Controller strategy optimization method for user behavior analysis

Abstract

The invention provides a controller strategy optimization method for user behavior analysis, and relates to the technical field of controllers, and the method comprises the steps: collecting user driving parameters; speed risk level and speed change feature recognition segmentation is carried out according to the driving speed, and a speed adaptive interval is determined; according to the braking and steering force and frequency, steering and braking risk evaluation is conducted on the speed self-adaption interval; performing controller strategy matching analysis according to the stability risk coefficient, determining an optimization adjustment coefficient, and adding the optimization adjustment coefficient to a speed adaptive interval; matching is carried out according to the monitored current driving speed and the speed self-adaptive interval, an optimization adjustment coefficient is obtained, and controller strategy adjustment is carried out. The technical problem that in the prior art, due to the fact that the electric tricycle is prone to insufficient stability and rollover during high-speed turning, driving is unstable can be solved, the control strategy is dynamically adjusted according to the actual driving behavior of a user and the state of the tricycle, and the driving safety and stability are improved.

Description

Technical Field

[0001] The present application relates to the field of controller technology, and in particular to a controller strategy optimization method for user behavior analysis. Background Art

[0002] As an important means of transportation, electric tricycles are widely used in short-distance transportation, urban distribution and rural travel. They have a simple structure, low cost and easy operation, so they play an important role in logistics transportation, cargo handling and individual travel. Electric tricycles usually adopt a front-wheel steering and rear-wheel drive layout. According to different uses, they can be divided into cargo type, passenger type and comprehensive type. However, since the design of electric tricycles needs to accommodate a larger cargo space or riding area, their center of gravity is often higher than that of two-wheeled electric vehicles or ordinary cars, and they are prone to instability when turning at high speed. Especially when carrying cargo or people, the center of gravity may deviate from the center line, further increasing the risk of rollover.

[0003] The controller of an electric tricycle is one of the core electronic components, responsible for precise control of the speed, torque and braking system of the motor to ensure efficient and smooth driving of the electric tricycle. However, most of the existing electric tricycle controller strategies are based on fixed algorithm control strategies, which usually do not consider the dynamic factors in complex driving environments and lack real-time response to the driver's individual driving behavior. Therefore, in the face of emergency situations or high-speed turns, it is easy to cause insufficient vehicle stability and increase the risk of rollover.

[0004] In summary, the prior art has technical problems in that due to the structural characteristics, center of gravity distribution and user operation of electric tricycles, they are prone to lack of stability and rollover when turning at high speeds, further affecting driving safety. Summary of the invention

[0005] The purpose of this application is to provide a controller strategy optimization method for user behavior analysis, in order to solve the technical problem in the prior art that due to the structural characteristics, center distribution and user operation of electric tricycles, insufficient stability and rollover may occur during high-speed turns, further affecting driving safety.

[0006] In view of the above problems, the present application provides a controller strategy optimization method for user behavior analysis, wherein the controller strategy optimization method for user behavior analysis includes: collecting user driving parameters, the user driving parameters include driving speed, braking and steering force and frequency; performing speed risk level and speed change feature identification and segmentation according to the driving speed, and determining a speed adaptive interval, the speed adaptive interval includes a speed risk level and a speed change amplitude; performing steering and braking risk assessment on the speed adaptive interval according to the braking and steering force and frequency, and obtaining a stability risk coefficient; performing controller strategy matching analysis according to the stability risk coefficient, determining an optimization adjustment coefficient, and adding the optimization adjustment coefficient to the speed adaptive interval to generate an interval optimization adjustment label; matching the current driving speed with the speed adaptive interval based on monitoring, obtaining the optimization adjustment coefficient corresponding to the adaptive interval, and adjusting the controller strategy according to the optimization adjustment coefficient.

[0007] The technical solution provided in this application has at least the following technical effects or advantages: By collecting user driving parameters, the user driving parameters include driving speed, braking and steering strength and frequency; according to the driving speed, the speed risk level and speed change characteristics are identified and segmented to determine the speed adaptive interval, and the speed adaptive interval includes the speed risk level and speed change amplitude; according to the braking and steering strength and frequency, the speed adaptive interval is respectively evaluated for steering and braking risk to obtain the stability risk coefficient; according to the stability risk coefficient, the controller strategy matching analysis is performed to determine the optimization adjustment coefficient, and the optimization adjustment coefficient is added to the speed adaptive interval to generate an interval optimization adjustment label; according to the monitoring of the current driving speed and the speed adaptive interval, the optimization adjustment coefficient corresponding to the adaptive interval is obtained, and the controller strategy is adjusted according to the optimization adjustment coefficient. In other words, by collecting user driving parameters, the user's driving habits and behavior patterns are fully understood, the driving state of the vehicle is evaluated in real time, the risk of braking and steering during driving is evaluated, the stability coefficient is determined, the control strategy that best suits the current driving state is selected, and the controller strategy is dynamically matched and adjusted to adapt to the user's driving characteristics and vehicle state, thereby improving the safety and stability of vehicle driving.

[0008] 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

[0009] 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.

[0010] Figure 1 A flowchart of a controller strategy optimization method for user behavior analysis in this application.

[0011] Figure 2 A schematic diagram of a flow chart for determining an optimization adjustment coefficient in a controller strategy optimization method for user behavior analysis in the present application. DETAILED DESCRIPTION

[0012] This application provides a controller strategy optimization method for user behavior analysis, which solves the technical problem in the prior art that electric tricycles are prone to instability and rollover when turning at high speeds due to factors such as the structural characteristics, center distribution and user operation of electric tricycles, which further affects driving safety. By collecting user driving parameters, the user's driving habits and behavior patterns are fully understood, the driving state of the vehicle is evaluated in real time, the risk of braking and steering during driving is evaluated, the stability coefficient is determined, the control strategy most suitable for the current driving state is selected, and the controller strategy is dynamically matched and adjusted to adapt to the user's driving characteristics and vehicle status, thereby improving the safety and stability of vehicle driving.

[0013] 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.

[0014] For examples, please see the attached Figure 1 The present application provides a controller strategy optimization method for user behavior analysis, wherein the controller strategy optimization method for user behavior analysis specifically includes the following steps: Step 1: Collect user driving parameters, which include driving speed, braking and steering force and frequency.

