Intelligent empennage control method, device and equipment, storage medium and product

Abstract

The invention discloses an intelligent empennage control method, device and equipment, a storage medium and a product, and relates to the technical field of vehicles, and the intelligent empennage control method comprises the steps that vehicle condition recognition is conducted on vehicle condition data collected in real time, and whether energy recovery needs to be conducted or not is judged according to a vehicle condition recognition result; if yes, performing fusion processing on the vehicle condition data according to a preset weighting engine and a preset multi-mode decision model to obtain control parameters corresponding to the target empennage; the target empennage is controlled based on the control parameters corresponding to the target empennage, and compared with the limitation that current empennage control depends on a single vehicle speed parameter, control requirements under complex working conditions cannot be met, so that the control precision is low, and the control efficiency is high. According to the method, energy recovery is achieved while the vehicle empennage is accurately controlled through multi-modal data recognition and the preset multi-modal decision model, and therefore the control requirement of a multi-working-condition scene is met.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to intelligent tail wing control methods, devices, equipment, storage media, and products. Background Technology

[0002] Currently, with the accelerated pace of electrification (pure electric and hybrid) of commercial vehicles, driving range, energy consumption control and driving safety have become core demands. Intelligent rear wings are key devices for reducing wind resistance and improving energy efficiency. However, current rear wing control relies on the limitation of a single vehicle speed parameter, which cannot meet the control requirements under complex working conditions, resulting in low control accuracy.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this application is to provide an intelligent tail wing control method, device, equipment, storage medium, and product, aiming to solve the technical problem that current tail wing control relies on a single vehicle speed parameter, which cannot meet the control requirements under complex working conditions and results in low control accuracy.

[0005] To achieve the above objectives, this application proposes an intelligent tail wing control method, the intelligent tail wing control method comprising: The system performs vehicle condition identification on the real-time collected vehicle condition data and determines whether energy recovery is needed based on the identification results. If so, the vehicle condition data is fused according to the preset weighted engine and the preset multimodal decision model to obtain the control parameters corresponding to the target tail wing; The target tail fin is controlled based on the control parameters corresponding to the target tail fin.

[0006] In one embodiment, the step of performing vehicle condition identification on the real-time collected vehicle condition data and determining whether energy recovery is needed based on the vehicle condition identification result includes: Vehicle condition identification is performed on real-time collected vehicle condition data to obtain vehicle condition identification results; If the road gradient in the vehicle condition recognition results is greater than the preset gradient, it is determined that energy recovery is required.

[0007] In one embodiment, the preset multimodal decision model includes a vehicle speed and load model, a crosswind compensation model, a battery SOC adaptation model, and an energy recovery coordination model. The step of fusing the vehicle condition data based on the preset weighted engine and the preset multimodal decision model to obtain the control parameters corresponding to the target tail wing includes: Based on the vehicle speed and load model, the crosswind compensation model, the battery SOC adaptation model, the energy recovery coordination model, and the vehicle condition data, the rear wing parameters are predicted to obtain the rear wing parameter prediction results. The predicted tail fin parameters are dynamically weighted and fused based on a preset weighting engine to obtain the control parameters corresponding to the target tail fin.

[0008] In one embodiment, the step of predicting the rear wing parameters based on the vehicle speed and load model, the crosswind compensation model, the battery SOC adaptation model, the energy recovery coordination model, and the vehicle condition data to obtain the predicted rear wing parameters includes: Based on the vehicle speed and load model and the vehicle condition data, the tail wing parameters are predicted to obtain a first prediction result; Based on the crosswind compensation model and the vehicle condition data, the tail wing parameters are predicted to obtain a second prediction result; Based on the battery SOC adaptation model and the vehicle condition data, the rear wing parameters are predicted to obtain a third prediction result; Based on the energy recovery collaborative model and the vehicle condition data, the tail wing parameters are predicted to obtain a fourth prediction result; The tail fin parameter prediction results are determined based on the first prediction result, the second prediction result, the third prediction result, and the fourth prediction result.

[0009] In one embodiment, the step of dynamically weighting and fusing the tail fin parameter prediction results based on a preset weighting engine to obtain the control parameters corresponding to the target tail fin includes: Determine whether weighting correction is needed based on the crosswind intensity and battery SOC in the vehicle condition data. If so, then the first prediction result, the second prediction result, the third prediction result and the fourth prediction result are dynamically weighted based on the preset weighting engine to obtain the adjusted weight information; Based on the adjusted weight information, the first prediction result, the second prediction result, the third prediction result, and the fourth prediction result are weighted and fused to obtain the control parameters corresponding to the target tail fin.

[0010] In one embodiment, after the step of controlling the target tail fin based on the control parameters corresponding to the target tail fin, the method further includes: If the actuation resistance is detected to reach the preset anti-pinch threshold, the parameters of the target tail fin are adjusted to the target parameters and an audible and visual alarm is triggered. If the actuation drag is detected to reach a preset anti-collision threshold, the target tail fin is controlled to retract rapidly to its minimum state within a preset time.

[0011] Furthermore, to achieve the above objectives, this application also proposes an intelligent tail fin control device, which includes: The vehicle condition recognition module is used to identify the vehicle condition data collected in real time and determine whether energy recovery is needed based on the vehicle condition recognition results. The parameter determination module is used to perform fusion processing on the vehicle condition data according to the preset weighted engine and the preset multimodal decision model if the condition is met, so as to obtain the control parameters corresponding to the target tail wing. The tail fin control module is used to control the target tail fin based on the control parameters corresponding to the target tail fin.

