Automobile Antenna Control Method, Device and Storage Medium for New Energy Vehicles

Abstract

The present invention belongs to the technical field of signal processing. The present invention discloses a method, device and storage medium for controlling an automotive antenna of a new energy vehicle, including: collecting real-time signal data from N units of an antenna array; preliminarily processing the collected real-time signal data to obtain a comprehensive feature data set; training a quality evaluation model based on the comprehensive feature data set to predict a comprehensive communication quality index; evaluating whether the communication quality meets the standard according to the comprehensive communication quality index; if the communication quality does not meet the standard, dynamically adjusting the antenna gain through a beamforming algorithm to obtain a corresponding gain adjustment strategy; implementing the corresponding gain adjustment strategy until the communication quality meets the standard; so as to optimize the communication link and ensure the stability and quality of communication.

Description

Technical Field

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

[0002] The patent with the patent publication number CN117118539A provides an automotive antenna, device, storage medium, and control method for a new energy vehicle. When the automotive antenna of the new energy vehicle is working, it acquires the antenna radiation signal, maps the antenna radiation signal to the standard range to obtain the standard antenna radiation signal data, performs amplitude-frequency extraction on the standard antenna radiation signal data to obtain the amplitude-frequency characteristics of the antenna radiation conduction, filters the antenna radiation signal according to the determination value of the antenna radiation conduction signal to obtain the preprocessed antenna radiation signal data, determines the average error of the antenna conduction radiation anomaly according to the average frequency of the antenna variable carrier frequency, sends a correction connection instruction to the automotive antenna, and performs reconnection to complete the signal conduction correction control of the automotive antenna of the new energy vehicle. The control of the automotive antenna of the new energy vehicle can be completed by correcting the signal conduction of the automotive antenna of the new energy vehicle to improve the stability of the operation of the automotive antenna.

[0003] The existing methods for controlling automotive antennas of new energy vehicles have the following main problems:

[0004] The direction and gain of the antenna array are not optimized, and 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 remains in the best signal state, and there may be signal fluctuations, increased interference, or an increase in the bit error rate. In severe cases, it will 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. Without dynamic adjustment of the direction and gain of the antenna array, the stability of the system may be greatly reduced.

[0005] The step size is not dynamically adjusted, which may result in an overly large step size causing oscillation and non-convergence, or an overly small step size causing too slow an optimization speed and making it difficult to reach the optimal solution within a reasonable time. The beam direction of the antenna array is not adjusted specifically, and the anti-interference ability of the system is weak. It may not be able to effectively identify and suppress signals from other directions, thus affecting the stability and performance of the communication system.

[0006] By maximizing the signal gain without optimizing the objective function, the antenna array may not achieve the best signal coverage in the target direction, resulting in insufficient signal strength; without optimizing the signal gain, the signal may be attenuated during transmission, leading to insufficient signal strength at the receiving end and unable to meet the requirements of high-quality communication; without dynamically adjusting the relative weights between the signal and interference, the system may not be able to cope with the challenges in the interference environment, making it difficult to distinguish between the signal and interference signals, thus affecting the signal quality; without precisely controlling the output of each unit by adjusting the amplitude and phase of the antenna array, the system may not be able to concentrate the signal in the target direction, and thus cannot fully improve the signal gain.

[0007] In view of this, the present invention proposes an antenna control method, device, and storage medium for new energy vehicles to solve the above problems. Summary of the Invention

[0008] To overcome the above defects of the prior art and to achieve the above object, the present invention provides the following technical solutions: An antenna control method for a new energy vehicle, comprising:

[0009] S1. Collect real-time signal data from N units of the antenna array;

[0010] S2. Perform preliminary processing on the collected real-time signal data to obtain a comprehensive feature data set;

[0011] S3. Train and obtain a quality evaluation model based on the comprehensive feature data set, and predict to obtain a comprehensive communication quality index; evaluate whether the communication quality meets the standard according to the comprehensive communication quality index;

[0012] S4. If the communication quality does not meet the standard, dynamically adjust the antenna gain through a beamforming algorithm to obtain a corresponding gain adjustment strategy;

[0013] S5. Implement the corresponding gain adjustment strategy until the communication quality meets the standard.