[0015] Specifically, sensors on the vehicle (such as accelerometers, steering angle sensors, brake pressure sensors, etc.) are used to collect key data in real time during driving, including driving speed, braking and steering force and frequency. Driving speed is obtained through the vehicle-mounted GPS or wheel speed sensor; braking force is measured by the pressure sensor on the brake pedal; steering force is obtained through the force sensor of the steering system or the current signal of the electric steering system, or by measuring the steering wheel rotation angle and force; braking frequency and steering frequency are achieved by detecting the timing of braking or steering operations, usually recorded by accelerometers or special clock synchronization systems. Braking frequency refers to the frequency of the driver's braking operation within a certain period of time, and steering frequency refers to the frequency of the driver's steering operation, reflecting the driver's driving habits (for example, frequent braking may indicate that the driver's operation is more intense or encounters more traffic interference). A high steering frequency may mean that the driver is frequently changing lanes or turning, especially in urban traffic, which is usually closely related to driving habits, road conditions and traffic flow. The driver's operating habits are identified by analyzing the collected driving data. For example, frequent sudden braking may mean that the driver is not foreseeable when facing traffic conditions, while frequent high-speed turns may reflect a more aggressive driving style. By collecting data on driving speed, braking force and steering behavior in real time, detailed information about the user's driving behavior can be obtained in a timely manner and responded to.

[0016] Step 2: According to the driving speed, the speed risk level and speed change characteristics are identified and segmented to determine a speed adaptation interval, wherein the speed adaptation interval includes a speed risk level and a speed change amplitude.

[0017] Specifically, multiple tests are conducted on different vehicle models, and vehicle speed test data is collected. Speed ​​stability risk level classification is conducted, that is, risk assessment is conducted on the stability of different vehicle models under different speed conditions to determine the corresponding speed risk level. Based on the risk classification, multiple speed risk level intervals are determined, including speed risk intervals and speed change amplitude intervals. Based on the vehicle's driving speed, the speed change amplitude is calculated, and the driving speed and speed change amplitude are matched and mapped with multiple speed risk level intervals to determine the speed risk level corresponding to the current driving speed. Speed ​​risk level classification is to determine the corresponding risk level based on the vehicle's driving speed in the speed risk interval, and match the driving speed with the corresponding speed range. Speed ​​change feature recognition is to determine the corresponding risk level based on the calculated speed change amplitude in the speed change amplitude interval, and identify which speed changes are more drastic fluctuations.

[0018] According to different risk levels and speed change amplitudes, the speed adaptive interval is obtained to ensure that the vehicle can maintain the best stability under different driving conditions. The speed adaptive interval is a dynamically adjusted interval, which is set according to the real-time driving speed of the vehicle, the driver's behavior and the stability requirements of the vehicle. The parameters in each interval may be different and will be automatically adjusted according to the current speed, vehicle condition and external environmental factors to ensure the best driving experience and safety. The speed adaptive interval includes speed risk level and speed change amplitude. Among them, the speed risk level refers to the risk level divided according to the stability assessment results within different speed ranges; the speed change amplitude refers to the change in vehicle speed, indicating the intensity of vehicle acceleration or deceleration. The shorter the time of intense speed change, the greater the change amplitude. By dividing the speed risk level and speed change amplitude, the control strategy is adjusted in real time under different driving conditions to improve the stability of the vehicle when driving at high speed and turning sharply, thereby reducing the risk of rollover and loss of control.

[0019] Step 3: Perform steering and braking risk assessment on the speed adaptation interval according to the braking and steering forces and frequencies to obtain a stability risk coefficient.

[0020] Specifically, according to the braking and steering force, the operation duration of braking and steering is obtained. The operation intensity is obtained by using the operation force and operation duration of each braking or steering. According to the correspondence between the operation intensity and the frequency of braking and steering, the operation frequency of different operation intensities is obtained. The frequency discreteness of the operation frequency of different operation intensities is calculated, and the corresponding feature evaluation level is divided according to the frequency discreteness using the preset level discrete interval value. According to the braking and steering force and frequency data, the user behavior characteristics are analyzed, and the analyzed behavior characteristics are associated with the user's driving mode to determine the user's driving operation characteristics, that is, conservative driving, smooth driving, and aggressive driving.

[0021] According to the vehicle's driving speed, braking and steering operation strength and frequency, the corresponding speed adaptation interval is determined. It is a dynamically changing range that will be continuously adjusted according to the speed, intensity and frequency of the control behavior. The user's driving operation characteristics and the speed value and speed change amplitude of the speed adaptation interval are used as input variables, and the risk prediction model is input to predict the risks of different driving speeds within the speed adaptation interval to obtain a qualitative risk coefficient. Using the stability risk coefficient and speed adaptation interval, the control strategy is automatically adjusted to adapt to different driving conditions and operating styles, thereby improving the stability and safety of the vehicle.

[0022] Step 4: Perform controller strategy matching analysis based on the stability risk coefficient, determine the optimization adjustment coefficient, and add the optimization adjustment coefficient to the speed adaptation interval to generate an interval optimization adjustment label.

[0023] Specifically, by analyzing historical data and conducting experiments, the control influence relationship between steering stability risk, braking stability risk and controller strategy parameters is established, and which control parameters can be adjusted to solve the stability problems in braking and steering. Defining a stability threshold and determining when the controller strategy parameters need to be adjusted is a key indicator for measuring vehicle safety. When the stability risk coefficient exceeds this threshold, parameter adjustment is triggered to reduce the risk. A large amount of historical data is collected to calculate the stability risk coefficient of the vehicle under different driving conditions. At the same time, the strategy parameters of the control system (such as braking intensity, steering angle, etc.) and their changes are collected during each test. Through the collected historical data, the adjustment relationship between the stability risk coefficient and the controller strategy parameters is established using the test data fitting technology, which is used to characterize the parameter adjustment range that matches the stability risk coefficient, that is, to determine how much the controller strategy parameters need to be adjusted according to the size of the stability risk coefficient.

[0024] The parameter control coefficient in the control influence relationship is adjusted using the adjustment relationship. The parameter control coefficient is part of the controller strategy parameters and determines the degree of adjustment of the controller to the vehicle behavior. By adjusting the parameter control coefficient, the optimization adjustment coefficient is obtained, which is calculated based on the current stability risk coefficient and the preset adjustment relationship, aiming to optimize the stability and safety of the vehicle. For example, when the stability risk coefficient is low (such as 0.2), it means that the current driving state is relatively safe. At this time, the controller can provide a more sensitive response, such as a higher steering angle and acceleration response; when the stability risk coefficient is high (such as 0.9), it means that the current driving state has a higher risk. At this time, the controller needs to make a more conservative response, such as reducing the steering angle, reducing the acceleration response, or strengthening the braking response, such as increasing the braking intensity by 20% and reducing the steering angle by 10%.

