Automobile antenna control method and device of new energy automobile and storage medium

Abstract

The invention belongs to the technical field of signal processing, and discloses an automobile antenna control method and device for a new energy automobile and a storage medium, and the method comprises the steps: collecting real-time signal data from N units of an antenna array; performing primary processing on the collected real-time signal data to obtain a comprehensive feature data set; training according to the comprehensive feature data set to obtain a quality evaluation model, and predicting to obtain a comprehensive communication quality index; according to the comprehensive communication quality index, evaluating whether the communication quality reaches the standard; if the communication quality does not reach the standard, dynamically adjusting the antenna gain through a beam forming algorithm to obtain a corresponding gain adjustment strategy; implementing a corresponding gain adjustment strategy until the communication quality reaches the standard; a communication link is optimized, and the stability and quality of communication are ensured.

Description

Technical Field

[0001] The present invention relates to the field of signal processing technology, and more specifically, to a method, device and storage medium for controlling a vehicle antenna of a new energy vehicle. Background Art

[0002] The patent with patent announcement number CN117118539A provides a car antenna, equipment, storage medium and control method for new energy vehicles. When the car antenna of the new energy vehicle is working, the antenna radiation signal is obtained, the antenna radiation signal is mapped into the standard range to obtain standard antenna radiation signal data, the standard antenna radiation signal data is subjected to amplitude-frequency extraction to obtain the antenna radiation signal conduction amplitude-frequency characteristics, the antenna radiation signal is filtered according to the antenna radiation conduction signal judgment value to obtain pre-processed antenna radiation signal data, the antenna conduction radiation abnormality average error is determined according to the antenna variable carrier frequency average frequency, a correction connection instruction is sent to the car antenna, and reconnection is performed to complete the signal conduction correction control of the car antenna of the new energy vehicle. The control of the car antenna of the new energy vehicle can be completed by correcting the signal conduction of the car antenna of the new energy vehicle to improve the working stability of the car antenna.

[0003] The existing vehicle antenna control method for new energy vehicles has the following main problems: If the direction and gain of the antenna array are not optimized, the signal gain may not be maximized, resulting in insufficient signal strength and coverage, which in turn affects the communication quality; it is impossible to ensure that the system always maintains the best signal state, and signal fluctuations, increased interference or increased bit error rate may occur, which may seriously affect the user's communication experience; the direction and gain of the antenna array are not dynamically optimized, and the system cannot effectively control the interference source, resulting in interference signals from other directions affecting the target signal; the direction and gain of the antenna array are not dynamically adjusted, and the stability of the system may be greatly reduced If the step size is not adjusted dynamically, the system may experience oscillation and non-convergence due to a too large step size, or slow optimization due to a too small step size, making it difficult to reach the optimal solution within a reasonable time. If the beam direction of the antenna array is not adjusted in a targeted manner, the system's anti-interference ability is weak and it may not be able to effectively identify and suppress signals from other directions, thus affecting the stability and performance of the communication system. If the signal gain is not maximized by optimizing the objective function, the antenna array may not be able to obtain the best signal coverage in the target direction, resulting in insufficient signal strength; if the signal gain is not optimized, the signal may be attenuated during transmission, resulting in insufficient signal strength at the receiving end, which cannot meet the needs of high-quality communication; if the relative weight between signal and interference is not dynamically adjusted, the system may not be able to cope with the challenges in the interference environment, resulting in difficulty in distinguishing between the signal and the interference signal, thereby affecting the signal quality; if the output of each unit is not precisely controlled by adjusting the amplitude and phase of the antenna array, the system may not be able to concentrate the signal in the target direction, thereby failing to fully improve the signal gain.

[0004] In view of this, the present invention proposes a vehicle antenna control method, device and storage medium for a new energy vehicle to solve the above problems. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solution: a vehicle antenna control method for a new energy vehicle, comprising: S1, collect real-time signal data from N units of the antenna array; S2, preliminarily processing the collected real-time signal data to obtain a comprehensive feature data set; S3. Train a quality assessment model based on the comprehensive feature data set to predict a comprehensive communication quality index; and assess whether the communication quality meets the standard based on the comprehensive communication quality index; S4. If the communication quality does not meet the standard, the antenna gain is dynamically adjusted through the beamforming algorithm to obtain a corresponding gain adjustment strategy; S5. Implement corresponding gain adjustment strategies until the communication quality reaches the standard.