[0012] In addition, to achieve the above objectives, this application also proposes an intelligent tail wing control device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the intelligent tail wing control method as described above.

[0013] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the intelligent tail wing control method described above.

[0014] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the intelligent tail wing control method described above.

[0015] One or more technical solutions proposed in this application have at least the following technical effects: This application performs vehicle condition identification on real-time collected vehicle condition data and determines whether energy recovery is needed based on the identification results. If so, the vehicle condition data is fused using a preset weighted engine and a preset multimodal decision model to obtain the control parameters corresponding to the target rear wing. The target rear wing is controlled based on the control parameters corresponding to the target rear wing. Compared with the current limitation of rear wing control relying on a single vehicle speed parameter, which cannot meet the control requirements under complex working conditions and results in low control accuracy, this application achieves precise control of the vehicle rear wing and energy recovery simultaneously through multimodal data identification and a preset multimodal decision model, thereby meeting the control requirements of multiple working conditions. Attached Figure Description The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating an embodiment of the intelligent tail fin control method of this application. Figure 2 This is a schematic diagram of a closed-loop system provided in Embodiment 1 of the intelligent tail fin control method of this application; Figure 3 This is a flowchart illustrating Embodiment 2 of the intelligent tail fin control method of this application. Figure 4 This is a schematic diagram of the module structure of the intelligent tail wing control device according to an embodiment of this application; Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the intelligent tail fin control method in the embodiments of this application.

[0018] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of this application.

[0020] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0021] The main solution of this application embodiment is: This application identifies the vehicle condition by collecting real-time vehicle condition data, and determines whether energy recovery is needed based on the vehicle condition identification result; if so, the vehicle condition data is fused according to a preset weighted engine and a preset multimodal decision model to obtain the control parameters corresponding to the target tail wing; the target tail wing is controlled based on the control parameters corresponding to the target tail wing.

[0022] In this embodiment, for ease of description, the following description uses a computing service device as the execution subject.

[0023] The current tail wing control relies on a single vehicle speed parameter, which is insufficient to meet the control requirements under complex operating conditions, resulting in low control accuracy.

[0024] This application provides a solution that achieves precise control of the vehicle's rear wing while simultaneously recovering energy through multimodal data recognition and a preset multimodal decision model, thereby meeting the control requirements of multiple operating scenarios.

[0025] As can be seen from the above embodiments, this application identifies the vehicle condition by collecting real-time vehicle condition data and determines whether energy recovery is needed based on the identification results. If so, the vehicle condition data is fused using a preset weighted engine and a preset multimodal decision model to obtain the control parameters corresponding to the target rear wing. The target rear wing is then controlled based on these control parameters. Compared to the limitations of current rear wing control, which relies on a single vehicle speed parameter and cannot meet the control requirements under complex operating conditions, resulting in low control accuracy, this application achieves precise control of the vehicle rear wing while simultaneously realizing energy recovery through multimodal data identification and a preset multimodal decision model, thereby meeting the control requirements of multiple operating scenarios.

[0026] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, including a device with an intelligent tail wing control system. The following description uses a computer as an example to illustrate this embodiment and the subsequent embodiments.

[0027] Based on this, embodiments of this application provide an intelligent tail wing control method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the intelligent tail wing control method of this application.

[0028] In this embodiment, the intelligent tail fin control method includes steps S10 to S30: Step S10: Perform vehicle condition identification on the real-time collected vehicle condition data, and determine whether energy recovery is required based on the vehicle condition identification results.

[0029] It should be noted that this application proposes a multimodal sensing intelligent rear wing adapted to new energy commercial vehicles, including a rear wing body, a multimodal sensing module, an intelligent rear wing dedicated control unit, a dual redundant actuator, and a full-scenario safety protection module. The components work together to form a closed-loop system of "perception-decision-execution-protection": The rear wing body is integrally molded with a carbon fiber-honeycomb aluminum composite sandwich structure, taking into account both lightweight and high strength; it includes a fixed section, a telescopic section, and a deflectable wing surface. The fixed section is rigidly connected to the commercial vehicle trailer by bolts, the telescopic section is slidably connected to the fixed section by a high-precision ball screw guide rail, and the deflectable wing surface is hinged to the telescopic section by a double rotating shaft to achieve coordinated adjustment of the deployment length and deflection angle; the connection between the telescopic section and the fixed section is equipped with a double-layer dustproof sealing sleeve, which is resistant to high and low temperatures ranging from -45℃ to 130℃ and water pressure ≥1.5MPa, making it suitable for harsh working conditions; the edge of the deflectable wing surface is equipped with a 10mm thick polyurethane flexible anti-collision strip with a Shore hardness of 50-60A to reduce collision damage.