[0014] Further, the antenna array is composed of N units arranged, and each unit is an independent antenna element; the real-time signal data includes signal state data, spatial state data, environmental state data, and communication basic data;

[0015] The signal state data includes signal amplitude, signal phase, signal frequency, and signal power; the spatial state data includes angle of arrival, angle of emission, and array configuration; the array configuration includes the phase value and amplitude of the signal of each antenna unit; the environmental state data includes driving speed, vehicle position, obstacle distribution, interference signal frequency, interference signal strength, and weather conditions; the communication basic data includes communication task type, communication link rate, and communication delay.

[0016] Furthermore, the method for preliminarily processing the collected real-time signal data includes:

[0017] S31. Identify and remove outliers in the real-time signal data using the box plot method, and fill in the missing values in the real-time signal data using linear interpolation;

[0018] S32. Extract features from the real-time signal data through fast Fourier transform. Denote the real-time signal data as: ; where is the real-time signal data; is the signal state data; is the spatial state data, and the spatial state data is a combination of each antenna signal; is the environmental state data; is the communication basic data;

[0019] S33. Perform fast Fourier transform on the real-time signal data to obtain the frequency-domain signal: ; where is the frequency-domain signal obtained after fast Fourier transform; is each point of the real-time signal in the time domain; is the frequency variable, representing each point in the frequency domain after fast Fourier transform; is the imaginary unit; is the complex exponential function, representing the rotation factor of the real-time signal in the frequency domain; is pi; is the integral operation symbol;

[0020] Calculate the modulus value of the frequency-domain signal through the signal amplitude calculation formula to obtain the amplitude of the frequency-domain signal. The signal amplitude calculation formula is: ; where 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;

[0021] S34. Find the position with the largest amplitude in the frequency domain as the main frequency of the frequency-domain signal: ; where is the main frequency of the frequency-domain signal;

[0022] S35. Extract the main frequency of the frequency-domain signal, integrate the frequency-domain features extracted from the real-time signal data into a real-time signal feature dataset , and perform standard deviation normalization on the real-time signal feature dataset to obtain the normalized comprehensive feature dataset.

[0023] Further, the method for obtaining the quality evaluation model includes:

[0024] Divide the data set into a training set, a validation set, and a test set, train the model, evaluate the model performance, and verify the model generalization ability; the sample set is a subset of the data set, and each sample set includes a historical comprehensive feature data set and the corresponding comprehensive communication quality index; select GBDT as the specific implementation of the gradient boosting tree model to handle the regression task;

[0025] Initialize the GBDT parameters and define the objective function; the GBDT parameters include the maximum number of leaf nodes in each tree, the learning rate, the number of trees, and the maximum depth of the tree; use the historical comprehensive feature data set as the input data and the corresponding comprehensive communication quality index as the output label to train the quality evaluation model; the quality evaluation model is a gradient boosting tree model;

[0026] Use the mean squared error as the loss function to measure the difference between the predicted value and the actual value of the model; in each iteration, GBDT constructs a new decision tree to fit the negative gradient of the loss function in 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;

[0027] Use the validation set to evaluate the performance of the model, tune the model, and adjust the model parameters according to its performance feedback until the preset stop condition is reached; use the trained quality evaluation model to predict the current comprehensive feature data set to obtain the comprehensive communication quality index.

[0028] Further, the comprehensive communication quality index is composed of a signal strength index, a signal-to-dry ratio index, a bit error rate index, and a signal quality index through weighted averaging.

[0029] Further, the method for evaluating whether the communication quality meets the standard according to the comprehensive communication quality index:

[0030] Compare the predicted comprehensive communication quality index with the preset comprehensive communication quality index threshold:

[0031] 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;

[0032] If the predicted comprehensive communication quality index is less than the preset comprehensive communication quality index threshold, it is determined that the communication quality does not meet the standard.