[0006] Furthermore, the antenna array is composed of N units, each unit being an independent antenna element; the real-time signal data includes signal state data, space state data, environment state data and communication basic data; Signal status data includes signal amplitude, signal phase, signal frequency and signal power; spatial status data includes arrival angle, transmission angle and array configuration; array configuration includes the phase value and amplitude of each antenna unit signal; environmental status data includes driving speed, vehicle position, obstacle distribution, interference signal frequency, interference signal strength and weather conditions; basic communication data includes communication task type, communication link rate and communication delay.

[0007] Furthermore, the method for preliminarily processing the collected real-time signal data includes: S31, using a box plot method to identify and remove outliers in the real-time signal data, and using a linear interpolation method to fill in missing values ​​in the real-time signal data; S32, extracting features from the real-time signal data by fast Fourier transform, and recording the real-time signal data as: ;in, For real-time signal data; is the signal status data; is the space state data, which is the combination of each antenna signal; is the environmental status data; Basic data for communication; S33, real-time signal data Perform fast Fourier transform to obtain the frequency domain signal: ;in, is the frequency domain signal obtained after fast Fourier transform; For every point of the real-time signal in the time domain; is a frequency variable, representing each point in the frequency domain after fast Fourier transform; is an imaginary unit; is a complex exponential function, which represents the rotation factor of the real-time signal in the frequency domain; is the ratio of pi; is the integral operation symbol; The modulus of the frequency domain signal is calculated by the signal amplitude calculation formula to obtain the amplitude of the frequency domain signal; the signal amplitude calculation formula is: ;in, is the amplitude of the frequency domain signal; is the real part of the frequency domain signal; is the imaginary part of the frequency domain signal; S34. Find the position with the largest amplitude in the frequency domain as the main frequency of the frequency domain signal: ;in, is the dominant frequency of the frequency domain signal; S35. Extract the main frequency of the frequency domain signal and integrate the frequency domain features extracted from the real-time signal data into a real-time signal feature data set. , for real-time signal feature datasets Standard deviation normalization is performed to obtain a normalized comprehensive feature data set.

[0008] Furthermore, the method for obtaining the quality assessment model includes: The dataset is divided into training set, validation set and test set to train the model, evaluate the model performance and verify the model generalization ability; the sample set is a subset of the dataset, each sample set includes a historical comprehensive feature dataset and the corresponding comprehensive communication quality indicators; GBDT is selected as the specific implementation of the gradient boosting tree model to handle the regression task; Initialize GBDT parameters and define the objective function; GBDT parameters include the maximum number of leaf nodes in each tree, learning rate, number of trees and maximum depth of trees; use the historical comprehensive feature data set as input data and the corresponding comprehensive communication quality index as output label to train the quality assessment model; the quality assessment model is a gradient boosting tree model; Use mean square error as the loss function to measure the difference between the model's predicted value and the actual value; in each iteration, GBDT builds a new decision tree to fit the negative gradient of the loss function of the previous step, uses the negative gradient of the objective function as the learning target of the new tree, and adjusts the contribution of the new tree to the final result through the learning rate to minimize the loss function; Use the validation set to evaluate the performance of the model, tune the model, and adjust the model parameters based on its performance feedback until the preset stopping condition is reached; use the trained quality assessment model to predict the current comprehensive feature data set to obtain comprehensive communication quality indicators.

[0009] Furthermore, the comprehensive communication quality index is composed of a signal strength index, a signal-to-noise ratio index, a bit error rate index and a signal quality index through weighted averaging.

[0010] Furthermore, the method for evaluating whether the communication quality meets the standard according to the comprehensive communication quality index is as follows: Compare the predicted comprehensive communication quality index with the preset comprehensive communication quality index threshold: If the predicted comprehensive communication quality index is greater than or equal to the preset comprehensive communication quality index threshold, it is determined that the communication quality meets the standard; If the predicted comprehensive communication quality index is less than a preset comprehensive communication quality index threshold, it is determined that the communication quality does not meet the standard.

