Intelligent control platform for retractable rear spoiler structure

Abstract

An intelligent control platform for a retractable rear spoiler structure, comprising: a monitoring end (10), a control end (20), and a service center (30). The monitoring end (10) collects airflow velocity information and airflow angle information. The control end (20) uploads movement velocity information, rear spoiler structure extension / retraction length information, rear spoiler structure azimuth angle information, and rear spoiler structure pitch angle information. The service center (30) comprises: a stability prediction unit (31), a resistance prediction unit (32), a control optimization unit (33), and a data exchange unit (34), wherein the stability prediction unit (31) predicts a running stability coefficient, the resistance prediction unit (32) predicts a running resistance coefficient, the control optimization unit (33) optimizes the movement velocity information, the rear spoiler structure extension / retraction length information, the rear spoiler structure azimuth angle information, and the rear spoiler structure pitch angle information to generate a retractable rear spoiler structure control scheme, and the data exchange unit (34) performs retractable rear spoiler structure control. The intelligent control platform for a retractable rear spoiler structure can solve the technical problem of untimely and inaccurate rear spoiler adjustment, thereby achieving the technical objective of real-time optimized control of retractable rear spoiler structures, and achieving the technical effect of improving the aerodynamic performance and running stability of vehicles.

Description

Intelligent control platform for retractable spoiler structure TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control, and in particular to an intelligent control platform for a retractable spoiler structure. BACKGROUND

[0002] In modern automotive aerodynamic optimization, the application of retractable spoiler structures has become increasingly common. The spoiler structure adjusts the air flow at the rear of the vehicle to reduce drag and increase downforce, thereby improving the stability and handling of the vehicle. However, existing retractable spoiler control systems still have some deficiencies in dealing with complex and variable driving conditions, and need to be improved.

[0003] Currently, existing technologies mainly rely on preset spoiler control strategies, which are usually based on fixed driving conditions or simple sensor feedback. However, the air flow speed and angle in the actual driving environment varies greatly, and the motion state of the vehicle also changes constantly. The preset strategy is difficult to cope with these dynamic changes, resulting in lag or inaccuracy in spoiler adjustment. In addition, existing systems lack comprehensive consideration of driving stability and drag, often focusing only on optimizing one aspect, and cannot achieve overall performance improvement. For complex driving environments such as high-speed driving, sharp turns, or sudden strong winds, the response speed and adjustment accuracy of existing control systems cannot meet the requirements, resulting in decreased vehicle stability and increased driving risk. In addition, existing technologies also have defects in data processing and transmission. Due to the timeliness and accuracy of data transmission, the spoiler control system cannot adjust the spoiler structure in real time, resulting in deviations between actual control effect and expectation. This situation is particularly evident at high speeds, where air flow changes rapidly and spoiler adjustment requires higher response speed and accuracy. However, due to the lag in data interaction, existing systems often cannot meet this requirement.

[0004] In summary, the existing technology has the technical problem of tail wing adjustment not timely and inaccurate due to the limitations of preset control strategy and data transmission lag, which further affects the aerodynamic performance and driving stability of the vehicle. SUMMARY

[0005] The purpose of the present application is to provide an intelligent control platform for a retractable spoiler structure to solve the technical problem of tail wing adjustment not timely and inaccurate due to the limitations of preset control strategy and data transmission lag in existing technology, which further affects the aerodynamic performance and driving stability of the vehicle.

[0006] In view of the above problems, the present application provides an intelligent control platform for a retractable spoiler structure.

[0007] The application provides an intelligent control platform for a telescopic tail wing structure, which comprises a monitoring end, a control end and a service center.

[0008] The one or more technical solutions provided in the application have at least the following technical effects or advantages:

[0009] The monitoring end is used to collect airflow speed information and airflow angle information of a pre-driving area; the control end is used to upload motion speed information, tail wing structure telescopic length information, tail wing structure azimuth angle information and tail wing structure pitch angle information; the service center comprises a stability prediction unit, a resistance prediction unit and a control optimization unit; the stability prediction unit is used to process the airflow speed information, the airflow angle information, the motion speed information, the tail wing structure telescopic length information, the tail wing structure azimuth angle information and the tail wing structure pitch angle information, and predict a driving stability coefficient; the resistance prediction unit is used to process the airflow speed information, the airflow angle information, the motion speed information, the tail wing structure telescopic length information, the tail wing structure azimuth angle information and the tail wing structure pitch angle information, and predict a driving resistance coefficient; the control optimization unit is used to optimize the motion speed information, the tail wing structure telescopic length information, the tail wing structure azimuth angle information and the tail wing structure pitch angle information according to the driving stability coefficient and the driving resistance coefficient, and generate a telescopic tail wing structure control scheme; and a data interaction unit is used to send the telescopic tail wing structure control scheme to the control end for telescopic tail wing structure control.