[0033] Further, the method for dynamically adjusting the antenna gain through the beamforming algorithm to obtain the corresponding gain adjustment strategy if the communication quality does not meet the standard includes:

[0034] S71. Select the target direction of the antenna array through the gradient search method based on the obtained comprehensive communication quality index ; The preset initial direction is , the initial step size is and the maximum number of iterations ;

[0035] S72. Calculate the gradient based on the initial direction ; Among them, is the partial derivative vector of the comprehensive quality index with respect to the initial direction ; is the partial derivative of the comprehensive quality index with respect to the direction ; is the partial derivative of the comprehensive quality index with respect to the direction ; is the partial derivative of the comprehensive quality index with respect to the direction ; and are the indices of the direction dimension, ; is the transpose operator;

[0036] S73. Adjust the initial direction through the direction update formula; The direction update formula is ; Among them, is the updated direction; is the initial update step size; The update step size is dynamically adjusted using the step size adjustment formula; The step size adjustment formula is ; Among them, is the adjusted step size; is the update step size at the th iteration; is the attenuation coefficient; is the current iteration number;

[0037] S74. Repeat steps S72 - S73 until the preset maximum number of iterations is reached and then stop, thereby obtaining the target direction of the antenna array;

[0038] S75. Based on the collected real - time signal data, construct a signal covariance matrix; Optimize the beam direction vector through the beamforming algorithm. When the signal gain of the antenna array in the target direction is maximized, obtain the optimal beam direction vector; Adjust the amplitude and phase of the N units of the antenna array according to the optimal beam direction vector, thereby obtaining the corresponding gain adjustment strategy.

[0039] Further, the method for obtaining the optimal beam direction vector includes:

[0040] S81. Preset the signal gain of the antenna array in the target direction as , and construct an optimization objective function; the optimization objective function is: ; where 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;

[0041] Dynamically design the signal adjustment coefficient factor through the coefficient factor adaptive formula, and the coefficient factor adjustment adaptive formula is: ; where is the angle of the target direction; is the angle of the beam direction; is the factor for adjusting the interference suppression effect;

[0042] S82. Combine the optimization objective function and the constraint condition of the beam direction vector, and construct a Lagrangian function through the Lagrange multiplier method for solution, and then obtain the optimal beam direction vector; the Lagrangian function is: ; where is the Lagrangian function; is the Lagrange multiplier; take the partial derivative of the beam direction vector and make the partial derivative of the beam direction vector equal to zero to obtain an eigenvalue problem: ;

[0043] S84. By solving this eigenvalue problem, select the eigenvector corresponding to the largest eigenvalue as the optimal beam direction vector: ;

[0044] S85. Adjust the amplitude and phase of the N units of the antenna array according to the optimal beam direction vector.

[0045] A device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the vehicle antenna control method for new energy vehicles.

[0046] A storage medium stores a computer program thereon. When the computer program is executed, it implements a method for controlling an automotive antenna of a new energy vehicle.

[0047] The present invention provides a method, device, and storage medium for controlling an automotive antenna of a new energy vehicle, having the following beneficial effects:

[0048] By using the gradient search method and beamforming algorithm to dynamically adjust the direction and gain of the antenna array, the signal gain can be maximized, ensuring that the antenna array always points to the optimal direction, improving the signal coverage and intensity; by real-time monitoring of the comprehensive communication quality index, it can ensure that the system always remains in the optimal state, enhancing the user experience and system stability; the gradient search method enables the system to perform real-time optimization in a complex environment by continuously adjusting the target direction of the antenna array. As the communication environment changes (e.g., user position change, appearance of interference sources, etc.), this method can dynamically adjust the antenna gain to adapt to different scenarios;

[0049] The step size adjustment formula makes the update step size dynamically change in each iteration, which helps to 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, it ensures that the signal gain is maximized and reduces the interference effect; by using the beamforming algorithm to optimize the beam direction vector of the antenna array, it can effectively focus the signal while suppressing interference from other directions, enabling the signal to maintain high-quality transmission in a complex communication environment;