[0030] Understandably, compared to traditional intelligent rear wings, which are all based on the design of fuel vehicles and do not take into account the specific characteristics of new energy commercial vehicles such as the SOC state of the power battery and energy recovery conditions, they only rely on a single vehicle speed parameter for adjustment. When the battery is low, they cannot prioritize the optimization of wind resistance to extend the range, and they lack aerodynamic coordination during the energy recovery phase, resulting in low range improvement and falling far short of the needs of new energy commercial vehicles. This application integrates multi-dimensional data such as load, crosswind, and road slope, but the wind resistance optimization effect under complex conditions such as high-speed heavy load and mountain driving is limited (only a reduction of 5%-8%), and may even increase driving resistance due to improper rear wing angle, failing to adapt to the dynamic needs of different operating scenarios. The real-time vehicle condition data is real-time vehicle data, including integrated high-precision sensor data and whole vehicle data, such as vehicle speed, load, crosswind, battery status, and road conditions. This application collects vehicle condition data by constructing a comprehensive data acquisition network, where vehicle speed data is connected to the whole vehicle CAN bus and read from the whole vehicle controller (V CU) reading, range 0-90km / h, accuracy ±0.3km / h, update frequency 20Hz; Load data: collected by strain gauge load sensors, attached to key stress points of the longitudinal beams of the chassis, measurement range 0-50 tons, accuracy ±0.5 tons, temperature compensation range -40℃~85℃; Crosswind data: collected by ultrasonic crosswind sensors, installed in the center of the top of the cab, measurement range 0-20m / s, accuracy ±0.2m / s, wind direction recognition range 0-360° (error ≤5°), protection level IP67; Battery status data: read directly from the battery management system (BMS) via CAN bus, SOC value accuracy ±1%, battery temperature range -20℃~85℃ (accuracy ±0.5℃); Road condition data: accessed cloud road condition database via vehicle networking (4G / 5G), combined with vehicle positioning information, slope range -15°~+15° (accuracy ±0.1°), road type recognition accuracy ≥98%. By deeply integrating the BMS and energy recovery system, the aerodynamic attitude under low SOC conditions and during the recovery phase is optimized to improve the range and solve the core pain point of insufficient adaptability of traditional tail fins to new energy sources, thus significantly improving the adaptability to new energy sources.

[0031] In its implementation, this application collects vehicle condition data in real time and determines whether to activate energy recovery based on this data. By integrating six types of core parameter data, it overcomes the limitations of existing technologies that rely solely on vehicle speed parameter control, achieving comprehensive data collection across all dimensions, including vehicle speed, load, crosswind, battery status, and road conditions, thus providing full support for precise adjustments.

[0032] Furthermore, step S10 also includes: performing vehicle condition identification on the real-time collected vehicle condition data to obtain vehicle condition identification results; if the road condition gradient in the vehicle condition identification results is greater than a preset gradient, then it is determined that energy recovery is required.

[0033] It should be noted that the system performs vehicle condition identification on the real-time collected vehicle condition data to obtain the vehicle condition identification results. If the road slope in the vehicle condition identification results is greater than the preset slope, it is determined that energy recovery needs to be performed. The preset slope can be a pre-set slope range used to determine when to start energy recovery. For example, if the slope is ≥5°, it is determined that energy recovery should be triggered.

[0034] Understandably, by matching road condition data with vehicle positioning information, the slope information of the vehicle during driving is determined. When the vehicle enters a downhill section (slope ≥ 5°), the road condition data triggers energy recovery, and by fine-tuning the rear wing angle, it works in conjunction with the electric motor braking to improve energy recovery efficiency.

[0035] In practice, the energy recovery triggering conditions of this application are not limited to slope triggering, but can also be triggered by braking, depending on the vehicle condition data.

[0036] Step S20: If yes, then the vehicle condition data is fused according to the preset weighted engine and the preset multimodal decision model to obtain the control parameters corresponding to the target tail wing.

[0037] It should be noted that the preset weighted engine is a pre-set engine used to dynamically adjust the weights of sub-models in the preset multimodal decision model. The preset multimodal decision model incorporates a multi-parameter fusion control algorithm, which consists of a vehicle speed-load base model, a crosswind compensation model, a battery SOC adaptation model, and an energy recovery coordination model. When energy recovery is required, the energy recovery coordination model in the preset multimodal decision model is triggered. When energy recovery is activated, the tail wing angle is finely adjusted by 2-3° to improve recovery efficiency. This is achieved through a dual-redundant actuator: a main electric drive component and a backup pneumatic drive component, which seamlessly switch within ≤15ms via an electromagnetic clutch, thus realizing energy recovery. This application uses a CAN bus for bidirectional communication with the vehicle's VCU and BMS, and incorporates a multi-parameter fusion control algorithm to achieve new energy control. The control parameters corresponding to the target tail wing include those for tail wing extension / retraction and wing surface deflection. This application, through a dual-redundant drive and rapid switching design, with electric + pneumatic dual-drive redundancy configuration and seamless switching, overcomes the safety bottleneck of existing single-drive designs, ensuring reliability under fault conditions.

[0038] Understandably, the preset multimodal decision model includes a vehicle speed-load basic model, a crosswind compensation model, a battery SOC adaptation model, and an energy recovery coordination model. Among them, the vehicle speed-load basic model is used as the core basic model, and the crosswind compensation model, battery SOC adaptation model, and energy recovery coordination model are used as dynamic compensation models to predict the control parameters of the tail wing. The preset weighted engine dynamically adjusts the weights of the four tail wing parameter prediction results output by the four sub-models to determine the control parameters corresponding to the target tail wing.

[0039] In practice, the multimodal perception module collects data such as vehicle speed, load, crosswind intensity, battery SOC value, and road slope in real time, and inputs the above data into the preset multimodal decision model. When the vehicle's operating conditions are detected and energy recovery is triggered, the energy recovery coordination model is triggered. The vehicle speed-load basic model is the core (weight 50%), and the crosswind compensation model (weight 20%), battery SOC adaptation model (weight 20%), and energy recovery coordination model (weight 10%) are dynamic compensation items. The weight allocation is dynamically adjusted through real-time data and preset weighted engine feedback to ensure the optimal control logic under different operating conditions. Thus, after receiving the data, the intelligent rear wing control unit adapts the model to take the lead in the control logic, determines the control parameters corresponding to the target rear wing, and outputs commands to control the extension and retraction of the rear wing and the deflection of the wing surface.