[0011] Furthermore, if the communication quality does not meet the standard, the method of dynamically adjusting the antenna gain through a beamforming algorithm to obtain a corresponding gain adjustment strategy includes: S71. Based on the obtained comprehensive communication quality index, the target direction of the antenna array is selected by the gradient search method. ; The default initial direction is , the initial step length is and the maximum number of iterations ; S72, based on the initial direction Computing Gradients ;in, Comprehensive quality index For the initial direction The partial derivative vector of ; Comprehensive quality index Direction The partial derivative of Comprehensive quality index Direction The partial derivative of Comprehensive quality index Direction The partial derivative of and is the index of the direction dimension, ; is the transposition operator; S73, the initial direction is updated by the direction update formula Make adjustments; the direction update formula is ;in, for the updated direction; is the initial update step size; the step size adjustment formula is used to dynamically adjust the update step size; the step size adjustment formula is ;in, is the adjusted step length; For the The update step size at the iteration; is the attenuation coefficient; is the current iteration number; S74, repeat steps S72-S73 until the preset maximum number of iterations is reached Stop when the target direction of the antenna array is obtained. ; S75. Based on the collected real-time signal data, a signal covariance matrix is ​​constructed; the beam direction vector is optimized through the beamforming algorithm. When the antenna array is in the target direction, When the signal gain on is maximized, the optimal beam direction vector is obtained; the amplitude and phase of the N units of the antenna array are adjusted according to the optimal beam direction vector, and then the corresponding gain adjustment strategy is obtained.

[0012] Furthermore, the method for obtaining the optimal beam direction vector includes: S81, preset antenna array in the target direction The signal gain on , construct the optimization objective function; the optimization objective function is: ;in, is the signal gain; is the beam direction vector, and the constraint condition of the beam direction vector is ; is the conjugate transpose of the beam direction vector; is the conjugate transpose operator; is the signal covariance matrix; is the interference covariance matrix; is the signal adjustment coefficient factor; The signal adjustment coefficient factor is dynamically designed through the coefficient factor adaptive formula, and the coefficient factor adjustment adaptive formula is: ;in, is the angle of the target direction; is the angle of the beam direction; A factor to adjust the interference suppression effect; S82. Combining the optimization objective function with the constraint conditions of the beam direction vector, constructing a Lagrangian function by the Lagrangian multiplier method to solve, and then obtaining the optimal beam direction vector; the Lagrangian function is: ;in, is the Lagrangian function; is the Lagrange multiplier; find the partial derivative of the beam direction vector and make the partial derivative of the beam direction vector equal to zero, and get the eigenvalue problem: ; S84. By solving the eigenvalue problem, the eigenvector corresponding to the maximum eigenvalue is selected as the optimal beam direction vector: ; S85. Adjust the amplitude and phase of the N elements of the antenna array according to the optimal beam direction vector.

[0013] A device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a method for controlling a vehicle antenna of a new energy vehicle is implemented.

[0014] A storage medium stores a computer program, and when the computer program is executed, a method for controlling a vehicle antenna of a new energy vehicle is implemented.

[0015] The present invention provides a vehicle antenna control method, device and storage medium for a new energy vehicle, which have the following beneficial effects: Dynamically adjusting the direction and gain of the antenna array through the gradient search method and beamforming algorithm can maximize the signal gain, ensure that the antenna array always points to the best direction, and improve the coverage and strength of the signal; through real-time monitoring of comprehensive communication quality indicators, it can ensure that the system is always in the best state, improve user experience and system stability; the gradient search method continuously adjusts the target direction of the antenna array, so that the system can be optimized in real time in a complex environment. As the communication environment changes (for example, the user's position changes, interference sources appear, etc.), this method can dynamically adjust the antenna gain to adapt to different scenarios; The step size adjustment formula allows the update step size to change dynamically in each iteration, which helps avoid convergence problems caused by too large or too small step sizes, making the optimization process more stable and accurate. By optimizing the beam direction vector, the signal gain is maximized and the interference effect is reduced. By optimizing the beam direction vector of the antenna array through the beamforming algorithm, the signal can be effectively focused while suppressing interference from other directions, so that the signal can still maintain high-quality transmission in complex communication environments. By optimizing the objective function to maximize signal gain, the antenna array can obtain the best signal coverage in the target direction, which helps to improve communication quality and reduce signal loss or attenuation; the dynamic adjustment formula of the coefficient factor enables the system to adjust the weight of the signal and interference according to the actual situation, thereby effectively controlling the impact of interference; in the case of strong interference, increase the weight of the interference suppression effect factor to reduce the impact of interference on signal quality; by adjusting the amplitude and phase of the antenna array, the output of each antenna unit can be accurately controlled to optimize the beam direction and signal coverage. The amplitude and phase adjustment of each antenna unit ensures the maximum signal enhancement in the optimal beam direction, thereby improving the overall system performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic flow chart of a method for controlling a vehicle antenna of a new energy vehicle of the present invention; Figure 2 This is a schematic structural diagram of a vehicle antenna control system for a new energy vehicle of the present invention; Figure 3 It is a schematic diagram of the device of the present invention; Figure 4 Schematic diagram of the storage medium of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Example 1, please refer to Figure 1 In this embodiment, the vehicle antenna control method of the new energy vehicle includes: S1, collect real-time signal data from N units of the antenna array; S2, preliminarily processing the collected real-time signal data to obtain a comprehensive feature data set; S3. Train a quality assessment model based on the comprehensive feature data set to predict a comprehensive communication quality index; and assess whether the communication quality meets the standard based on the comprehensive communication quality index; S4. If the communication quality does not meet the standard, the antenna gain is dynamically adjusted through the beamforming algorithm to obtain a corresponding gain adjustment strategy; S5. Implement corresponding gain adjustment strategies until the communication quality reaches the standard.