[0050] By optimizing the objective function to maximize the signal gain, the antenna array can obtain the best signal coverage in the target direction, helping 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 influence of interference; in the case of strong interference, increasing the weight of the interference suppression effect factor reduces the impact of interference on signal quality; by adjusting the amplitude and phase of the antenna array, it can precisely control the output of each antenna element, optimizing the beam direction and signal coverage. The adjustment of the amplitude and phase of each antenna element ensures the maximization of signal enhancement in the optimal beam direction, thereby enhancing the overall system efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic flowchart of the method for controlling an automotive antenna of a new energy vehicle according to the present invention;

[0052] Figure 2 It is a schematic structural diagram of the automotive antenna control system of a new energy vehicle according to the present invention;

[0053] Figure 3 It is a schematic diagram of the device according to the present invention;

[0054] Figure 4 Schematic diagram of the storage medium of the present invention. Specific embodiments

[0055] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0056] Embodiment 1, please refer to Figure 1 , in this embodiment, the method for controlling an automotive antenna of a new energy vehicle includes:

[0057] S1. Collect real-time signal data from N units of the antenna array;

[0058] S2. Perform preliminary processing on the collected real-time signal data to obtain a comprehensive feature data set;

[0059] S3. Train and obtain a quality evaluation model according to the comprehensive feature data set, and predict to obtain a comprehensive communication quality index; evaluate whether the communication quality meets the standard according to the comprehensive communication quality index;

[0060] S4. If the communication quality does not meet the standard, dynamically adjust the antenna gain through the beamforming algorithm to obtain a corresponding gain adjustment strategy;

[0061] S5. Implement the corresponding gain adjustment strategy until the communication quality meets the standard.

[0062] The antenna array is composed of N units arranged, and each unit is an independent antenna element; the antenna array is widely used in wireless communication, radar, satellite communication, 5G, automotive communication, aerospace and other fields; especially in new energy vehicles, the antenna array can be used for functions such as in-vehicle communication systems, navigation systems, and radar detection. For example, in in-vehicle communication, through the beam control of the antenna array, it can help achieve environmental perception and high-precision satellite navigation of autonomous vehicles. The real-time signal data includes signal state data, spatial state data, environmental state data, and communication basic data;

[0063] The signal state data includes signal amplitude, signal phase, signal frequency, and signal power; the spatial state data includes angle of arrival, angle of departure, and array configuration; the array configuration includes the phase value and amplitude of the signal of each antenna unit; the environmental state data includes driving speed, vehicle position, obstacle distribution, interference signal frequency, interference signal strength, and weather conditions; the communication basic data includes communication task type, communication link rate, and communication delay.

[0064] The phase value of each antenna unit signal, which is used to implement beamforming and direction control. By adjusting the phase difference, the interference direction of the signal is controlled, thereby forming a directional beam to achieve signal enhancement or interference suppression in a specific direction; the amplitude of each antenna unit signal, which is used to control the distribution of signal strength. By adjusting the amplitude distribution, the beam shape and sidelobe suppression are optimized to ensure the power efficiency of the antenna array; the angle of arrival refers to the receiving direction at the location of the receiving antenna. In the antenna array, measuring the angle of arrival can help implement beamforming and optimize signal reception to improve communication quality; the angle of departure refers to the angle at which the signal propagates from the transmitting antenna in any direction. In the antenna array, the transmitting direction (i.e., the angle of departure) of the signal can be precisely adjusted by controlling the signal phase of the transmitting antenna to achieve beam orientation or focusing.