[0040] Step S30: Control the target tail fin based on the control parameters corresponding to the target tail fin.

[0041] It should be noted that after adjusting the weights of each sub-model, the predicted tail wing parameters output by each sub-model in combination with real-time vehicle condition data are weighted and fused by a preset weighted engine to obtain the control parameters corresponding to the final target tail wing. Then, the target command is output according to the control parameters to control the target tail wing.

[0042] Furthermore, after step S30, the method further includes: if the actuation resistance is detected to reach a preset anti-pinch threshold, adjusting the parameters of the target tail fin to the target parameters and triggering an audible and visual alarm; if the actuation resistance is detected to reach a preset anti-collision threshold, controlling the target tail fin to rapidly retract to its minimum state within a preset time.

[0043] It should be noted that, compared to existing designs that commonly use a single drive component, the tail wing becomes completely inoperable after a motor or transmission mechanism failure, posing a driving safety hazard. Furthermore, the anti-pinch and anti-collision protection mechanisms are inadequate, making the tail wing prone to interference and collisions with cargo / trailers during high-frequency loading and unloading or in complex road conditions, impacting operational efficiency and safety. This application also proposes a safety protection measure: during operation, the detection unit senses the resistance in real time to ensure no risk of pinching; if the main electric drive component experiences an abnormal current, it automatically switches to the backup pneumatic drive component after several seconds, maintaining the tail wing's stable attitude. The all-scenario safety protection module includes a pressure detection unit, a collision sensor, and an emergency switching unit, constructing a triple safety protection system, including anti-pinch protection, anti-collision protection, and fault emergency protection.

[0044] Understandably, if the actuation drag is detected to reach the preset anti-pinch threshold, the parameters of the target tail fin are adjusted to the target parameters and an audible and visual alarm is triggered; if the actuation drag is detected to reach the preset anti-collision threshold, the target tail fin is controlled to quickly retract to the minimum state within a preset time.

[0045] In its implementation, this application integrates anti-pinch, anti-collision, and fault emergency protection, setting precise thresholds and a rapid response mechanism to fill the gaps in existing technology's inadequate protection. The all-scenario safety protection module includes a pressure detection unit, a collision sensor, and an emergency switching unit, constructing a triple safety protection system: Anti-pinch protection triggers a 70mm retraction of the tail fin at 90mm / s and triggers an audible and visual alarm when an actuation resistance ≥40N is detected; anti-collision protection triggers rapid tail fin retraction to its minimum state within 0.05s when an impact force ≥100N is detected. Fault emergency protection automatically switches to backup drive when the current, voltage, and temperature of the main drive component remain abnormal for 1.5s, and fault data is uploaded to the cloud. For further explanation of the closed-loop system of this solution, please refer to... Figure 2 The diagram shows a closed-loop system, including a perception layer, a decision-making layer, an execution layer, and a protection layer. This is a multimodal perception intelligent rear wing adapted for new energy commercial vehicles. Its core working principle is as follows: A multimodal perception module collects comprehensive data, which is then processed by a multi-parameter fusion algorithm in the intelligent rear wing's dedicated control unit to output optimal commands. Dual redundant actuators drive the dynamic adjustment of the rear wing, and a full-scenario safety protection module ensures reliability under all operating conditions. The specific workflow is as follows: 1. Data Acquisition: The multimodal perception module collects real-time data such as vehicle speed, load, crosswind intensity, battery SOC value, and road slope; 2. Decision Output: The intelligent rear wing's dedicated control unit receives data... The system prioritizes the control logic based on the adaptive model, outputting commands to control the tail wing extension and deflection. 3. Action Execution: The main electric drive component precisely adjusts the tail wing attitude according to commands, with a self-locking function to lock the position and prevent displacement during driving. 4. Safety Protection: During driving, the detection unit senses the motion resistance in real time to ensure no risk of pinching. If the main electric drive component experiences an abnormal current, it automatically switches to the backup aerodynamic drive component after several seconds, maintaining a stable tail wing attitude. 5. Operating Condition Switching: When the vehicle enters a downhill section (gradient ≥ 5°), road condition data triggers the energy recovery collaborative model, fine-tuning the tail wing angle and working in conjunction with motor braking to improve energy recovery efficiency. Through multimodal perception and fusion of six core parameters, it adaptively reduces the drag coefficient, achieving more precise adjustment under complex operating conditions. The dual-redundant drive design and triple safety protection result in a short fault response time and anti-pinch and anti-collision functions, addressing existing safety hazards.

[0046] This embodiment identifies the vehicle condition based on real-time collected vehicle condition data and determines whether energy recovery is needed based on the identification results. If so, the vehicle condition data is fused using a preset weighted engine and a preset multimodal decision model to obtain the control parameters corresponding to the target rear wing. The target rear wing is then controlled based on these control parameters. Compared to the limitations of current rear wing control, which relies on a single vehicle speed parameter and cannot meet the control requirements under complex operating conditions, resulting in low control accuracy, this embodiment achieves precise control of the vehicle's rear wing while simultaneously realizing energy recovery through multimodal data identification and a preset multimodal decision model, thereby meeting the control requirements of multiple operating scenarios.

