitor according to the second frequency to acquire the abnormal characteristic distribution sequence; The vehicle imbalance warning module is used to perform a similarity comparison between the matched driving behavior abnormal characteristics and the abnormal characteristic distribution sequence. If the comparison similarity coefficient is less than the similarity threshold, a vehicle imbalance warning is issued.

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