[0019] The antenna array is composed of N units, each unit is an independent antenna element; antenna arrays are widely used in wireless communications, radar, satellite communications, 5G, automotive communications, aerospace and other fields; especially in new energy vehicles, antenna arrays can be used for vehicle communication systems, navigation systems, radar detection and other functions. For example, in vehicle communications, the beam control of the antenna array can help realize the environmental perception of autonomous vehicles and high-precision satellite navigation. Real-time signal data includes signal status data, space status data, environmental status data and communication basic data; Signal status data includes signal amplitude, signal phase, signal frequency and signal power; spatial status data includes arrival angle, transmission angle and array configuration; array configuration includes the phase value and amplitude of each antenna unit signal; environmental status data includes driving speed, vehicle position, obstacle distribution, interference signal frequency, interference signal strength and weather conditions; basic communication data includes communication task type, communication link rate and communication delay.

[0020] The phase value of each antenna unit signal is used to achieve beamforming and direction control. The interference direction of the signal is controlled by adjusting the phase difference to form a directional beam to achieve signal enhancement or interference suppression in a specific direction. The amplitude of each antenna unit signal is used to control the distribution of signal strength. The beam shape and sidelobe suppression are optimized by adjusting the amplitude distribution to ensure the power efficiency of the antenna array. The angle of arrival refers to the receiving direction of the receiving antenna. By measuring the angle of arrival in the antenna array, it can help achieve beamforming, optimize signal reception and improve communication quality. The transmission angle refers to the angle at which the signal propagates from the transmitting antenna in any direction. In the antenna array, the signal phase of the transmitting antenna can be controlled to accurately adjust the transmission direction (i.e., the transmission angle) of the signal to achieve beam directing or focusing.

[0021] The methods for preliminary processing of the collected real-time signal data include: S31, using a box plot method to identify and remove outliers in the real-time signal data, and using a linear interpolation method to fill in missing values ​​in the real-time signal data; S32, extracting features from the real-time signal data by fast Fourier transform, and recording the real-time signal data as: ;in, For real-time signal data; is the signal status data; is the space state data, which is the combination of each antenna signal; is the environmental status data; Basic data for communication; S33, real-time signal data Perform fast Fourier transform to obtain the frequency domain signal: ;in, is the frequency domain signal obtained after fast Fourier transform; For every point of the real-time signal in the time domain; is a frequency variable, representing each point in the frequency domain after fast Fourier transform; is an imaginary unit; is a complex exponential function, which represents the rotation factor of the real-time signal in the frequency domain; is the ratio of pi; is the integral operation symbol; The modulus of the frequency domain signal is calculated by the signal amplitude calculation formula to obtain the amplitude of the frequency domain signal; the signal amplitude calculation formula is: ;in, is the amplitude of the frequency domain signal; is the real part of the frequency domain signal; is the imaginary part of the frequency domain signal; S34. Find the position with the largest amplitude in the frequency domain as the main frequency of the frequency domain signal: ;in, is the dominant frequency of the frequency domain signal; S35. Extract the main frequency of the frequency domain signal and integrate the frequency domain features extracted from the real-time signal data into a real-time signal feature data set. , for real-time signal feature datasets Standard deviation normalization is performed to obtain a normalized comprehensive feature data set.