[0010] The above description is only a summary of the technical solutions of the present application. In order to enable the technical means of the present application to be more clearly understood, and to be implemented according to the content of the description, and in order to enable the above and other purposes, characteristics and advantages of the present application to be more apparent and easy to understand, the following specific embodiments of the present application are described. It should be understood that the content described in this part 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 apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only exemplary, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.

[0012] Fig. 1 is a structural schematic diagram of the intelligent control platform for the retractable tail wing structure of the present application;

[0013] Fig. 2 is a flowchart of the stability prediction unit in the intelligent control platform for the retractable tail wing structure of the present application.

[0014] Explanation of reference signs:

[0015] Monitoring end 10, control end 20, service center 30, stability prediction unit 31, resistance prediction unit 32, control optimization unit 33, data interaction unit 34. DETAILED DESCRIPTION

[0016] The present application provides an intelligent control platform for a retractable tail wing structure, which solves the technical problem in the prior art that due to the limitations of the preset control strategy and the data transmission lag, the tail wing adjustment is not timely and accurate, which further affects the aerodynamic performance and driving stability of the vehicle. The technical goal of real-time optimization control of the retractable tail wing structure is achieved, and the technical effect of improving the aerodynamic performance and driving stability of the vehicle is achieved.

[0017] The technical solutions in the present application will be described clearly and completely below with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments of the present application. It should be understood that the present application is not limited by the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application. In addition, it should be noted that only parts related to the present application are shown in the drawings for convenience of description, not all.

[0018] Embodiment, please refer to attached drawing 1, the application provides an intelligent control platform for telescopic tail wing structure, which specifically includes:

[0019] The monitoring end 10 is used to collect airflow speed information and airflow angle information of the pre-driving area.

[0020] Specifically, the main function of the monitoring end 10 is to collect airflow speed information and airflow angle information of the pre-driving area. The monitoring end 10 monitors and records the airflow changes in front of the vehicle in real time through sensors and data acquisition systems. The airflow changes are used for the adjustment and control of the tail wing structure, because the speed and angle of the airflow will directly affect the stability and handling performance of the vehicle. Through continuous monitoring, it is ensured that the tail wing structure can adapt to the airflow changes in different driving environments, thereby improving the driving safety and stability of the vehicle.

[0021] The control end 20 is used to upload motion speed information, tail wing structure telescopic length information, tail wing structure azimuth angle information and tail wing structure pitch angle information.

[0022] Specifically, the control end 20 is responsible for processing and uploading multiple key information, including vehicle motion speed information, tail wing structure telescopic length information, tail wing structure azimuth angle information and tail wing structure pitch angle information. The motion speed information refers to the driving speed data of the vehicle at a certain time point. It is collected by the speed sensor on the vehicle, which can reflect the change of the driving speed of the vehicle in real time. The motion speed information is crucial for the control of the tail wing structure, because the adjustment of the tail wing needs to be dynamically adjusted according to the speed of the vehicle to ensure the optimization of the aerodynamic performance. The tail wing structure telescopic length information refers to the extension or contraction length of the tail wing in different states. It is collected by the position sensor installed on the tail wing structure, which can reflect the specific telescopic state of the tail wing during operation. The accurate measurement and control of the tail wing structure telescopic length information helps to adjust the aerodynamic effect of the tail wing under different speed and airflow conditions, thereby improving the stability and handling of the vehicle. The tail wing structure azimuth angle information refers to the rotation angle of the tail wing relative to the longitudinal axis of the vehicle. It is measured by the azimuth angle sensor, which can accurately reflect the angle change of the tail wing in different operating states. By controlling the azimuth angle of the tail wing, the aerodynamic characteristics of the tail wing are optimized, so that the stability and efficiency of the vehicle are maintained under different driving conditions. The tail wing structure pitch angle information refers to the inclination angle of the tail wing relative to the horizontal plane. It is obtained by the pitch angle sensor, which can accurately reflect the inclination angle of the tail wing in different operating states. The pitch angle adjustment of the tail wing has a direct impact on the aerodynamic effect of the vehicle in the up-down direction, and accurate pitch angle control can improve the handling performance and driving safety of the vehicle.

[0023] The service center 30 includes:

[0024] A stability prediction unit 31 is configured to process the airflow speed information, the airflow angle information, the motion speed information, the tail wing structure extension length information, the tail wing structure azimuth angle information, and the tail wing structure pitch angle information to predict a driving stability coefficient.

[0025] Specifically, the stability prediction unit 31 is configured to process a variety of information, including airflow speed information, airflow angle information, motion speed information, tail wing structure extension length information, tail wing structure azimuth angle information, and tail wing structure pitch angle information, to predict a driving stability coefficient of the vehicle. The airflow speed information and the airflow angle information provide data about the dynamic changes of the external environment, which are used to predict the behavior of the vehicle under different wind speeds and directions. The motion speed information reflects the current driving state of the vehicle, while the tail wing structure information provides the specific adjustment state of the tail wing and the position of the tail wing in space.