[0025] The optimization adjustment coefficient is added to the speed adaptation interval to generate an interval optimization adjustment label, which is used to identify the controller parameter adjustment strategy that should be applied in each speed interval, so that the controller can automatically select the appropriate control strategy and adjust the controller parameters according to the real-time driving state of the vehicle and the driving behavior of the user to provide a more stable and safe driving experience. By performing controller strategy matching analysis based on the stability risk factor, the optimization adjustment coefficient is generated and applied to the speed adaptation interval, and the control parameters are dynamically adjusted for each speed interval and stability risk factor, which improves the stability of the vehicle, especially in high-risk situations, to ensure that the vehicle will not lose control.

[0026] Step 5: According to the monitoring, the current driving speed is matched with the speed adaptive interval to obtain the optimization adjustment coefficient corresponding to the adaptive interval, and the controller strategy is adjusted according to the optimization adjustment coefficient.

[0027] Specifically, the current driving speed is monitored in real time through the speed sensor, and the speed adaptation interval is matched to determine the corresponding speed interval. The speed adaptation interval is determined according to the vehicle's driving speed and speed change amplitude, and each interval has a corresponding optimization adjustment coefficient. The matched interval corresponds to a set of optimization adjustment coefficients, and the preset optimization adjustment coefficients are extracted. The optimization adjustment coefficients are obtained by fitting and calculating historical driving data and risk assessment models, and are specifically used to adjust the operating parameters of the controller. According to the obtained optimization adjustment coefficients, the controller strategy is adjusted, which means that the controller parameters are automatically adjusted according to the real-time driving status of the vehicle and the user's driving behavior to provide a more stable and safe driving experience. For example, if the current speed of the vehicle is 50km / h, it is matched to the medium speed interval; the optimization adjustment coefficients are extracted: braking intensity 1.2, steering sensitivity 0.8; the controller parameters are adjusted in real time, the braking intensity is increased from 100 to 120; the steering sensitivity is reduced from 1.0 to 0.8. When the strategy is not adjusted, the probability of vehicle rollover in the medium speed interval is 15%, and the driving stability score is 70%; after adjusting the strategy, the probability of vehicle rollover is reduced to 8%, and the driving stability score is increased to 85%. The control strategy is adjusted dynamically according to the real-time speed, which effectively reduces the stability risk caused by speed changes during driving, makes the vehicle's handling performance in different speed ranges more in line with driving needs, and improves overall comfort. According to the matching of the current driving speed and the speed adaptation range, the controller strategy is adjusted using the optimization adjustment coefficient to achieve dynamic optimization of vehicle control, ensure driving safety and comfort, and reflect the advantages of intelligent control.

[0028] Further, step 2 of this application includes: Acquire vehicle speed test data, the vehicle speed test data having a vehicle model; perform speed stability risk level classification according to the vehicle speed test data, and determine a speed risk level interval, which includes a speed risk interval and a speed change amplitude interval; perform speed change amplitude calculation according to the driving speed to obtain a speed time series change amplitude; match and map the driving speed and the speed time series change amplitude using the speed risk interval and the speed change amplitude interval to obtain a multi-speed risk level interval; and construct the speed adaptive interval according to the multi-speed risk level interval.

[0029] Specifically, professional testing equipment, such as speed recorders, are used to collect speed data of vehicles under specific test conditions. Each vehicle speed test data is marked with the corresponding vehicle model. Different models of vehicles differ in design, weight distribution, power system, etc., which affects their driving performance. Speed ​​test data include indicators such as vehicle acceleration performance, stability performance, and braking performance at different speeds. According to the vehicle speed test data, the stability under different speed conditions is evaluated. Through statistical analysis of speed data, such as average speed, maximum speed, minimum speed, and speed fluctuation amplitude, the speed risk level is determined, including speed risk interval and speed change amplitude interval. The speed risk interval divides the speed range into several intervals, such as low speed, medium speed, high speed, etc., according to the stability performance of the vehicle at different speeds. Each interval corresponds to a different risk level. The speed change amplitude interval analyzes the change amplitude of the vehicle speed, such as the rate of acceleration or deceleration, and divides it into different intervals, such as small amplitude, medium amplitude, large amplitude, etc., to evaluate the impact of speed change on stability.

[0030] Calculate the change amplitude of driving speed, that is, the change of vehicle speed within a certain period of time, which reflects the severity of vehicle acceleration or deceleration. By performing differential calculation on the vehicle's driving speed data (that is, the speed difference between adjacent moments), the speed time series change amplitude, that is, the change of the speed change rate over time, is obtained. Match and map the real-time collected driving speed with the pre-defined speed risk interval, and match and map the speed time series change amplitude calculated based on the driving speed with the pre-defined speed change amplitude interval to determine the risk level interval of the vehicle's current driving state. Matching mapping is a data processing method that compares a set of data (such as real-time monitored driving speed and speed change amplitude) with another set of pre-defined data intervals (such as speed risk intervals and speed change amplitude intervals) to determine the category or level to which the data belongs. Multi-speed risk level intervals refer to dividing the vehicle's driving state into multiple risk levels based on different combinations of driving speed and speed change amplitude.

[0031] According to the multi-speed risk level interval, the speed adaptive interval is constructed, that is, the speed range of the control strategy is automatically adjusted according to the real-time driving data of the vehicle (current speed and speed change amplitude). The speed adaptive interval allows the control strategy to be adjusted dynamically so that the vehicle can maintain the best stability under different speed conditions. By dividing the speed risk interval and the speed change amplitude interval in real time, the control strategy of the vehicle can be accurately adjusted within different speed ranges, effectively reducing the stability risk during high-speed driving and reducing the possibility of rollover and loss of control. By adaptively adjusting the controller parameters, a smoother and more comfortable driving experience can be provided according to different driving speeds and behavior patterns.

[0032] Further, step three of this application includes: According to the force and frequency of braking and steering, the user's braking and steering behavior characteristics are analyzed to determine the user's driving operation characteristics; the user's driving operation characteristics and the speed value and speed change amplitude of the speed adaptation interval are used as input variables, and the risk prediction model is used to perform risk prediction on each speed adaptation interval to obtain the stability risk coefficient.