[0047] Based on the above Figure 1 The first embodiment shown illustrates a second embodiment of the emotion recognition method proposed in this application; refer to... Figure 3 , Figure 3 This is a flowchart illustrating Embodiment Two of the emotion recognition method of this application. Based on Embodiment One of this application, the content in Embodiment Two that is the same as or similar to that in Embodiment One can be referred to the above description, and will not be repeated hereafter.

[0048] In this embodiment, the preset multimodal decision model includes a vehicle speed and load model, a crosswind compensation model, a battery SOC adaptation model, and an energy recovery coordination model. Step S20 further includes: Step S201: Based on the vehicle speed and load model, the crosswind compensation model, the battery SOC adaptation model, the energy recovery coordination model, and the vehicle condition data, predict the rear wing parameters to obtain the predicted rear wing parameters.

[0049] It should be noted that the vehicle speed-load base model is based on a commercial vehicle operating at speeds up to 90 km / h. Six core operating conditions are defined based on vehicle speed (≤50 km / h / 50-90 km / h) and load factor (≤0.5 / 0.5-0.8 / ≥0.8). The corresponding tail wing deployment length and deflection angle are pre-stored (as shown in the table below), with an adjustment error ≤0.5°. The specific tail wing control parameters for the corresponding vehicle speed and load data are shown in the table below.

[0050] The crosswind compensation model counteracts crosswind interference by fine-tuning the wing angle (±3-8°) according to crosswind intensity, resulting in vehicle deviation ≤5cm / 100m. The battery SOC adaptation model outputs the minimum drag command when SOC ≤20%, balances energy consumption and stability between SOC 20%-80%, and prioritizes stability when SOC ≥80%. The tail wing parameter prediction results include the prediction results output from the vehicle speed and load model, the crosswind compensation model, the battery SOC adaptation model, and the energy recovery coordination model.

[0051] Understandably, to further explain the construction process of each sub-model, (1) the vehicle speed-load basic model is constructed based on the BP neural network algorithm, as follows: 1. Network structure: 2 nodes in the input layer (vehicle speed v, load m), 3 layers in the hidden layer (16, 32, and 16 nodes in each layer respectively), 2 nodes in the output layer (tail wing deployment length L, deflection angle θ), and ReLU (hidden layer) and Sigmoid (output layer) are used as activation functions; 2. Training data: real vehicle working condition data (covering 0-90km) are collected. (Vehicle speed / h, 0-50 ton load range), simultaneously record drag coefficient Cd and tail fin attitude parameters, and divide into training set (80%), validation set (10%), and test set (10%) after Z-score standardization; 3. Training parameters: initial learning rate of 0.001, using Adam optimizer, loss function of mean squared error (MSE), 500 training iterations, training stops when the validation set loss value ≤ 0.002; 4. Mapping formula: construct a nonlinear mapping relationship through the network parameters after training: L = , = It is designed to be compatible with commercial vehicles operating at speeds up to 90 km / h. It is divided into 6 core operating conditions based on vehicle speed (≤50 km / h / 50-90 km / h) and load factor (≤0.5 / 0.5-0.8 / ≥0.8). The corresponding tail wing deployment length and deflection angle are pre-stored, with an adjustment error of ≤0.5°. (2) The crosswind compensation model is constructed based on the PID control algorithm, and the core formula is as follows: 1. Control formula: ,in This represents the current deviation in vehicle deviation. This is the proportionality coefficient. The integral coefficient is... 1. Differential coefficient; 2. Grading parameters: When the crosswind intensity is 0-5 m / s, =0.8、 =0.05、 =0.1, fine-tune the angle ±3°; at 5-10 m / s, =1.2、 =0.08、 =0.15, fine-tune the angle ±5°; at 10-20 m / s, =1.5、 =0.1、 =0.2, fine-tuning angle ±8°, the final vehicle deviation is ≤5cm / 100m; (3) Battery SOC adaptation model: Piecewise linear control logic is adopted, and the core formula and weight allocation are as follows: 1. Control logic formula: ;in To optimize wind resistance, For the purpose of driving stability, =1; 2. Segmented weights: When SOC≤20% =1、 =0, output minimum wind resistance command (prioritizing range); when SOC is 20%-80%, =0.5、 =0.5, adjusted according to the energy consumption and stability balance strategy; when SOC≥80%, =0.3、 =0.7, emphasizing stability; 3. Accuracy optimization: Integrating the ASRCKF (Adaptive Square Root Capacitive Kalman Filter) algorithm to filter the SOC data collected by the BMS, the filtering formula is: =A +B + Reduce noise interference to ensure SOC recognition accuracy of ±0.8%; (4) The energy recovery collaborative model is constructed based on the least squares method and the vehicle dynamics formula, as follows: 1. Load identification formula: The relationship between motor speed n, torque T and vehicle load m is fitted by the least squares method: m= ,in 1. Fitting coefficients, calibrated using real vehicle data; 2. Dynamic formula: Combining aerodynamics and vehicle braking theory, the relationship between the tail wing angle adjustment and recovery efficiency is as follows: ,in For energy recovery efficiency, To recover torque, - 3. Execution logic: The energy recovery activation signal and recovery intensity level (low / medium / high) are obtained in real time through the VCU, and the tail wing angle is finely adjusted by 2° / 2.5° / 3° accordingly to optimize the downforce of the airflow on the vehicle body and improve the energy recovery efficiency of the motor regenerative braking.