[0026] By processing real-time data and comparing with historical data, a driving stability coefficient reflecting the current driving environment and the state of the vehicle is generated, which helps the vehicle control system make more accurate adjustments, improves the stability and safety of the vehicle under various driving conditions, predicts the stability problems that the vehicle may encounter in advance, and makes corresponding adjustments before the problems occur, to ensure that the vehicle is always in the best state.

[0027] A resistance prediction unit 32 is configured to process the airflow speed information, the airflow angle information, the motion speed information, the tail wing structure extension length information, the tail wing structure azimuth angle information, and the tail wing structure pitch angle information to predict a driving resistance coefficient.

[0028] Specifically, the resistance prediction unit 32 is configured to process a variety of key information to predict the driving resistance coefficient, including airflow speed information, airflow angle information, motion speed information, tail wing structure telescopic length information, tail wing structure azimuth angle information, and tail wing structure pitch angle information. First, the airflow speed information and the airflow angle information provide detailed data about the aerodynamic environment around the vehicle, which in turn obtains the resistance exerted by the air on the vehicle, because the speed and angle of the airflow directly affect the way the air flows and the size of the resistance generated. By monitoring the airflow information in real time, the aerodynamic effect is more accurately predicted. The motion speed information reflects the current driving speed of the vehicle. The faster the vehicle drives, the greater the air resistance it experiences. Therefore, accurate motion speed information is an important basis for predicting the driving resistance coefficient. The tail wing structure telescopic length information, the tail wing structure azimuth angle information, and the tail wing structure pitch angle information describe the specific state of the tail wing structure, which determines the specific form and position of the tail wing under different driving conditions. The adjustment of the tail wing structure can significantly affect the aerodynamic characteristics, thereby affecting the overall resistance coefficient of the vehicle. Through the processing and analysis of multiple information, the optimal configuration of the tail wing structure under certain conditions is determined, so as to minimize the air resistance.

[0029] The control optimization unit 33 is configured to optimize the motion speed information, the tail wing structure telescopic length information, the tail wing structure azimuth angle information, and the tail wing structure pitch angle information according to the driving stability coefficient and the driving resistance coefficient, and generate a telescopic tail wing structure control scheme.

[0030] Specifically, the control optimization unit 33 is responsible for optimizing a variety of parameter information, including motion speed information, tail wing structure telescopic length information, tail wing structure azimuth angle information, and tail wing structure pitch angle information, according to the driving stability coefficient and the driving resistance coefficient, and generating an optimized telescopic tail wing structure control scheme.

[0031] During the optimization process, the driving stability coefficient is evaluated by the control optimization unit 33. A high stability coefficient means that the vehicle can maintain good balance and control during driving, reducing vibration and instability factors. On the other hand, the driving resistance coefficient reflects the air resistance experienced by the vehicle during driving. By reducing the driving resistance coefficient, the fuel efficiency and overall performance of the vehicle can be improved.

[0032] Through the control optimization unit, the specific parameters that need to be adjusted are determined through in-depth analysis of the driving stability coefficient and the driving resistance coefficient. The motion speed information is used to determine the current driving speed of the vehicle and to adjust it as needed to optimize the aerodynamic performance. The telescopic length, azimuth angle, and pitch angle information of the tail wing structure determine the specific configuration of the tail wing under different driving conditions. Through the precise control of multiple parameters, a telescopic tail wing structure control scheme is generated to optimize the driving performance of the vehicle.

[0033] The data interaction unit 34 is configured to send the retractable tail wing structure control scheme to the control end for controlling the retractable tail wing structure.

[0034] Specifically, the retractable tail wing structure control scheme generated by the control optimization unit is sent to the control end through the data interaction unit 34, so that the control scheme can be transmitted and executed in a timely and accurate manner, thereby realizing real-time control of the tail wing structure. The tail wing structure is dynamically adjusted according to the actual driving conditions to ensure that the vehicle is always in the best aerodynamic state. The retractable tail wing structure control scheme is sent to the control end through the data interaction unit 34, thereby ensuring the accuracy and timeliness of data transmission, and avoiding the influence of data transmission delay or error on the execution effect of the control scheme.

[0035] The intelligent control platform for the retractable tail wing structure achieves the technical goal of real-time optimization control of the retractable tail wing structure, and achieves the technical effect of improving the aerodynamic performance and driving stability of the vehicle.

[0036] Further, as shown in FIG. 2, the application also includes:

[0037] The shaking frequency prediction node is configured to process the airflow speed information, the airflow angle information, the movement speed information, the tail wing structure extension length information, the tail wing structure azimuth angle information, and the tail wing structure pitch angle information to predict the driving shaking frequency. The shaking amplitude prediction node is configured to process the airflow speed information, the airflow angle information, the movement speed information, the tail wing structure extension length information, the tail wing structure azimuth angle information, and the tail wing structure pitch angle information to predict the driving shaking amplitude. The stability prediction node is configured to process the driving shaking frequency and the driving shaking amplitude to generate the driving stability coefficient.