[0033] Specifically, the force of braking and steering refers to the force applied by the user when operating the brakes and steering during driving. For braking, the force determines the strength of the brakes; for steering, the force determines the steering angle or the speed of the steering. The frequency of braking and steering refers to the frequency of the user's braking and steering operations during driving, that is, the number of operations. Frequent braking or steering usually means that the driver is in a more complex driving environment, which may lead to higher operational risks. The force of each braking and steering operation performed by the user when operating the electric tricycle is obtained, and the duration is recorded, that is, the length of time the driver maintains the operation each time the brake or steering is performed. The operation intensity is calculated according to the operation force and operation duration of each braking or steering, and the operation intensity = force × duration. The intensity of each operation is analyzed with its frequency to obtain the operation frequency corresponding to different operation intensity levels. The calculation of the operation frequency is based on the number of times the driver brakes or turns at a certain intensity. Different operation intensities correspond to different operation frequencies. According to the obtained operation frequencies corresponding to different operation intensities, the discreteness of the operation frequency is calculated. Usually, the discreteness of the operation frequency is obtained by calculating the mean and standard deviation of the operation frequencies of different operation intensities and normalizing the standard deviation. According to the results of frequency discreteness, the driver's operating behavior is divided into three types: conservative driving, smooth driving, and aggressive driving, and the user's driving operation characteristics are obtained. Behavioral characteristic evaluation refers to the classification and evaluation of the driver's operating behavior based on characteristics such as frequency discreteness, which is usually divided into three types: conservative driving, smooth driving, and aggressive driving.

[0034] A risk prediction model is a mathematical or machine learning model that predicts potential risks based on input variables (such as user behavior, vehicle status, environmental factors, etc.). In the optimization of controller strategies for electric tricycles, risk prediction models are mainly used to predict stability risks that may occur during driving, especially safety issues such as rollover and loss of control. The safety of the current driving state is judged by analyzing data such as driver behavior, vehicle speed, acceleration / braking characteristics, and steering operations. By obtaining historical driving parameters, including driving speed, braking and steering force and frequency, and environmental factors. Data preprocessing is performed on historical driving parameters, including data cleaning, normalization or standardization, feature extraction, etc. By analyzing the correlation between the characteristics of historical driving parameters and target variables (such as stability risk coefficients), the most influential features are selected, including speed, speed change amplitude, braking force and frequency, and steering force and frequency. If the number of features is too large, the data is reduced in dimension by methods such as principal component analysis (PCA) to reduce redundant data and improve model training efficiency. According to the nature of the problem and data characteristics, a suitable risk prediction model is selected, including logistic regression, decision tree, random forest, support vector machine, neural network, etc.

[0035] In order to predict the vehicle stability risk, the random forest model is selected because it can automatically select important features and has strong expression ability for nonlinear relationships. It is especially suitable for processing high-dimensional data and is not easy to overfit. The collected data is divided into a training set and a validation set. Random forest is an ensemble learning method based on the decision tree model. By building multiple decision trees and integrating the prediction results of each decision tree, the overall prediction accuracy is improved. Each tree is generated by randomly sampling the training data, which helps to reduce overfitting. The training set is used to train the model, and the validation set is used to evaluate the model performance. The common ratio is 80% of the data for training and 20% for validation. The training set is learned using the selected algorithm (such as random forest) and the model parameters are adjusted to maximize the prediction accuracy. In order to ensure the representativeness of the training data and prevent the bias caused by the accidental data division, cross validation is usually used. For example, k-fold cross validation divides the data into k subsets, selects one subset as the validation set each time, and the other k-1 subsets as the training set, and repeats the training process k times. Finally, the model performance is evaluated by calculating the average of each validation result.

[0036] The random forest algorithm learns based on the features and labels (such as stability risk coefficient) in the training data. The construction steps of each tree are as follows: randomly select a part of the samples in the training set for training. Each time the decision tree is split, a specific number of features are randomly selected for division, which helps to increase the diversity between trees and avoid overfitting. Each tree is trained by continuously splitting nodes until the stopping condition is met (such as the maximum depth of the tree, the number of samples in the node is less than a certain threshold, etc.). After the training is completed, the final stability risk prediction is obtained by integrating the prediction results of all trees. After the training is completed, the model needs to be evaluated on the validation set to calculate the accuracy, precision, etc., or calculate the mean square error with the actual value. According to the evaluation results, adjust the hyperparameters in the random forest (such as the number of trees, the maximum depth of the tree, the minimum number of samples per tree, etc.), or add new features, etc., to optimize the model performance and obtain the final risk prediction model. Assume that after evaluation, the accuracy of the model on the validation set is 85%, but the prediction is inaccurate in some extreme cases (such as sharp turns). Therefore, add more data on extreme driving behavior, or choose a different algorithm for further optimization.

[0037] The user's driving operation characteristics and the speed value and speed change amplitude in the speed adaptation interval are used as input variables. The risk prediction model will combine all input variables (including speed, speed change amplitude, braking force and frequency, steering force and frequency, etc.) to evaluate the risk and generate a stability risk coefficient, which indicates the possibility of the vehicle losing control or rolling over in the current driving situation. By analyzing the braking and steering behaviors of different drivers, a personalized control strategy is provided for each driver, thereby achieving more accurate risk prediction and safety protection, timely predicting potential stability risks during driving, and taking measures in advance, such as adjusting vehicle control parameters, to improve vehicle safety.

[0038] Furthermore, the present application also includes the following steps: The operation duration of braking and steering is obtained; according to the braking and steering strength, the operation intensity is obtained by using the operation strength and operation duration of each braking or steering; according to the correspondence between the operation intensity and the frequency of braking and steering, the operation intensity frequency analysis is performed to obtain the operation frequencies of different operation intensities; according to the frequency discreteness of the operation frequencies of different operation intensities, the behavioral characteristics are evaluated to obtain the user's driving operation characteristics.

[0039] Specifically, by monitoring the pressure change of the brake pedal or the response time of the brake system, the time from the start to the end of the braking is recorded to obtain the continuous operation time of the braking. Similarly, the angle sensor or steering force sensor is used to detect the time when the driver turns the steering wheel, and the time from the start to the end of the steering is recorded to obtain the continuous operation time of the steering. According to the force of braking and steering, the operation intensity is obtained by combining the operation force and operation duration of each braking or steering. Operation intensity refers to the comprehensive intensity of braking or steering operation, which is usually determined by two factors: operation force and operation duration. The greater the operation intensity, the more significant the impact of the operation on the vehicle. For braking operation, the operation intensity can be calculated by braking force × braking duration; for steering operation, the operation intensity can be calculated by steering force × steering duration. If the user applies a large force in a braking operation and it lasts for a long time, the overall intensity of this operation will be higher.