[0065] The methods for preliminary processing of the collected real-time signal data include:

[0066] S31. Identify and remove the outliers in the real-time signal data using the box plot method, and fill the missing values in the real-time signal data using the linear interpolation method;

[0067] S32. Extract the features of the real-time signal data through the fast Fourier transform. Denote the real-time signal data as: ; where is the real-time signal data; is the signal status data; is the spatial status data, and the spatial status data is a combination of each antenna signal; is the environmental status data; is the communication basic data;

[0068] S33. Perform the fast Fourier transform on the real-time signal data to obtain the frequency-domain signal: ; where is the frequency-domain signal obtained after the fast Fourier transform; is each point of the real-time signal in the time domain; is the frequency variable, representing each point in the frequency domain after the fast Fourier transform; is the imaginary unit; is the complex exponential function, representing the rotation factor of the real-time signal in the frequency domain; is the pi; is the integral operation symbol;

[0069] Calculate the modulus value of the frequency-domain signal through the signal amplitude calculation formula to obtain the amplitude of the frequency-domain signal. The signal amplitude calculation formula is: ; where 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;

[0070] S34. Find the position with the largest amplitude in the frequency domain as the main frequency of the frequency-domain signal: ; where is the main frequency of the frequency-domain signal;

[0071] S35. Extract the main frequency of the frequency-domain signal, integrate the frequency-domain features extracted from the real-time signal data into a real-time signal feature dataset , for the real-time signal feature dataset Perform standard deviation normalization processing to obtain a normalized comprehensive feature dataset.

[0072] The method for obtaining the quality assessment model includes:

[0073] Divide the dataset into a training set, a validation set, and a test set, train the model, evaluate the model performance, and verify the model generalization ability; the sample set is a subset of the dataset, and each sample set includes a historical comprehensive feature dataset and the corresponding comprehensive communication quality index; select GBDT as the specific implementation of the gradient boosting tree model to handle the regression task;

[0074] Initialize the GBDT parameters and define the objective function; the GBDT parameters include the maximum number of leaf nodes in each tree, the learning rate, the number of trees, and the maximum depth of the tree; use the historical comprehensive feature dataset as the input data and the corresponding comprehensive communication quality index as the output label to train the quality assessment model; the quality assessment model is a gradient boosting tree model;

[0075] Use the mean squared error as the loss function to measure the difference between the predicted value and the actual value of the model; in each iteration, GBDT constructs a new decision tree to fit the negative gradient of the previous step's loss function, 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;

[0076] Use the validation set to evaluate the performance of the model, tune the model, and adjust the model parameters according to its performance feedback until the preset stop condition is reached; use the trained quality assessment model to predict the current comprehensive feature dataset to obtain the comprehensive communication quality index.

[0077] The comprehensive communication quality index is composed of a signal strength index, a signal-to-interference-plus-noise ratio index, a bit error rate index, and a signal quality index through weighted averaging.

[0078] The method for evaluating whether the communication quality meets the standard according to the comprehensive communication quality index:

[0079] Compare the predicted comprehensive communication quality index with the preset comprehensive communication quality index threshold:

[0080] 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;

[0081] If the predicted comprehensive communication quality index is less than the preset comprehensive communication quality index threshold, it is determined that the communication quality does not meet the standard.

[0082] If the communication quality does not meet the standard, the method for dynamically adjusting the antenna gain through the beamforming algorithm to obtain the corresponding gain adjustment strategy includes:

[0083] S71. Based on the obtained comprehensive communication quality index, select the target direction of the antenna array through the gradient search method ; The preset initial direction is , the initial step size is and the maximum number of iterations ;

[0084] S72. Calculate the gradient based on the initial direction ; Among them, is the comprehensive quality index partial derivative vector of the initial direction ; is the comprehensive quality index partial derivative of with respect to the direction , indicating the influence degree of the direction adjustment on the comprehensive quality index ; is the comprehensive quality index partial derivative of with respect to the direction ; is the comprehensive quality index partial derivative of with respect to the direction ; and are the indexes of the direction dimension, ; is the transpose operator;

[0085] S73. Adjust the initial direction through the direction update formula; The direction update formula is ; Among them, is the updated direction; is the initial update step size, used to control the update amplitude; The step size is dynamically adjusted using the step size adjustment formula; The step size adjustment formula is ; Among them, is the adjusted step size; is the The update step size at the next iteration; is the attenuation coefficient, used to balance the reduction speed of the step size; is the current iteration number;