[0038] Specifically, the service center 30 includes a stability prediction unit 31, which is composed of a plurality of key nodes for processing and predicting different types of information. One node of the stability prediction unit 31 is a shaking frequency prediction node, which processes the airflow speed information, the airflow angle information, the movement speed information, the tail wing structure extension length information, the tail wing structure azimuth angle information, and the tail wing structure pitch angle information. Through comprehensive analysis of multiple data, the shaking frequency prediction node can predict the shaking frequency of the vehicle during driving, thereby understanding the dynamic response of the vehicle under different conditions, and can help to make corresponding adjustments in advance to improve the driving stability of the vehicle.

[0039] Then, another node is the jitter amplitude prediction node, which processes the same set of information, i.e., airflow speed information, airflow angle information, movement speed information, tail wing structure telescopic length information, tail wing structure azimuth angle information, and tail wing structure pitch angle information. The jitter amplitude prediction node can predict the jitter amplitude that may occur during vehicle driving by deep analysis of multiple data. Predicting the jitter amplitude helps to understand the response capability of the vehicle under different airflow conditions and speed states, so that measures can be taken to reduce the jitter amplitude and improve the ride comfort and safety of the vehicle.

[0040] Then, the last node is the stability prediction node, which processes the data generated by the other two nodes, i.e., driving jitter frequency and driving jitter amplitude. Through comprehensive analysis, the stability prediction node can generate a driving stability coefficient to measure the overall stability of the vehicle under the current driving conditions. After generating the driving stability coefficient, corresponding adjustments and optimizations are made according to the driving stability coefficient to ensure that the vehicle maintains optimal stability under various driving conditions.

[0041] Through the cooperative work of the jitter frequency prediction node, the jitter amplitude prediction node, and the stability prediction node in the service center 30, the stability prediction unit 31 processes and predicts key driving data to generate a driving stability coefficient, helping the vehicle to maintain optimal stability and safety under various driving conditions.

[0042] Further, the present application also includes:

[0043] The stability prediction node construction step includes: constructing a stability prediction function: , wherein S represents the driving stability coefficient, p and q are adjustment indexes representing the nonlinearity degree of the influence of the jitter amplitude and frequency on stability, A represents the jitter amplitude, Z represents the jitter frequency, k represents the first adjustment factor, and c is the second adjustment factor; configuring a first stability prediction loss function, wherein the first stability prediction loss function is used to calculate the mean of the driving stability coefficient prediction deviation every N times of training; configuring a second stability prediction loss function, wherein the second stability prediction loss function is used to calculate the proportion of the driving stability coefficient prediction deviation less than or equal to the stability coefficient prediction deviation threshold every N times of training; according to the main body model of the telescopic tail wing structure, collecting the driving jitter frequency record data set, the driving jitter amplitude record data set, and the driving stability coefficient identification data set, and configuring the stability prediction function; when the first stability prediction loss function is less than or equal to the stability coefficient prediction deviation threshold, and the second stability prediction loss function is less than or equal to the abnormal proportion threshold, the stability prediction node is generated.

[0044] Specifically, the construction step of the stability prediction node includes constructing a stability prediction function for characterizing the driving stability coefficient of the vehicle. Wherein, when the value of the jitter amplitude A or the jitter frequency Z is smaller, and other parameters remain unchanged, the value of the driving stability coefficient S is larger, then the driving stability is higher, on the contrary, then it is lower.

[0045] Then, in the process of constructing the stability prediction node, a first stability prediction loss function is configured. The first stability prediction loss function is used to calculate the mean deviation of the driving stability coefficient prediction after every N training, and then evaluate the accuracy and reliability of the prediction model, so as to make corresponding adjustment and optimization, and ensure the accuracy of the prediction result.

[0046] Next, a second stability prediction loss function is configured for constructing the stability prediction node. The second stability prediction loss function is used to calculate the proportion of the driving stability coefficient prediction deviation less than or equal to the stability coefficient prediction deviation threshold after every N training, which reflects the stability and consistency of the model prediction result, and helps to evaluate the performance of the model in actual application.

[0047] Next, before configuring the stability prediction function, relevant data sets need to be collected according to the specific deployment subject model of the telescopic tail wing structure, including driving jitter frequency record data set, driving jitter amplitude record data set and driving stability coefficient identification data set. Through the analysis of the collected data set, the stability prediction function can be reasonably configured, that is, the collected data set is input into the stability prediction function, and then the driving stability of the vehicle under different conditions can be more accurately reflected.

[0048] Then, when the value of the first stability prediction loss function is less than or equal to the set stability coefficient prediction deviation threshold, and the value of the second stability prediction loss function is less than or equal to the set abnormal proportion threshold, it means that the prediction accuracy and stability of the model have reached the requirements. Further, the stability prediction node is generated, so that the stability prediction node can play a role in actual application.

[0049] By constructing the prediction function, configuring the loss function, collecting the data set and generating the node, it is ensured that the stability prediction node can accurately and stably predict the driving stability of the vehicle.