[0040] Operation frequency refers to the number of operations that occur per unit time. By analyzing the frequency of operations of different intensities, we can obtain the distribution of operation intensity and frequency. Calculate the frequency of operations of different intensities per unit time. For example, count the intensity of each operation within a certain period of time, classify the operation intensity into different intervals, and then calculate the operation frequency in each interval. For each operation intensity, record the user's braking and steering frequency to obtain the operation frequency under different operation intensities.

[0041] The frequency standard deviation formula is used to calculate the average value of the operation frequency of different operation intensities, and then the frequency standard deviation is calculated to obtain the intensity standard deviation, which reflects the fluctuation of the driver's operation intensity. The intensity standard deviation is normalized, and the normalized frequency coefficient of variation is obtained by calculating the ratio of the intensity standard deviation to the average value of the operation frequency, which is used to measure the relative discreteness of the data. According to the frequency discreteness, the driving behavior is divided into different levels, such as low discreteness level, medium discreteness level, and high discreteness level. According to the feature evaluation level, the driver's driving style is classified, such as conservative driving, smooth driving, and aggressive driving, which correspond to the feature evaluation level respectively. The feature evaluation level is a quantitative evaluation of the user's operation behavior characteristics, while the user's driving operation characteristics are a qualitative description of the user's driving style. Conservative driving usually means that the user is more cautious in driving, tends to accelerate and decelerate slowly, and rarely makes sharp turns; smooth driving means that the user's operation is stable, the acceleration and deceleration are uniform, and the turning is smooth; aggressive driving means that the user's operation is bolder, tends to accelerate and decelerate quickly, and makes sharp turns frequently. By analyzing the operation intensity and frequency discreteness, the system can accurately identify the driver's driving style (conservative, smooth, aggressive), promptly identify the driver's aggressive driving behavior, and adopt corresponding control strategies to reduce safety risks caused by excessive driver operation (such as rollover, loss of control, etc.).

[0042] Furthermore, the present application also includes the following steps: According to the operation frequencies of the different operation intensities, the intensity standard deviation is calculated by the frequency standard deviation formula; based on the intensity standard deviation, normalization is performed to obtain the frequency discreteness, and the frequency discreteness is represented by the normalized frequency variation coefficient; according to the frequency discreteness, feature evaluation levels are divided, and the feature evaluation levels are determined by matching the frequency discreteness with preset level discrete interval values, and the feature evaluation levels include low discreteness level, medium discreteness level, and high discreteness level; according to the feature evaluation levels, the user's driving operation characteristics are determined, and the user's driving operation characteristics include conservative driving, smooth driving, and aggressive driving, which correspond to the feature evaluation levels respectively.

[0043] Specifically, the operation frequencies of different intensity levels are obtained, which indicates the operation frequencies of the driver for different operation intensities (such as braking and steering) within a certain period of time. According to the operation frequencies of different operation intensities, the frequency standard deviation formula is used to calculate the intensity standard deviation. The standard deviation is an indicator to measure the degree to which a set of data deviates from its average value, which is used to measure the fluctuation amplitude of the operation intensity frequency, that is, the stability of the driver's operation intensity. If the standard deviation is large, it means that the operation intensity changes greatly and the driving behavior may be more intense; if the standard deviation is small, it indicates that the operation intensity is relatively stable. Next, the intensity standard deviation is normalized, and the normalized frequency variation coefficient, that is, the frequency discreteness, is obtained by calculating the ratio of the intensity standard deviation and the average value of the operation frequency, which reflects the discreteness of the operation frequency. If the frequency discreteness is large, it means that the driver's operation behavior is more aggressive and frequent violent operations are performed; if it is small, it means that the operation frequency is relatively stable and the driver's behavior is relatively conservative. For example, the frequencies of five operations recorded in a period of time (unit: times / minute) are 1.2, 1.5, 1.3, 1.6, and 1.4 respectively. The average frequency is calculated to be 1.4 times / minute. The average of the square deviations is then calculated to be [(1.2-1.4)²+(1.5-1.4)²+(1.3-1.4)²+(1.6-1.4)²+(1.4-1.4)²] / 5=0.02. The intensity standard deviation is 0.1414, and the frequency discreteness is 0.101. The operation frequency refers to the number of times the driver completes a certain operation (such as braking or steering) in a unit of time, reflecting the driver's operational activity. The frequency standard deviation is used to measure the degree of fluctuation of the operation frequency. The normalized frequency coefficient of variation is the ratio of the standard deviation to the mean, which is used to measure the relative discreteness of the data. It can eliminate the influence of different means. The purpose is to convert the intensity standard deviation into a dimensionless indicator so that the discreteness between different data sets can be compared.

[0044] The driver's operating characteristics are divided into different evaluation levels according to the frequency discreteness. The characteristic evaluation level is a quantitative evaluation of the user's operating behavior characteristics, reflecting the stability and consistency of the user's operating frequency under different operating intensities. The preset level discrete interval value is a set of predefined intervals used to map the frequency discreteness index to different characteristic evaluation levels. The preset level discrete interval value can be adjusted according to specific requirements and actual application scenarios. The frequency discrete type is divided into characteristic evaluation levels according to the preset level discrete interval value, including low discreteness level, medium discreteness level, and high discreteness level. The low discreteness level indicates that the user's operating frequency under different operating intensities changes little, and the operating behavior is more conservative; the medium discreteness level indicates that the user's operating frequency under different operating intensities has certain changes, and the operating behavior is relatively stable; the high discreteness level indicates that the user's operating frequency under different operating intensities changes greatly, and the operating behavior is relatively unstable.

[0045] According to the characteristic evaluation level, the user's driving operation characteristics, that is, the user's driving style, are determined, including conservative driving, smooth driving, and aggressive driving, which correspond to the characteristic evaluation level respectively. Low discrete levels correspond to conservative driving, medium discrete levels correspond to smooth driving, and high discrete levels correspond to aggressive driving. By analyzing the fluctuation of the operation frequency, the driver's driving style can be accurately distinguished, such as conservative, smooth or aggressive driving. The electric tricycle controller can adjust the control strategy according to the driver's driving habits. By monitoring the driver's operating behavior in real time, the controller automatically adjusts the vehicle's response when it finds that the driver's operation is too aggressive to avoid possible safety hazards (such as loss of control, rollover, etc.).