[0052] In specific implementation, through the aforementioned model training and construction process, a vehicle speed-load model, a crosswind compensation model, a battery SOC adaptation model, and an energy recovery coordination model are constructed. These four models are then weighted and fused to obtain a pre-defined multimodal decision model. Based on a weighted fusion algorithm, the weight coefficients of each sub-model are determined using the analytic hierarchy process (AHP) to achieve dynamic synergistic fusion of the four sub-models. Specifically, the vehicle speed-load basic model is the core (50% weight), while the crosswind compensation model (20% weight), battery SOC adaptation model (20% weight), and energy recovery coordination model (10% weight) serve as dynamic compensation items. The weight allocation is dynamically adjusted based on real-time data feedback to ensure optimal control logic under different operating conditions. By inputting vehicle condition data into the four sub-models and predicting the rear wing parameters, the predicted rear wing parameters for each sub-model are obtained.

[0053] Further, step S201 also includes: predicting the rear wing parameters based on the vehicle speed and load model and the vehicle condition data to obtain a first prediction result; predicting the rear wing parameters based on the crosswind compensation model and the vehicle condition data to obtain a second prediction result; predicting the rear wing parameters based on the battery SOC adaptation model and the vehicle condition data to obtain a third prediction result; predicting the rear wing parameters based on the energy recovery coordination model and the vehicle condition data to obtain a fourth prediction result; and determining the rear wing parameter prediction result based on the first prediction result, the second prediction result, the third prediction result, and the fourth prediction result.

[0054] Understandably, the tail wing parameter prediction results include the tail wing parameter prediction results output by each sub-model of the vehicle speed and load model, the crosswind compensation model, the battery SOC adaptation model, and the energy recovery coordination model. The tail wing parameters are predicted based on the vehicle speed and load model and vehicle condition data to obtain the first prediction result; the tail wing parameters are predicted based on the crosswind compensation model and vehicle condition data to obtain the second prediction result; the tail wing parameters are predicted based on the battery SOC adaptation model and vehicle condition data to obtain the third prediction result; the tail wing parameters are predicted based on the energy recovery coordination model and vehicle condition data to obtain the fourth prediction result; and the tail wing parameter prediction result is determined based on the first prediction result, the second prediction result, the third prediction result, and the fourth prediction result.

[0055] Step S202: Dynamically weight and fuse the predicted tail fin parameters based on a preset weighted engine to obtain the control parameters corresponding to the target tail fin.

[0056] It should be noted that the first prediction results from the four sub-models were obtained. Second prediction result Third prediction result And the fourth prediction result The first prediction result is processed by a preset weighting engine. Second prediction result Third prediction result And the fourth prediction result Dynamic weighted fusion is performed to obtain the control parameters corresponding to the target tail fin.

[0057] Furthermore, step S202 further includes: determining whether weight correction is needed based on the crosswind intensity and battery SOC in the vehicle condition data; if so, dynamically adjusting the weights of the first prediction result, the second prediction result, the third prediction result, and the fourth prediction result based on a preset weighting engine to obtain adjusted weight information; and performing weighted fusion on the first prediction result, the second prediction result, the third prediction result, and the fourth prediction result based on the adjusted weight information to obtain the control parameters corresponding to the target tail fin.

[0058] It should be noted that the need for weight adjustment is determined based on the crosswind intensity and battery SOC in the vehicle condition data; if so, the first prediction result is based on the preset weighted engine. Second prediction result Third prediction result And the fourth prediction result Perform dynamic weight adjustment to obtain adjusted weight information; based on the adjusted weight information, analyze the first prediction result. Second prediction result Third prediction result And the fourth prediction result Weighted fusion is performed to obtain the control parameters corresponding to the target tail fin.

[0059] Understandably, , , , These correspond to the weights of the vehicle speed and load model, the crosswind compensation model, the battery SOC adaptation model, and the energy recovery coordination model, respectively. Dynamic weight adjustment can be achieved by setting trigger conditions, such as when the battery SOC ≤ 20%. Increased to 30%, Reduced to 40%; when crosswind intensity is ≥10m / s, Increased to 25%, The weight was lowered to 45%, and the weight was dynamically adjusted based on real-time data feedback.

[0060] In the specific implementation, it is based on a weighted fusion algorithm and the analytic hierarchy process (AHP), as follows: 1. AHP process: Construct a hierarchical structure of "overall objective (optimal tail control) - criterion layer (drag optimization, range assurance, driving stability) - scheme layer (four sub-models)", construct a judgment matrix and perform a consistency check (CR=0.06<0.1, meeting the consistency requirement), and determine the initial weights; 2. Weighted fusion formula: + ,in - These are the weights of the four sub-models (initial values ​​50%, 20%, 20%, 10%); 3. Dynamic weight adjustment: Set trigger conditions, such as when SOC ≤ 20%, Increased to 30%, Reduced to 40%; when crosswind ≥10m / s, Increased to 25%, The weight is adjusted to 45% and dynamically corrected based on real-time data feedback. The main electric drive assembly consists of a stepper motor and worm gear transmission, with an adjustment accuracy of ±0.1°, meeting the high-precision adjustment requirements of normal operating conditions. The backup pneumatic drive assembly consists of a high-pressure cylinder and an electromagnetic proportional valve, with a response speed ≤10ms, ensuring normal tail wing operation in case of main drive assembly failure. Both have mechanical self-locking functions (self-locking torque ≥50N·m) to prevent accidental displacement during driving. By integrating four sub-models, the system is specifically adapted to the SOC state and energy recovery conditions of new energy commercial vehicles, addressing the shortcomings of traditional algorithms that do not consider the characteristics of new energy vehicles.