[0022] Methods for obtaining quality assessment models include: The dataset is divided into training set, validation set and test set to train the model, evaluate the model performance and verify the model generalization ability; the sample set is a subset of the dataset, each sample set includes a historical comprehensive feature dataset and the corresponding comprehensive communication quality indicators; GBDT is selected as the specific implementation of the gradient boosting tree model to handle the regression task; Initialize GBDT parameters and define the objective function; GBDT parameters include the maximum number of leaf nodes in each tree, learning rate, number of trees and maximum depth of trees; use the historical comprehensive feature data set as input data and the corresponding comprehensive communication quality index as output label to train the quality assessment model; the quality assessment model is a gradient boosting tree model; Use mean square error as the loss function to measure the difference between the model's predicted value and the actual value; in each iteration, GBDT builds a new decision tree to fit the negative gradient of the loss function of the previous step, uses the negative gradient of the objective function as the learning target of the new tree, and adjusts the contribution of the new tree to the final result through the learning rate to minimize the loss function; Use the validation set to evaluate the performance of the model, tune the model, and adjust the model parameters based on its performance feedback until the preset stopping condition is reached; use the trained quality assessment model to predict the current comprehensive feature data set to obtain comprehensive communication quality indicators.

[0023] The comprehensive communication quality index is composed of the signal strength index, signal-to-noise ratio index, bit error rate index and signal quality index through weighted average.

[0024] Methods for evaluating whether communication quality meets the standards based on comprehensive communication quality indicators: Compare the predicted comprehensive communication quality index with the preset comprehensive communication quality index threshold: If the predicted comprehensive communication quality index is greater than or equal to the preset comprehensive communication quality index threshold, it is determined that the communication quality meets the standard; If the predicted comprehensive communication quality index is less than a preset comprehensive communication quality index threshold, it is determined that the communication quality does not meet the standard.

[0025] If the communication quality does not meet the standard, the antenna gain is dynamically adjusted through the beamforming algorithm. The method of obtaining the corresponding gain adjustment strategy includes: S71. Based on the obtained comprehensive communication quality index, the target direction of the antenna array is selected by the gradient search method. ; The default initial direction is , the initial step length is and the maximum number of iterations ; S72, based on the initial direction Computing Gradients ;in, Comprehensive quality index For the initial direction The partial derivative vector of ; Comprehensive quality index Direction The partial derivative of Adjustment of comprehensive quality indicators the extent of the impact; Comprehensive quality index Direction The partial derivative of Comprehensive quality index Direction The partial derivative of and is the index of the direction dimension, ; is the transposition operator; S73, the initial direction is updated by the direction update formula Make adjustments; the direction update formula is ;in, for the updated direction; is the initial update step size, which is used to control the update amplitude. The step size adjustment formula is used to dynamically adjust the update step size. The step size adjustment formula is: ;in, is the adjusted step length; For the The update step size at the iteration; is the attenuation coefficient, which is used to balance the decreasing speed of the step length; is the current iteration number; S74, repeat steps S72-S73 until the preset maximum number of iterations is reached Stop when the target direction of the antenna array is obtained. ; S75. Based on the collected real-time signal data, a signal covariance matrix is ​​constructed; the beam direction vector is optimized through the beamforming algorithm. When the antenna array is in the target direction, When the signal gain on is maximized, the optimal beam direction vector is obtained; the amplitude and phase of the N units of the antenna array are adjusted according to the optimal beam direction vector, and then the corresponding gain adjustment strategy is obtained; The beamforming algorithm is an advanced signal processing technology that allows a specific beam direction to be formed by adjusting the amplitude and phase of each element in the antenna array. This technology can significantly enhance the signal gain in the target direction while suppressing interference and noise in other directions, which not only helps to improve the communication quality, but also improves the overall performance and efficiency of the system.

[0026] The method for obtaining the optimal beam direction vector includes: S81, preset antenna array in the target direction The signal gain on , construct the optimization objective function; the optimization objective function is: ;in, is the signal gain; is the beam direction vector, and the constraint condition of the beam direction vector is ; is the conjugate transpose of the beam direction vector; is the conjugate transpose operator; is the signal covariance matrix; is the interference covariance matrix; is the signal adjustment coefficient factor, which is used to adjust the relative weight between the target signal and the interference signal; The signal adjustment coefficient factor is dynamically designed through the coefficient factor adaptive formula, and the coefficient factor adjustment adaptive formula is: ;in, is the angle of the target direction; is the angle of the beam direction; To adjust the interference suppression effect factor and control the influence of the interference signal; S82. Combining the optimization objective function with the constraint conditions of the beam direction vector, constructing a Lagrangian function by the Lagrangian multiplier method to solve, and then obtaining the optimal beam direction vector; the Lagrangian function is: ;in, is the Lagrangian function; is the Lagrange multiplier; find the partial derivative of the beam direction vector and make the partial derivative of the beam direction vector equal to zero, and get the eigenvalue problem: ; The purpose of this eigenvalue problem is to find the optimal beam direction vector so that the beam direction can maximize the signal gain while satisfying the constraints.