[0050] Further, the present application also includes:

[0051] The main body model with a retractable tail wing structure is taken as a category retrieval condition, and then relevant data is screened and classified, ensuring the pertinence of data analysis and effectively processing the characteristics and behaviors of tail wing structures of different models.

[0052] Specifically, the main body model with a retractable tail wing structure is taken as a category retrieval condition, and then relevant data is screened and classified, ensuring the pertinence of data analysis and effectively processing the characteristics and behaviors of tail wing structures of different models.

[0053] Then, the airflow velocity information and the airflow angle information are normalized, and the data is standardized to construct uniform driving scene feature coordinates. Through normalization, the scale difference between different data sources is eliminated, so that the airflow velocity and angle information can be compared and analyzed in the same coordinate system. On this basis, the first fuzzy retrieval condition is constructed by combining the scene deviation Euclidean distance threshold, and then samples that meet the specific airflow scene feature are quickly retrieved from a large amount of data, thereby improving the efficiency and accuracy of analysis.

[0054] Then, the motion speed information, the tail wing structure extension length information, the tail wing structure azimuth angle information and the tail wing structure pitch angle information are normalized, and after normalization, they are integrated into a unified driving control feature coordinate, and then the changes of different control parameters are analyzed in a standardized coordinate system. The second fuzzy retrieval condition is constructed by combining the control deviation Euclidean distance threshold, which helps to quickly find samples that meet the specific control features in the data, supporting further analysis and prediction.

[0055] Next, according to the category retrieval condition, the first fuzzy retrieval condition and the second fuzzy retrieval condition, a large amount of historical data and currently collected data are used for large sample analysis to predict the driving resistance coefficient. Large sample analysis can provide more representative and accurate prediction results, ensuring that the prediction of the driving resistance coefficient has high reliability and accuracy under different driving conditions.

[0056] Through data normalization, feature coordinate construction, fuzzy retrieval condition setting and large sample analysis, accurate prediction of the driving resistance coefficient is achieved, improving the performance and stability of the vehicle under different driving conditions.

[0057] Further, the present application also includes:

[0058] collecting a sample data set that meets the category search condition, the first fuzzy search condition, and the second fuzzy search condition, wherein the sample data set includes data collection sources and travel resistance record data, and each data collection source and travel resistance record data correspond to each other; performing historical shared data analysis on the data collection sources to obtain a historical shared data accuracy rate; when the historical shared data accuracy rate is less than or equal to a data accuracy threshold, deleting the travel resistance record data from the sample data set; when the data collection sources are all verified, obtaining a trusted travel resistance record data set; and performing data fusion on the trusted travel resistance record data set to generate the travel resistance coefficient.

[0059] Specifically, in the execution step of the resistance prediction unit, a sample data set that meets the category search condition, the first fuzzy search condition, and the second fuzzy search condition is collected. The sample data set contains data collection sources and corresponding travel resistance record data, and each pair of data collection source and travel resistance record data corresponds to each other, ensuring the integrity and relevance of the sample data and providing a basis for subsequent analysis and prediction.

[0060] Then, by analyzing the accuracy of historical data, the reliability and value of data collection sources in the prediction process are evaluated, and the quality of data in the sample data set is ensured. If it is found that the accuracy rate of the travel resistance record data corresponding to the data collection source, i.e., the accuracy rate of the travel resistance record data corresponding to some historical shared data, is less than or equal to the set data accuracy threshold, the travel resistance record data corresponding to the historical contribution data will be deleted from the sample data set, excluding low-quality or unreliable data and ensuring the accuracy and credibility of the prediction model.

[0061] Next, after all data collection sources are verified and completed, a trusted travel resistance record data set is obtained, i.e., a trusted travel resistance record data set with high-quality and reliable data is obtained after screening and verification, ensuring that the basic data for subsequent analysis is trusted.

[0062] Next, data fusion is performed on the trusted travel resistance record data set to generate the travel resistance coefficient by comprehensively analyzing and processing the data in the trusted data set. Data fusion technology can combine information from multiple data sources to generate a comprehensive prediction result, improving the accuracy and reliability of the prediction. Through the fusion analysis of a large amount of high-quality data, the actual situation can be more accurately reflected, providing a reliable basis for the aerodynamic optimization of vehicles.

[0063] Through data collection, historical data analysis, data verification, and data fusion, accurate travel resistance coefficients are generated, thereby improving the performance and stability of vehicles under different driving conditions.

[0064] Further, the present application also includes:

[0065] Performing data fusion on the reliable driving resistance record data set to generate the driving resistance coefficient includes mode analysis or central tendency analysis or box plot analysis.

[0066] Specifically, the process of performing data fusion on the reliable driving resistance record data set to generate the driving resistance coefficient includes multiple analysis methods to ensure the accuracy and reliability of the final result. The purpose of data fusion is to integrate information from multiple data sources and derive a comprehensive driving resistance coefficient through different statistical analysis methods.

[0067] One method is mode analysis. Mode analysis is to find the most frequent value in the data set to represent the central tendency of the overall data. In the driving resistance record data set, mode analysis can help identify the most common driving resistance value, reflecting the resistance situation of the vehicle in most cases, for identifying common operating conditions and optimizing the tail wing structure adjustment under common driving conditions.