[0061] This embodiment identifies vehicle conditions based on real-time collected vehicle condition data and determines whether energy recovery is needed based on the identification results. If so, it predicts the rear wing parameters based on a vehicle speed and load model, a crosswind compensation model, a battery SOC adaptation model, an energy recovery coordination model, and the vehicle condition data, obtaining the predicted rear wing parameters. The predicted rear wing parameters are then dynamically weighted and fused using a preset weighted engine to obtain the control parameters corresponding to the target rear wing. The target rear wing is then controlled based on these control parameters. Compared to current rear wing control methods that rely on a single vehicle speed parameter, which cannot meet the control requirements under complex operating conditions and result in low control accuracy, this embodiment achieves precise control of the vehicle's rear wing while simultaneously realizing energy recovery through multimodal data identification and a preset multimodal decision model, thereby meeting the control requirements of multiple operating scenarios.

[0062] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the intelligent tail wing control method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0063] This application also provides an intelligent tail wing control device, please refer to... Figure 4 The intelligent tail fin control device includes: The data acquisition module 10 is used to identify the vehicle condition data collected in real time and determine whether energy recovery is needed based on the vehicle condition identification results. The multimodal decision module 20 is used to perform fusion processing on the vehicle condition data according to the preset weighted engine and the preset multimodal decision model if the condition is true, so as to obtain the control parameters corresponding to the target tail wing. The tail fin control module 30 is used to control the target tail fin based on the control parameters corresponding to the target tail fin.

[0064] Furthermore, the data acquisition module 10 is also used to perform vehicle condition identification on the real-time acquired vehicle condition data and obtain vehicle condition identification results; if the road condition gradient in the vehicle condition identification results is greater than the preset gradient, it is determined that energy recovery is required.

[0065] Furthermore, the preset multimodal decision model includes a vehicle speed and load model, a crosswind compensation model, a battery SOC adaptation model, and an energy recovery coordination model. The multimodal decision module 20 is also used to predict the tail wing parameters based on the vehicle speed and load model, the crosswind compensation model, the battery SOC adaptation model, the energy recovery coordination model, and the vehicle condition data to obtain tail wing parameter prediction results; and to dynamically weight and fuse the tail wing parameter prediction results based on a preset weighting engine to obtain the control parameters corresponding to the target tail wing.

[0066] Furthermore, the multimodal decision module 20 is also used to predict the tail wing parameters based on the vehicle speed and load model and the vehicle condition data to obtain a first prediction result; predict the tail wing parameters based on the crosswind compensation model and the vehicle condition data to obtain a second prediction result; predict the tail wing parameters based on the battery SOC adaptation model and the vehicle condition data to obtain a third prediction result; predict the tail wing parameters based on the energy recovery coordination model and the vehicle condition data to obtain a fourth prediction result; and determine the tail wing parameter prediction result based on the first prediction result, the second prediction result, the third prediction result, and the fourth prediction result.

[0067] Furthermore, the multimodal decision module 20 is also used to determine whether weight correction is needed based on the crosswind intensity and battery SOC in the vehicle condition data; if so, it dynamically adjusts the weights of the first prediction result, the second prediction result, the third prediction result, and the fourth prediction result based on a preset weighting engine to obtain adjusted weight information; and performs weighted fusion on the first prediction result, the second prediction result, the third prediction result, and the fourth prediction result based on the adjusted weight information to obtain the control parameters corresponding to the target tail fin.

[0068] Furthermore, the tail wing control module 30 is also used to adjust the parameters of the target tail wing to the target parameters and issue an audible and visual alarm when the actuation drag is detected to reach the preset anti-pinch threshold; and to control the target tail wing to retract to the minimum state rapidly within a preset time when the actuation drag is detected to reach the preset anti-collision threshold.

[0069] The intelligent tail wing control device provided in this application, employing the intelligent tail wing control method in the above embodiments, can solve the technical problem of intelligent tail wing control. Compared with the prior art, the beneficial effects of the intelligent tail wing control device provided in this application are the same as those of the intelligent tail wing control method provided in the above embodiments, and other technical features in the intelligent tail wing control device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0070] This application provides an intelligent tail wing control device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the intelligent tail wing control method in the first embodiment described above.

[0071] The following is for reference. Figure 5 The diagram illustrates a structural schematic of a smart tail wing control device suitable for implementing embodiments of this application. The smart tail wing control device in embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The intelligent tail wing control device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0072] like Figure 5As shown, the intelligent tail fin control device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the intelligent tail fin control device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 allows the smart tail fin control device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows a smart tail fin control device with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0073] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0074] The intelligent rear wing control device provided in this application, employing the intelligent rear wing control method described in the above embodiments, can solve the technical problem that current rear wing control relies on a single vehicle speed parameter, failing to meet control requirements under complex operating conditions and resulting in low control accuracy. Compared with the prior art, the beneficial effects of the intelligent rear wing control device provided in this application are the same as those of the intelligent rear wing control method provided in the above embodiments, and other technical features of this intelligent rear wing control device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0075] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0076] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0077] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the smart tail fin control method in the above embodiments.

[0078] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0079] The aforementioned computer-readable storage medium may be included in the intelligent tail fin control device; or it may exist independently and not assembled into the intelligent tail fin control device.