[0027] S84. By solving the eigenvalue problem, the eigenvector corresponding to the maximum eigenvalue is selected as the optimal beam direction vector: ; S85, adjusting the amplitude and phase of the N elements of the antenna array according to the optimal beam direction vector; the amplitude of the N elements of the antenna array after adjustment is ;in, is the amplitude of the N elements of the antenna array after adjustment; is the amplitude corresponding to the N elements of the antenna array in the optimal beam direction vector; the phase of the N elements of the antenna array after adjustment is ;in, is the phase of the N elements of the antenna array after adjustment; is the phase corresponding to the N elements of the antenna array in the optimal beam direction vector.

[0028] The preset comprehensive communication quality index threshold is set by the staff, and different comprehensive communication quality indicators are collected through the car antenna control terminal, and the average value of multiple comprehensive communication quality indicators is taken as the preset comprehensive communication quality index threshold.

[0029] This embodiment dynamically adjusts the direction and gain of the antenna array through the gradient search method and beamforming algorithm, which can maximize the signal gain, ensure that the antenna array always points to the optimal direction, and improve the coverage and strength of the signal; through real-time monitoring of the comprehensive communication quality index, it can ensure that the system is always in the best state, improve the user experience and system stability; the gradient search method continuously adjusts the target direction of the antenna array, so that the system can be optimized in real time in a complex environment. As the communication environment changes (for example, the user's position changes, interference sources appear, etc.), the method can dynamically adjust the gain of the antenna to adapt to different scenarios; The step size adjustment formula allows the update step size to change dynamically in each iteration, which helps avoid convergence problems caused by too large or too small step sizes, making the optimization process more stable and accurate. By optimizing the beam direction vector, the signal gain is maximized and the interference effect is reduced. By optimizing the beam direction vector of the antenna array through the beamforming algorithm, the signal can be effectively focused while suppressing interference from other directions, so that the signal can still maintain high-quality transmission in complex communication environments. By optimizing the objective function to maximize signal gain, the antenna array can obtain the best signal coverage in the target direction, which helps to improve communication quality and reduce signal loss or attenuation; the dynamic adjustment formula of the coefficient factor enables the system to adjust the weight of the signal and interference according to the actual situation, thereby effectively controlling the impact of interference; in the case of strong interference, increase the weight of the interference suppression effect factor to reduce the impact of interference on signal quality; by adjusting the amplitude and phase of the antenna array, the output of each antenna unit can be accurately controlled to optimize the beam direction and signal coverage. The amplitude and phase adjustment of each antenna unit ensures the maximum signal enhancement in the optimal beam direction, thereby improving the overall system performance.

[0030] Example 2, please refer to Figure 2 As shown, the part not described in detail in this embodiment is described in Example 1, and a vehicle antenna control system for a new energy vehicle is provided, including: A data acquisition module, used to collect real-time signal data from N units of the antenna array; A data processing module is used to perform preliminary processing on the collected real-time signal data to obtain a comprehensive feature data set; The quality assessment module is used to obtain a quality assessment model based on the comprehensive feature data set training, predict the comprehensive communication quality index; and evaluate whether the communication quality meets the standard based on the comprehensive communication quality index; Gain adjustment module, if the communication quality does not meet the standard, the antenna gain is dynamically adjusted through the beamforming algorithm to obtain the corresponding gain adjustment strategy; The strategy implementation module is used to implement the corresponding gain adjustment strategy until the communication quality reaches the standard.

[0031] Example 3, please refer to Figure 3 As shown, the present application also provides a device 500. The device 500 may include one or more processors and one or more memories. The memories store computer readable codes, and when the computer readable codes are executed by one or more processors, the vehicle antenna control method of the new energy vehicle as described above may be executed.