[0068] Another method is central tendency analysis. Central tendency analysis includes statistical quantities such as mean and median, which can understand the central tendency and distribution of data. The mean can provide an overall average level, while the median can reduce the impact of outliers on the result. Through central tendency analysis, the overall characteristics of the driving resistance data can be more comprehensively understood, providing a reliable reference for the determination of the driving resistance coefficient.

[0069] Box plot analysis is a visual statistical method that can visually display the distribution characteristics of data, outliers, and quartiles of data. Box plot analysis can help identify the distribution of outliers in the data set, allowing for more effective filtering and processing of data. Through box plot analysis, the distribution characteristics and variation range of the data can be more clearly understood, providing more detailed basis for the final determination of the driving resistance coefficient.

[0070] Through various data fusion methods such as mode analysis, central tendency analysis and box plot analysis, the reliable driving resistance record data set is comprehensively analyzed to generate an accurate and reliable driving resistance coefficient, improving the performance and stability of the vehicle under different driving conditions.

[0071] Further, the present application also includes:

[0072] When the driving stability coefficient is less than or equal to a first convergence threshold, or / and the driving resistance coefficient is greater than or equal to a second convergence threshold: interact with the user terminal to receive the desired driving speed interval; interact with the control terminal to receive the tail wing structure telescopic length rated interval, the tail wing structure azimuth angle rated interval and the tail wing structure pitch angle rated interval; based on the tail wing structure telescopic length rated interval, the tail wing structure azimuth angle rated interval and the tail wing structure pitch angle rated interval, uniformly distribute within a preset step size to obtain a tail wing structure control scheme set; traverse the tail wing structure control scheme set with the minimum value of the desired driving speed interval, and based on the stability prediction unit and the resistance prediction unit, obtain the maximum value of the driving stability coefficient and the minimum value of the driving resistance coefficient; when the maximum value of the driving stability coefficient is less than or equal to the first convergence threshold, or / and the minimum value of the driving resistance coefficient is greater than or equal to the second convergence threshold, generate a desired speed down adjustment suggestion, and update the desired driving speed interval with the user terminal; according to the desired driving speed update interval, optimize the motion speed information, the tail wing structure telescopic length information, the tail wing structure azimuth angle information and the tail wing structure pitch angle information to generate the telescopic tail wing structure control scheme.

[0073] Specifically, when the driving stability coefficient is less than or equal to the first convergence threshold or the driving resistance coefficient is greater than or equal to the second convergence threshold, or when the driving stability coefficient is less than or equal to the first convergence threshold and the driving resistance coefficient is greater than or equal to the second convergence threshold, data interaction is performed with the user terminal to receive the desired driving speed interval, i.e., the desired driving speed interval is input by the user, reflecting the user's expectation and demand for the vehicle driving speed.

[0074] Next, through data interaction with the control terminal, the rated interval information of the tail wing structure is received, including the tail wing structure telescopic length rated interval, the tail wing structure azimuth angle rated interval and the tail wing structure pitch angle rated interval. By receiving multiple rated intervals, the operating range of the tail wing structure under different parameter settings is obtained, providing basic data for subsequent optimization.

[0075] Based on multiple rated intervals, a tail wing structure control scheme set is generated by uniformly distributing within a preset step size, which contains all possible combinations of the tail wing structure under different parameter settings. Through uniform distribution, it is ensured that all possible tail wing parameter combinations are considered, thereby providing comprehensive options for finding the best control scheme.

[0076] Next, based on the minimum value of the desired speed interval, the tail wing structure control scheme set is accessed in turn. Through the stability prediction unit 31 and the resistance prediction unit 32, the driving stability coefficient and the driving resistance coefficient under each tail wing structure control scheme are calculated by analyzing each tail wing structure control scheme. Finally, the combination of the maximum driving stability coefficient and the minimum driving resistance coefficient is selected as the optimal control scheme.

[0077] If in the optimal scheme, when the maximum driving stability coefficient is still less than or equal to the first convergence threshold or the minimum driving resistance coefficient is still greater than or equal to the second convergence threshold, or when the maximum driving stability coefficient is still less than or equal to the first convergence threshold and the minimum driving resistance coefficient is still greater than or equal to the second convergence threshold, a desired speed reduction suggestion is generated, and the desired driving speed interval is updated through the interactive user terminal, ensuring that the user can adjust the desired speed in time to achieve better driving effect.

[0078] Finally, according to the updated desired driving speed interval, the motion speed information, the tail wing structure extension length information, the tail wing structure azimuth angle information and the tail wing structure pitch angle information are optimized. Through the adjustment of the parameters, the final telescopic tail wing structure control scheme is generated, ensuring the driving stability and minimizing the driving resistance, while meeting the speed demand of the user, optimizing the overall performance of the vehicle.