[0046] Furthermore, the present application also includes the following steps: The frequency standard deviation formula is: ; The frequency discreteness = ,in, is the frequency standard deviation, F i The operating frequency for each intensity level, is the mean of the operation frequency, and N is the number of operation intensity levels.

[0047] Specifically, the frequency standard deviation formula is expressed as ,in, is the frequency standard deviation, which indicates the discrete degree or fluctuation amplitude of the data; Fi is the operation frequency of each intensity level, which indicates the operation frequency corresponding to the i-th operation intensity level (such as low intensity, medium intensity, high intensity, etc.), reflecting the frequency of a specific operation intensity (such as braking or steering) occurring within a certain period of time; is the average value of the operation frequency, that is, the average value of all operation intensity frequencies. The operation frequencies of N intensity levels are added and divided by N to obtain the average value; N is the number of operation intensity levels, that is, the number of different operation intensity categories considered in the analysis process. First, the deviation between each operation intensity frequency and the mean is calculated, which reflects the difference between the operation frequency and the mean. Then, the square of each deviation is calculated to avoid the influence of negative values ​​on the calculation and to amplify the influence of large deviations. The square of the deviation is to emphasize extreme values, especially when the driver's behavior is more intense, frequent operations will be more marked. By summing the squares of all deviations and dividing them by the total number of operation intensity levels, the average of the squared deviations is obtained, which is an indicator of the fluctuation of the operation frequency. Taking the square root of the average squared deviation value, the standard deviation is obtained to measure the volatility of the operation frequency. The larger the standard deviation, the greater the change in operation intensity and the more intense the driver's behavior; the smaller the standard deviation, the more stable the driver's operation.

[0048] Frequency Discreteness = , which is the ratio of the standard deviation to the mean, is used to indicate the relative discreteness of the data. Frequency discreteness is usually used to compare the degree of discreteness of different data sets, especially when the mean values ​​of the data differ greatly, the coefficient of variation can more effectively reflect the discreteness. The standard deviation is used to measure the degree of fluctuation in the frequency of the driver's operation intensity. A larger standard deviation indicates that the operation frequency fluctuates more, which means that the driving behavior is more intense. Frequency discreteness normalizes the ratio of the standard deviation to the mean, so that even if the mean value of the operation frequency is small, it can effectively measure the discreteness of the operation intensity. A larger coefficient of variation indicates that the operation frequency fluctuates more and the driver's behavior is more aggressive.

[0049] Further, as attached Figure 2 As shown, step 4 of this application includes: A control influence relationship between steering stability risk, braking stability risk and controller strategy parameters is established; an adjustment relationship between a stability risk coefficient and the controller strategy parameters is established, wherein the adjustment relationship is obtained by fitting test data using historical data with a stability threshold as a target, and is used to characterize a parameter adjustment amplitude that matches the stability risk coefficient; the parameter control coefficient in the control influence relationship is adjusted using the adjustment relationship to obtain the optimized adjustment coefficient.

[0050] Specifically, determine which controller strategy parameters can be adjusted to address stability issues in braking and steering. Steering stability risk is an indicator for evaluating the impact of steering operations on vehicle stability. It usually reflects the risk of rollover or loss of control that may occur when the vehicle is turning, and is related to factors such as steering force, steering angle, steering frequency and driving speed. Similar to steering stability risk, braking stability risk assesses the impact of the driver's braking operation on vehicle stability. Too sharp braking or frequent braking operations may cause the vehicle to lose control or roll over, especially when driving at high speeds. Controller strategy parameters refer to the various parameters required by the vehicle controller when performing braking and steering, such as braking intensity, steering angle, acceleration limit, stability control intervention intensity, etc., which directly affect the vehicle's driving stability, response speed and driving experience.

[0051] By optimizing the braking force distribution, the braking force distribution of the front and rear wheels is adjusted according to the user's braking behavior characteristics to optimize braking efficiency and stability. For example, if the user frequently performs emergency braking, the controller can increase the rear wheel braking force to reduce the risk of locking the front wheel. By adjusting the steering assist strength, the strength of the steering assist system is adjusted according to the user's steering behavior characteristics. If the user tends to turn frequently or sharply, the controller can increase the steering assist strength to provide a more stable steering experience. By controlling the power output, the output power of the motor is adjusted according to the current speed and the user's acceleration behavior. At high speeds or cornering, the controller can limit the power output to prevent stability problems caused by excessive acceleration.

[0052] The stability threshold refers to the critical value of the stability risk factor that is considered safe under specific driving conditions. When this threshold is exceeded, it means that the vehicle may face a greater risk of loss of control, and it is necessary to enhance the stability of the vehicle by adjusting the controller strategy parameters. Test data fitting is to test a large amount of historical data to fit the relationship between the stability risk factor and the controller strategy parameter adjustment. Defining a stability threshold is a key indicator for measuring vehicle safety. When the stability risk factor of the vehicle exceeds the threshold, it is considered that the vehicle has entered a dangerous driving state (such as steering operations at different speeds, sudden braking, sharp turns, or high-speed driving, etc.), and measures need to be taken to avoid accidents. Through the test data of the vehicle at different speeds, braking forces, and steering forces, the stability risk factor and the corresponding controller strategy parameters (such as braking intensity, steering angle, etc.) in each case are recorded.

[0053] Statistical methods (such as linear regression, nonlinear regression, machine learning, etc.) are used to fit historical data to obtain the mathematical relationship between the stability risk factor and the controller strategy parameters. For example, if the risk factor is high, the braking strategy may need to be adjusted to reduce the braking response speed; if the risk factor is low, the sensitivity of the vehicle response can be increased. If the relationship between the stability risk factor and the controller parameters is linear, the best linear equation is found by fitting the data points through linear regression. If linear regression cannot accurately fit the data, a nonlinear regression method is used to obtain a more accurate relationship. When the amount of data is large, machine learning methods (such as decision trees, support vector machines, neural networks, etc.) are used to establish more complex adjustment relationship models. The adjustment relationship is used to characterize the parameter adjustment amplitude that matches the stability risk factor, which means that the controller strategy parameters need to be adjusted according to the size of the stability risk factor.