[0080] The aforementioned computer-readable storage medium carries one or more programs. When the aforementioned one or more programs are executed by the intelligent rear wing control device, the intelligent rear wing control device: performs vehicle condition identification on the real-time collected vehicle condition data, and determines whether energy recovery is required based on the vehicle condition identification result; if so, it performs fusion processing on the vehicle condition data according to a preset weighted engine and a preset multimodal decision model to obtain the control parameters corresponding to the target rear wing; and controls the target rear wing based on the control parameters corresponding to the target rear wing.

[0081] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0082] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0083] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0084] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described intelligent tail wing control method. This addresses the limitation of current tail wing control relying on a single vehicle speed parameter, which fails to meet control requirements under complex operating conditions, resulting in low control accuracy. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the intelligent tail wing control method provided in the above embodiments, and will not be elaborated upon here.

[0085] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the intelligent tail fin control method described above.

[0086] The computer program product provided in this application can solve the technical problem that current tail wing control relies on a single vehicle speed parameter, which cannot meet the control requirements under complex operating conditions, resulting in low control accuracy. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the intelligent tail wing control method provided in the above embodiments, and will not be repeated here.

[0087] The above description is only a part of the embodiments of this application and does not limit the scope of this application. All equivalent structural transformations made under the technical concept of this application and using the content of this application specification and drawings, or direct / indirect applications in other related technical fields, are included within the protection scope of this application.

Claims

1. A smart tail fin control method, characterized in that, The intelligent tail fin control method includes: The system performs vehicle condition identification on the real-time collected vehicle condition data and determines whether energy recovery is needed based on the identification results. If so, the vehicle condition data is fused according to the preset weighted engine and the preset multimodal decision model to obtain the control parameters corresponding to the target tail wing; The target tail fin is controlled based on the control parameters corresponding to the target tail fin.

2. The intelligent tail wing control method as described in claim 1, characterized in that, The step of identifying the vehicle condition from the real-time collected vehicle condition data and determining whether energy recovery is needed based on the identification results includes: Vehicle condition identification is performed on real-time collected vehicle condition data to obtain vehicle condition identification results; If the road gradient in the vehicle condition recognition results is greater than the preset gradient, it is determined that energy recovery is required.

3. The intelligent tail fin control method as described in claim 2, characterized in that, The preset multimodal decision model includes a vehicle speed and load model, a crosswind compensation model, a battery SOC adaptation model, and an energy recovery coordination model. The step of fusing the vehicle condition data based on the preset weighted engine and the preset multimodal decision model to obtain the control parameters corresponding to the target tail wing includes: Based on the vehicle speed and load model, the crosswind compensation model, the battery SOC adaptation model, the energy recovery coordination model, and the vehicle condition data, the rear wing parameters are predicted to obtain the rear wing parameter prediction results. The predicted tail fin parameters are dynamically weighted and fused based on a preset weighting engine to obtain the control parameters corresponding to the target tail fin.

4. The intelligent tail wing control method as described in claim 3, characterized in that, The step of predicting the rear wing parameters based on the vehicle speed and load model, the crosswind compensation model, the battery SOC adaptation model, the energy recovery coordination model, and the vehicle condition data, and obtaining the predicted rear wing parameters, includes: Based on the vehicle speed and load model and the vehicle condition data, the tail wing parameters are predicted to obtain a first prediction result; Based on the crosswind compensation model and the vehicle condition data, the tail wing parameters are predicted to obtain a second prediction result; Based on the battery SOC adaptation model and the vehicle condition data, the rear wing parameters are predicted to obtain a third prediction result; Based on the energy recovery collaborative model and the vehicle condition data, the tail wing parameters are predicted to obtain a fourth prediction result; The tail fin parameter prediction results are determined based on the first prediction result, the second prediction result, the third prediction result, and the fourth prediction result.

5. The intelligent tail wing control method as described in claim 4, characterized in that, The step of dynamically weighting and fusing the predicted tail fin parameters based on a preset weighting engine to obtain the control parameters corresponding to the target tail fin includes: Determine whether weighting correction is needed based on the crosswind intensity and battery SOC in the vehicle condition data. If so, then the first prediction result, the second prediction result, the third prediction result and the fourth prediction result are dynamically weighted based on the preset weighting engine to obtain the adjusted weight information; Based on the adjusted weight information, the first prediction result, the second prediction result, the third prediction result, and the fourth prediction result are weighted and fused to obtain the control parameters corresponding to the target tail fin.

6. The intelligent tail fin control method as described in claim 4, characterized in that, After the step of controlling the target tail fin based on the control parameters corresponding to the target tail fin, the method further includes: If the actuation resistance is detected to reach the preset anti-pinch threshold, the parameters of the target tail fin are adjusted to the target parameters and an audible and visual alarm is triggered. If the actuation drag is detected to reach a preset anti-collision threshold, the target tail fin is controlled to retract rapidly to its minimum state within a preset time.

7. An intelligent tail fin control device, characterized in that, The intelligent tail fin control device includes: The vehicle condition recognition module is used to identify the vehicle condition data collected in real time and determine whether energy recovery is needed based on the vehicle condition recognition results. The parameter determination module is used to perform fusion processing on the vehicle condition data according to the preset weighted engine and the preset multimodal decision model if the condition is met, so as to obtain the control parameters corresponding to the target tail wing. The tail fin control module is used to control the target tail fin based on the control parameters corresponding to the target tail fin.

8. An intelligent tail fin control device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the smart tail fin control method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the intelligent tail fin control method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the intelligent tail fin control method as described in any one of claims 1 to 6.

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