[0079] Through user input and analysis, the driving parameters of the vehicle are optimized, and finally a comprehensive telescopic tail wing structure control scheme is generated, improving the driving performance and stability of the vehicle under different conditions.

[0080] Further, the present application also includes:

[0081] When the driving stability coefficient is greater than the first convergence threshold and the driving resistance coefficient is less than the second convergence threshold, the telescopic tail wing structure control scheme is output.

[0082] Specifically, when the driving stability coefficient is greater than the first convergence threshold and the driving resistance coefficient is less than the second convergence threshold, the telescopic tail wing structure control scheme is output, indicating that the current driving state has reached the preset optimization standard, i.e. the vehicle has both sufficient stability and low air resistance during driving.

[0083] The driving stability coefficient greater than the first convergence threshold means that the driving stability of the vehicle has reached or exceeded the requirements of the system, reflecting the degree of smoothness that the vehicle can maintain under different driving conditions. A higher driving stability coefficient means that the vehicle can maintain good dynamic balance when responding to changes in air flow, speed and tail wing adjustment, reducing vibration and shaking and providing a more comfortable driving experience.

[0084] The running resistance coefficient less than the second convergence threshold value indicates that the aerodynamic characteristics of the vehicle have been optimized, and the resistance is effectively controlled. Lower running resistance coefficient means that the vehicle receives less air resistance during running, which not only improves fuel efficiency, but also improves the overall performance of the vehicle. By accurately controlling the airflow speed, airflow angle, tail wing extension length, azimuth angle and pitch angle and other parameters, the optimal configuration of the tail wing structure is realized, thereby minimizing air resistance.

[0085] In the case of meeting two conditions, the telescopic tail wing structure control scheme is output, which contains all the optimized parameter settings to ensure that the vehicle can maintain the best performance and stability during running. The output scheme can not only be directly applied to the actual control of the vehicle, but also serve as a reference for subsequent optimization and adjustment.

[0086] By outputting the overall optimized telescopic tail wing structure control scheme when the running stability coefficient and the running resistance coefficient meet the preset optimization standard, the vehicle can maintain the best stability and the lowest resistance under various running conditions, thereby improving the running efficiency and safety.

[0087] In summary, the intelligent control platform for telescopic tail wing structure provided by the present application has the following technical effects:

[0088] The monitoring end is used to collect airflow speed information and airflow angle information of a pre-running area; the control end is used to upload motion speed information, tail wing structure extension length information, tail wing structure azimuth angle information and tail wing structure pitch angle information; the service center includes: a stability prediction unit for processing the airflow speed information, the airflow angle information, the motion speed information, the tail wing structure extension length information, the tail wing structure azimuth angle information and the tail wing structure pitch angle information to predict the running stability coefficient; a resistance prediction unit for processing the airflow speed information, the airflow angle information, the motion speed information, the tail wing structure extension length information, the tail wing structure azimuth angle information and the tail wing structure pitch angle information to predict the running resistance coefficient; a control optimization unit for optimizing the motion speed information, the tail wing structure extension length information, the tail wing structure azimuth angle information and the tail wing structure pitch angle information according to the running stability coefficient and the running resistance coefficient to generate a telescopic tail wing structure control scheme; and a data interaction unit for sending the telescopic tail wing structure control scheme to the control end for telescopic tail wing structure control, thereby achieving the technical goal of real-time optimization control of the telescopic tail wing structure and achieving the technical effect of improving the aerodynamic performance and running stability of the vehicle.

[0089] The foregoing description of the disclosed embodiments enables a person skilled in the art to make or use the application. Numerous modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without the use of the inventive faculty. Therefore, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0090] It will be readily apparent to one skilled in the art that varying substitutions and modifications can be made to the application disclosed herein without departing from the scope and spirit of the application. Accordingly, it is intended that all such alterations and modifications be considered as within the scope of the application.

Claims

1. An intelligent control platform for a retractable tail structure, characterized by, The system comprises: a monitoring end for collecting airflow speed information and airflow angle information of a pre-driving area; a control end for uploading motion speed information, tail wing structure telescopic length information, tail wing structure azimuth angle information and tail wing structure pitch angle information; a service center comprising: a stability prediction unit for processing the airflow speed information, the airflow angle information, the motion speed information, the tail wing structure telescopic length information, the tail wing structure azimuth angle information and the tail wing structure pitch angle information, and predicting a driving stability coefficient; a resistance prediction unit for processing the airflow speed information, the airflow angle information, the motion speed information, the tail wing structure telescopic length information, the tail wing structure azimuth angle information and the tail wing structure pitch angle information, and predicting a driving resistance coefficient; a control optimization unit for optimizing the motion speed information, the tail wing structure telescopic length information, the tail wing structure azimuth angle information and the tail wing structure pitch angle information according to the driving stability coefficient and the driving resistance coefficient, and generating a telescopic tail wing structure control scheme; a data interaction unit for sending the telescopic tail wing structure control scheme to the control end for telescopic tail wing structure control.