[0054] The control influence relationship refers to the connection between the controller strategy parameters and the vehicle's stability, response and other performance. The controller parameters (such as steering angle, braking force, etc.) directly affect the dynamic behavior and stability of the vehicle. The parameter control coefficient is the weight coefficient of each control parameter of the controller (such as steering, acceleration, braking, etc.) in the control influence relationship, which determines the degree of influence of each control parameter on the vehicle behavior. According to the established adjustment relationship, the parameter control coefficient in the control influence relationship is adjusted to obtain a new coefficient, namely the optimization adjustment coefficient. The optimization adjustment coefficient is used to adjust the controller parameters in real time to ensure that the vehicle maintains high stability under extreme conditions such as high-speed driving, sharp turns, and sudden braking. The controller continuously adjusts the controller strategy parameters based on the real-time monitored stability risk coefficient and historical test data. For example, when the stability risk coefficient is low, the controller will increase steering flexibility and improve acceleration response; when the stability risk coefficient is high, the controller will increase braking intensity, reduce steering angle, etc., so as to ensure that the vehicle will not lose control due to excessively fast response. According to the stability risk coefficient, the controller strategy parameters are dynamically adjusted to ensure that the vehicle can maintain optimal stability under different driving conditions. Test data is continuously collected and model optimization is performed. The controller can adaptively adjust the controller parameters according to the driver's different operations and environmental conditions to adapt to more diversified driving needs.

[0055] Furthermore, the present application also includes the following steps: A kinetic energy recovery strategy is obtained, and a steering and braking correlation analysis is performed on the kinetic energy recovery strategy to determine an associated kinetic energy recovery strategy; an influence relationship between the associated kinetic energy recovery strategy and the user's driving operation characteristics is established to obtain a recovery control strategy adjustment coefficient; and the control strategy of the kinetic energy recovery strategy is adjusted according to a risk stability coefficient of the current speed adaptation interval in combination with the recovery control strategy adjustment coefficient.

[0056] Specifically, kinetic energy recovery is a technology that converts the kinetic energy of a vehicle during braking or deceleration into electrical energy for storage. The kinetic energy recovery strategy refers to the specific scheme for controlling the operation of the kinetic energy recovery system, including recovery efficiency, recovery mode, trigger conditions, etc. The kinetic energy recovery strategy is analyzed for its steering and braking correlation, and the impact of the vehicle's steering or braking on the efficiency and effect of kinetic energy recovery is studied, especially how these operations change the vehicle's kinetic energy distribution and recovery potential. For example, the greater the braking force, the more kinetic energy may be recovered; while when turning, due to the inertia distribution of the vehicle, kinetic energy recovery may be subject to certain restrictions. During braking, the kinetic energy recovery system needs to work in conjunction with the braking system. When the risk is low, the braking and kinetic energy recovery forces are linearly superimposed (for example, the recovery system works at an efficiency of 80%, and the mechanical brake only bears 20% of the deceleration demand); strengthen the recovery force, reduce the intervention of mechanical braking, and improve the energy recovery efficiency. When the risk is medium, optimize the ratio of kinetic energy recovery to mechanical braking, reduce the strength of kinetic energy recovery, and avoid vehicle instability due to excessive deceleration during sudden braking; when the risk is high, kinetic energy recovery may aggravate vehicle deceleration and instability, so its strength needs to be reduced to a minimum or even disabled, relying entirely on mechanical braking to avoid the risk of skidding or rollover.

[0057] During vehicle steering, especially in high-speed or sharp turn scenarios, the adjustment of kinetic energy recovery strength is directly related to the lateral stability of the vehicle. When the risk is low, kinetic energy recovery has little effect on vehicle stability, and the kinetic energy recovery strength is kept unchanged (such as the recovery efficiency remains at 70%). When the risk is medium, the vehicle may have a certain lateral instability during high-speed or medium-speed turns. The kinetic energy recovery strength needs to be adjusted appropriately to avoid an increase in lateral acceleration, and the recovery strength is appropriately reduced to maintain smooth steering of the vehicle. When the risk is high, the vehicle is in a sharp turn or high-speed turning state, and kinetic energy recovery may significantly affect lateral stability. Kinetic energy recovery needs to be temporarily disabled, and mechanical braking and chassis stability control systems are completely relied upon to avoid instability or rollover caused by the recovery system.

[0058] The user's driving operation characteristics (such as conservative driving, smooth driving, and aggressive driving) are associated with the recovery efficiency and mode of the associated kinetic energy recovery strategy to establish an influence relationship. Through historical driving data and driving behavior characteristics, a linear regression or machine learning model is used to fit the relationship between user operation characteristics and kinetic energy recovery efficiency, and the recovery control strategy adjustment coefficient is obtained, which is used to dynamically adjust the working mode of the kinetic energy recovery system. The actual operating data of the kinetic energy recovery system under different driving modes is collected through experiments to establish a kinetic energy recovery strategy model. The user's driving operation characteristics (conservative, smooth, and aggressive) are used as the input variables of the kinetic energy recovery strategy. Three driving scenarios (conservative, smooth, and aggressive) are set up to record the vehicle deceleration stability, recovery efficiency, and user satisfaction data under each driving mode. The impact of different driving characteristics on the kinetic energy recovery strategy is quantified, and the relationship model between the recovery ratio and driving characteristics is obtained.

[0059] Input the current user driving operation characteristics into the relationship model and calculate the current recovery control strategy adjustment coefficient. Match the speed range according to the real-time driving speed to obtain the risk coefficient. Dynamically adjust the kinetic energy recovery strength according to the two core inputs (risk stability coefficient and adjustment coefficient), dynamically change the kinetic energy recovery strength, recovery ratio or trigger conditions to ensure vehicle stability and energy recovery efficiency. In high-risk scenarios such as high speed or sharp turns, the kinetic energy recovery strength is dynamically reduced to avoid vehicle instability; in low-risk scenarios (such as low-speed driving or gliding), the kinetic energy recovery strength is enhanced to improve energy utilization and extend the cruising range. Adjusting the kinetic energy recovery strategy according to the risk stability coefficient and the recovery control strategy adjustment coefficient helps to improve energy utilization efficiency while ensuring driving stability, thereby extending the cruising range of the electric tricycle.