[0032] like Figure 3 As shown, the device 500 may include a bus 501, one or more CPUs 502, ROM 503, RAM 504, a communication port 505 connected to a network, an input / output 506, a hard disk 507, etc. The storage device in the device 500, such as ROM 503 or hard disk 507, may store the vehicle antenna control method for a new energy vehicle provided in the present application. Furthermore, the device 500 may also include a user interface 508. Of course, Figure 2 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 2 One or more components of an electronic device are shown.

[0033] Example 4, please refer to Figure 4 As shown, a storage medium 250 according to an embodiment of the present application is shown. Computer-readable instructions are stored on the storage medium 250. When the computer-readable instructions are executed by the processor, the vehicle antenna control method of the new energy vehicle according to the embodiment of the present application described with reference to the above figures can be executed. The storage medium 250 includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory (cache). Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0034] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0035] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0036] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0037] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for controlling a vehicle antenna of a new energy vehicle, characterized in that: include: S1, collect real-time signal data from N units of the antenna array; S2, preliminarily processing the collected real-time signal data to obtain a comprehensive feature data set; S3. Train a quality assessment model based on the comprehensive feature data set to predict a comprehensive communication quality index; and assess whether the communication quality meets the standard based on the comprehensive communication quality index; S4. If the communication quality does not meet the standard, the antenna gain is dynamically adjusted through the beamforming algorithm to obtain a corresponding gain adjustment strategy; S5. Implement corresponding gain adjustment strategies until the communication quality reaches the standard.

2. The method for controlling the antenna of a new energy vehicle according to claim 1, characterized in that: The antenna array is composed of N units, each unit is an independent antenna element; the real-time signal data includes signal state data, space state data, environment state data and communication basic data; Signal status data includes signal amplitude, signal phase, signal frequency and signal power; spatial status data includes arrival angle, transmission angle and array configuration; array configuration includes the phase value and amplitude of each antenna unit signal; environmental status data includes driving speed, vehicle position, obstacle distribution, interference signal frequency, interference signal strength and weather conditions; basic communication data includes communication task type, communication link rate and communication delay.

3. The method for controlling the antenna of a new energy vehicle according to claim 2, characterized in that: The method for performing preliminary processing on the collected real-time signal data comprises: S31, using a box plot method to identify and remove outliers in the real-time signal data, and using a linear interpolation method to fill in missing values ​​in the real-time signal data; S32, extracting features from the real-time signal data by fast Fourier transform, and recording the real-time signal data as: ;in, For real-time signal data; is the signal status data; is the space state data, which is the combination of each antenna signal; is the environmental status data; Basic data for communication; S33, real-time signal data Perform fast Fourier transform to obtain the frequency domain signal: ;in, is the frequency domain signal obtained after fast Fourier transform; For every point of the real-time signal in the time domain; is a frequency variable, representing each point in the frequency domain after fast Fourier transform; is an imaginary unit; is a complex exponential function, which represents the rotation factor of the real-time signal in the frequency domain; is pi; is the integral operation symbol; The modulus of the frequency domain signal is calculated by the signal amplitude calculation formula to obtain the amplitude of the frequency domain signal; the signal amplitude calculation formula is: ;in, is the amplitude of the frequency domain signal; is the real part of the frequency domain signal; is the imaginary part of the frequency domain signal; S34. Find the position with the largest amplitude in the frequency domain as the main frequency of the frequency domain signal: ;in, is the dominant frequency of the frequency domain signal; S35. Extract the main frequency of the frequency domain signal and integrate the frequency domain features extracted from the real-time signal data into a real-time signal feature data set. , for real-time signal feature datasets Standard deviation normalization is performed to obtain a normalized comprehensive feature data set.

4. The method for controlling the antenna of a new energy vehicle according to claim 3, characterized in that: The method for obtaining the quality assessment model includes: The dataset is divided into training set, validation set and test set to train the model, evaluate the model performance and verify the model generalization ability; the sample set is a subset of the dataset, each sample set includes a historical comprehensive feature dataset and the corresponding comprehensive communication quality indicators; GBDT is selected as the specific implementation of the gradient boosting tree model to handle the regression task; Initialize GBDT parameters and define the objective function; GBDT parameters include the maximum number of leaf nodes in each tree, learning rate, number of trees and maximum depth of trees; use the historical comprehensive feature data set as input data and the corresponding comprehensive communication quality index as output label to train the quality assessment model; the quality assessment model is a gradient boosting tree model; Use mean square error as the loss function to measure the difference between the model's predicted value and the actual value; in each iteration, GBDT builds a new decision tree to fit the negative gradient of the loss function of the previous step, uses the negative gradient of the objective function as the learning target of the new tree, and adjusts the contribution of the new tree to the final result through the learning rate to minimize the loss function; Use the validation set to evaluate the performance of the model, tune the model, and adjust the model parameters based on its performance feedback until the preset stopping condition is reached; use the trained quality assessment model to predict the current comprehensive feature data set to obtain comprehensive communication quality indicators.