2. The platform of claim 1, wherein, The stability prediction unit comprises: a shaking frequency prediction node for processing the airflow speed information, the airflow angle information, the motion speed information, the tail wing structure telescopic length information, the tail wing structure azimuth angle information and the tail wing structure pitch angle information, and predicting a driving shaking frequency; a shaking amplitude prediction node for processing the airflow speed information, the airflow angle information, the motion speed information, the tail wing structure telescopic length information, the tail wing structure azimuth angle information and the tail wing structure pitch angle information, and predicting a driving shaking amplitude; a stability prediction node for processing the driving shaking frequency and the driving shaking amplitude, and generating the driving stability coefficient.

3. The platform of claim 2, wherein, The stability prediction node comprises the following steps: constructing a stability prediction function: ， wherein S represents a driving stability coefficient, p and q are adjustment indexes representing the nonlinearity degree of the influence of shaking amplitude and frequency on stability, A represents shaking amplitude, Z represents shaking frequency, k represents a first adjustment factor, and c represents a second adjustment factor; configuring a first stability prediction loss function, wherein the first stability prediction loss function is used to calculate the mean value of the driving stability coefficient prediction deviation of every N training; configuring a second stability prediction loss function, wherein the second stability prediction loss function is used to calculate the proportion of the driving stability coefficient prediction deviation less than or equal to a stability coefficient prediction deviation threshold value in every N training; according to the telescopic tail wing structure deployment main body model, collecting a driving shaking frequency record data set, a driving shaking amplitude record data set and a driving stability coefficient identification data set, and configuring the stability prediction function; generating the stability prediction node when the first stability prediction loss function is less than or equal to the stability coefficient prediction deviation threshold value, and the second stability prediction loss function is less than or equal to an abnormal proportion threshold value.

4. The platform of claim 1, wherein, The resistance prediction unit performs steps comprising: The main body model is deployed as a category retrieval condition with a retractable tail structure; The airflow speed information and the airflow angle information are normalized to construct a driving scene feature coordinate, and a scene deviation Euclidean distance threshold is combined to construct a first fuzzy retrieval condition; The motion speed information, the tail structure retractable length information, the tail structure azimuth angle information, and the tail structure pitch angle information are normalized to construct a driving control feature coordinate, and a control deviation Euclidean distance threshold is combined to construct a second fuzzy retrieval condition; According to the category retrieval condition, the first fuzzy retrieval condition, and the second fuzzy retrieval condition, large sample analysis is performed to predict the driving resistance coefficient.

5. The platform of claim 4, wherein, According to the category retrieval condition, the first fuzzy retrieval condition, and the second fuzzy retrieval condition, large sample analysis is performed to predict the driving resistance coefficient, comprising: Collecting a sample data set that meets the category retrieval condition, the first fuzzy retrieval condition, and the second fuzzy retrieval condition, wherein the sample data set includes a data collection source and driving resistance record data, and the data collection source and the driving resistance record data correspond one-to-one; Performing historical shared data analysis on the data collection source to obtain a historical shared data accuracy rate; When the historical shared data accuracy rate is less than or equal to a data accuracy rate threshold, deleting the driving resistance record data from the sample data set; When the data collection source is verified, a trusted driving resistance record data set is obtained; Performing data fusion on the trusted driving resistance record data set to generate the driving resistance coefficient.

6. The platform of claim 5, wherein, Performing data fusion on the trusted driving resistance record data set to generate the driving resistance coefficient, comprising: data fusion is mode analysis or central tendency analysis or box plot analysis.

7. The platform of claim 1, wherein, The control optimization unit performs steps comprising: When the driving stability coefficient is less than or equal to a first convergence threshold, or / and the driving resistance coefficient is greater than or equal to a second convergence threshold: Interacting with the user end to receive an expected driving speed interval; Interacting with the control end to receive a tail structure retractable length rated interval, a tail structure azimuth angle rated interval, and a tail structure pitch angle rated interval; Based on the tail structure retractable length rated interval, the tail structure azimuth angle rated interval, and the tail structure pitch angle rated interval, performing uniform distribution with a preset step size to obtain a tail structure control scheme set; Traversing the tail structure control scheme set with the minimum value of the expected driving speed interval, and performing analysis based on the stability prediction unit and the resistance prediction unit to obtain a maximum value of the driving stability coefficient and a minimum value of the driving resistance coefficient; When the maximum value of the driving stability coefficient is less than or equal to the first convergence threshold, or / and the minimum value of the driving resistance coefficient is greater than or equal to the second convergence threshold, generating a desired speed down adjustment suggestion, and updating the expected driving speed interval by interacting with the user end; According to the expected driving speed update interval, the motion speed information, the tail wing structure telescopic length information, the tail wing structure azimuth angle information and the tail wing structure pitch angle information are optimized to generate the telescopic tail wing structure control scheme.

8. The platform of claim 7, wherein, When the driving stability coefficient is greater than a first convergence threshold value, and the driving resistance coefficient is less than a second convergence threshold value, the telescopic tail wing structure control scheme is output.

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