[0060] In summary, the controller strategy optimization method for user behavior analysis provided by this application has the following technical effects: By collecting user driving parameters, the user driving parameters include driving speed, braking and steering strength and frequency; according to the driving speed, the speed risk level and speed change characteristics are identified and segmented to determine the speed adaptive interval, and the speed adaptive interval includes the speed risk level and speed change amplitude; according to the braking and steering strength and frequency, the speed adaptive interval is respectively evaluated for steering and braking risk to obtain the stability risk coefficient; according to the stability risk coefficient, the controller strategy matching analysis is performed to determine the optimization adjustment coefficient, and the optimization adjustment coefficient is added to the speed adaptive interval to generate an interval optimization adjustment label; according to the monitoring of the current driving speed and the speed adaptive interval, the optimization adjustment coefficient corresponding to the adaptive interval is obtained, and the controller strategy is adjusted according to the optimization adjustment coefficient. In other words, by collecting user driving parameters, the user's driving habits and behavior patterns are fully understood, the driving state of the vehicle is evaluated in real time, the risk of braking and steering during driving is evaluated, the stability coefficient is determined, the control strategy that best suits the current driving state is selected, and the controller strategy is dynamically matched and adjusted to adapt to the user's driving characteristics and vehicle state, thereby improving the safety and stability of vehicle driving.

[0061] 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.

[0062] 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 controller strategy optimization method for user behavior analysis, characterized in that: include: Collecting user driving parameters, including driving speed, braking and steering strength and frequency; According to the driving speed, the speed risk level and the speed change feature are identified and segmented to determine a speed adaptive interval, wherein the speed adaptive interval includes the speed risk level and the speed change amplitude; According to the braking and steering forces and frequencies, respectively, the steering and braking risk assessment is performed on the speed adaptation interval to obtain a stability risk coefficient; Performing controller strategy matching analysis according to the stability risk coefficient, determining an optimization adjustment coefficient, and adding the optimization adjustment coefficient to the speed adaptation interval to generate an interval optimization adjustment label; The current driving speed is monitored and matched with the speed adaptive interval to obtain an optimization adjustment coefficient corresponding to the adaptive interval, and the controller strategy is adjusted according to the optimization adjustment coefficient.

2. A controller strategy optimization method for user behavior analysis according to claim 1, characterized in that: According to the driving speed, the speed risk level and speed change characteristics are identified and segmented, and the speed adaptation interval is determined, including: Acquiring vehicle speed test data, wherein the vehicle speed test data has a vehicle model; According to the vehicle speed test data, speed stability risk levels are divided and speed risk level intervals are determined, including speed risk intervals and speed change amplitude intervals; Calculate the speed change amplitude according to the driving speed to obtain the speed time series change amplitude; The speed risk interval and the speed change amplitude interval are used to match and map the driving speed and the speed time series change amplitude to obtain multiple speed risk level intervals; The speed adaptive interval is constructed according to the multiple speed risk level intervals.

3. The controller strategy optimization method for user behavior analysis according to claim 1, characterized in that: According to the braking and steering forces and frequencies, the steering and braking risk assessment is performed on the speed adaptation interval to obtain a stability risk coefficient, including: Analyze the user's braking and steering behavior characteristics according to the braking and steering strength and frequency, and determine the user's driving operation characteristics; The user's driving operation characteristics and the speed value and speed change amplitude of the speed adaptive interval are used as input variables, and risk prediction is performed on each speed adaptive interval respectively through a risk prediction model to obtain the stability risk coefficient.

4. A controller strategy optimization method for user behavior analysis as claimed in claim 3, characterized in that: According to the braking and steering strength and frequency, the user's braking and steering behavior characteristics are analyzed to determine the user's driving operation characteristics, including: Get the duration of braking and steering operations; According to the force of braking and steering, the operation intensity is obtained by using the operation force and operation duration of each braking or steering; According to the correspondence between the operation intensity and the frequency of braking and steering, an operation intensity frequency analysis is performed to obtain operation frequencies of different operation intensities; According to the frequency discreteness of the operation frequencies of the different operation intensities, a behavior characteristic evaluation is performed to obtain the user driving operation characteristics.

5. A controller strategy optimization method for user behavior analysis as claimed in claim 4, characterized in that: According to the frequency discreteness of the operation frequencies of the different operation intensities, a behavior characteristic evaluation is performed to obtain the user driving operation characteristics, including: According to the operation frequencies of the different operation intensities, the intensity standard deviation is calculated by using the frequency standard deviation formula; Performing normalization processing based on the intensity standard deviation to obtain the frequency discreteness, wherein the frequency discreteness is represented by a normalized frequency variation coefficient; Divide the characteristic evaluation level according to the frequency discreteness, the characteristic evaluation level is determined by matching the frequency discreteness with a preset discrete interval value, and the characteristic evaluation level includes a low discreteness level, a medium discreteness level, and a high discreteness level; The user driving operation characteristics are determined according to the characteristic evaluation level, and the user driving operation characteristics include conservative driving, smooth driving, and aggressive driving, which correspond to the characteristic evaluation levels respectively.

6. A controller strategy optimization method for user behavior analysis as claimed in claim 5, characterized in that: The frequency standard deviation formula is: ; The frequency discreteness = ,in, is the frequency standard deviation, The operating frequency for each intensity level, is the mean of the operation frequency, and N is the number of operation intensity levels.

7. The controller strategy optimization method for user behavior analysis according to claim 1, characterized in that: According to the stability risk coefficient, controller strategy matching analysis is performed to determine the optimization adjustment coefficient, including: Establish the control influence relationship between steering stability risk, braking stability risk and controller strategy parameters; Establishing an adjustment relationship between the stability risk coefficient and the controller strategy parameter, wherein the adjustment relationship is obtained by fitting the test data using historical data with the stability threshold as the target, and is used to characterize the parameter adjustment range that matches the stability risk coefficient; The parameter control coefficient in the control influence relationship is adjusted using the adjustment relationship to obtain the optimized adjustment coefficient.

8. The controller strategy optimization method for user behavior analysis according to claim 5, characterized in that: Also includes: Obtaining a kinetic energy recovery strategy, performing steering and braking correlation analysis on the kinetic energy recovery strategy, and determining a correlated kinetic energy recovery strategy; Establishing the influence relationship between the associated kinetic energy recovery strategy and the user's driving operation characteristics, and obtaining the recovery control strategy adjustment coefficient; The kinetic energy recovery strategy is adjusted according to the risk stability coefficient of the current speed adaptation interval in combination with the recovery control strategy adjustment coefficient.

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