5. The method for controlling the antenna of a new energy vehicle according to claim 4, characterized in that: The comprehensive communication quality index is composed of a signal strength index, a signal-to-noise ratio index, a bit error rate index and a signal quality index through weighted averaging.

6. The method for controlling the antenna of a new energy vehicle according to claim 5, characterized in that: The method for evaluating whether the communication quality meets the standard according to the comprehensive communication quality index is as follows: Compare the predicted comprehensive communication quality index with the preset comprehensive communication quality index threshold: If the predicted comprehensive communication quality index is greater than or equal to the preset comprehensive communication quality index threshold, it is determined that the communication quality meets the standard; If the predicted comprehensive communication quality index is less than a preset comprehensive communication quality index threshold, it is determined that the communication quality does not meet the standard.

7. The method for controlling the antenna of a new energy vehicle according to claim 6, characterized in that: If the communication quality does not meet the standard, the method of dynamically adjusting the antenna gain through a beamforming algorithm to obtain a corresponding gain adjustment strategy includes: S71. Based on the obtained comprehensive communication quality index, the target direction of the antenna array is selected by the gradient search method. ; The default initial direction is , the initial step length is and the maximum number of iterations ; S72, based on the initial direction Computing Gradients ;in, Comprehensive quality index For the initial direction The partial derivative vector of ; Comprehensive quality index Direction The partial derivative of Comprehensive quality index Direction The partial derivative of Comprehensive quality index Direction The partial derivative of and is the index of the direction dimension, ; is the transposition operator; S73, the initial direction is updated by the direction update formula Make adjustments; the direction update formula is ;in, for the updated direction; is the initial update step size; the step size adjustment formula is used to dynamically adjust the update step size; the step size adjustment formula is ;in, is the adjusted step length; For the The update step size at the iteration; is the attenuation coefficient; is the current iteration number; S74, repeat steps S72-S73 until the preset maximum number of iterations is reached Stop when the target direction of the antenna array is obtained. ; S75. Based on the collected real-time signal data, a signal covariance matrix is ​​constructed; the beam direction vector is optimized through the beamforming algorithm. When the antenna array is in the target direction, When the signal gain on is maximized, the optimal beam direction vector is obtained; the amplitude and phase of the N units of the antenna array are adjusted according to the optimal beam direction vector, and then the corresponding gain adjustment strategy is obtained.

8. The method for controlling the antenna of a new energy vehicle according to claim 7, characterized in that: The method for obtaining the optimal beam direction vector includes: S81, preset antenna array in the target direction The signal gain on , construct the optimization objective function; the optimization objective function is: ;in, is the signal gain; is the beam direction vector, and the constraint condition of the beam direction vector is ; is the conjugate transpose of the beam direction vector; is the conjugate transpose operator; is the signal covariance matrix; is the interference covariance matrix; is the signal adjustment coefficient factor; The signal adjustment coefficient factor is dynamically designed through the coefficient factor adaptive formula, and the coefficient factor adjustment adaptive formula is: ;in, is the angle of the target direction; is the angle of the beam direction; A factor to adjust the interference suppression effect; S82. Combining the optimization objective function with the constraint conditions of the beam direction vector, constructing a Lagrangian function by the Lagrangian multiplier method to solve, and then obtaining the optimal beam direction vector; the Lagrangian function is: ;in, is the Lagrangian function; is the Lagrange multiplier; find the partial derivative of the beam direction vector and make the partial derivative of the beam direction vector equal to zero, and get the eigenvalue problem: ; S84. By solving the eigenvalue problem, the eigenvector corresponding to the maximum eigenvalue is selected as the optimal beam direction vector: ; S85. Adjust the amplitude and phase of the N elements of the antenna array according to the optimal beam direction vector.

9. A device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the device executes the computer program, it implements the vehicle antenna control method for a new energy vehicle as described in any one of claims 1 to 8.

10. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed, the vehicle antenna control method for a new energy vehicle described in any one of claims 1 to 8 is implemented.

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