[0086] S74. Repeat steps S72 - S73 until reaching the preset maximum number of iterations and then stop, thereby obtaining the target direction of the antenna array ;

[0087] S75. Based on the collected real - time signal data, construct the signal covariance matrix; optimize the beam direction vector through the beamforming algorithm. When the signal gain of the antenna array in the target direction is maximized, obtain the optimal beam direction vector; adjust the amplitudes and phases of the N units of the antenna array according to the optimal beam direction vector, thereby obtaining the corresponding gain adjustment strategy;

[0088] The beamforming algorithm is an advanced signal processing technology that allows forming a specific beam direction by adjusting the amplitudes and phases of individual units 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 improve the communication quality but also enhances the overall performance and efficiency of the system.

[0089] The method for obtaining the optimal beam direction vector includes:

[0090] S81. Preset the signal gain of the antenna array in the target direction as , and construct the optimization objective function; the optimization objective function is: ; where, 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, used to adjust the relative weight between the target signal and the interference signal;

[0091] Dynamically design the signal adjustment coefficient factor through the coefficient factor adaptive formula, and the coefficient factor adjustment adaptive formula is: ; where, is the angle of the target direction; is the angle of the beam direction; is the factor for adjusting the interference suppression effect, controlling the influence degree of the interference signal;

[0092] S82. Combine the optimization objective function and the constraint conditions of the beam direction vector, construct a Lagrangian function through the Lagrange multiplier method for solution, and then obtain the optimal beam direction vector; the Lagrangian function is: ; where is the Lagrangian function; is the Lagrange multiplier; take the partial derivative with respect to the beam direction vector and make the partial derivative of the beam direction vector equal to zero to obtain 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 constraint conditions.

[0093] S84. By solving this eigenvalue problem, select the eigenvector corresponding to the largest eigenvalue as the optimal beam direction vector: ;

[0094] S85. Adjust the amplitude and phase of the N units of the antenna array according to the optimal beam direction vector; the amplitude of the N units of the adjusted antenna array is ; where is the amplitude of the N units of the adjusted antenna array; is the amplitude corresponding to the N units of the antenna array in the optimal beam direction vector; the phase of the N units of the adjusted antenna array is ; where is the phase of the N units of the adjusted antenna array; is the phase corresponding to the N units of the antenna array in the optimal beam direction vector.

[0095] The preset comprehensive communication quality index threshold is set by the staff. Different comprehensive communication quality indexes are collected through the vehicle antenna control terminal, and the average value of multiple comprehensive communication quality indexes is taken as the preset comprehensive communication quality index threshold.

[0096] In this embodiment, the gradient search method and the beamforming algorithm are used to dynamically adjust the direction and gain of the antenna array, which can maximize the signal gain, ensure that the antenna array always points to the best direction, and improve the signal coverage and strength; by real-time monitoring of the comprehensive communication quality index, it can ensure that the system always maintains the best state, improving the user experience and the stability of the system; the gradient search method can make the system perform real-time optimization in a complex environment by continuously adjusting the target direction of the antenna array. 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;

[0097] The step-size adjustment formula enables the update step-size to vary dynamically in each iteration, which helps avoid convergence problems caused by overly large or small step-sizes, making the optimization process more stable and precise; by optimizing the beam direction vector, the signal gain is ensured to be maximized, reducing the interference effect; 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, enabling high-quality signal transmission in a complex communication environment;

[0098] By maximizing the signal gain through optimizing the objective function, the antenna array can obtain the best signal coverage in the target direction, which helps improve communication quality and reduce signal loss or attenuation; the dynamic adjustment formula of the coefficient factor enables the system to adjust the weights of the signal and interference according to the actual situation, thereby effectively controlling the impact of interference; in the case of strong interference, the weight of the interference suppression effect factor is increased to reduce the impact of interference on signal quality; by adjusting the amplitude and phase of the antenna array, the output of each antenna element can be precisely controlled, optimizing the beam direction and signal coverage. The adjustment of the amplitude and phase of each antenna element ensures the maximization of signal enhancement in the optimal beam direction, thereby enhancing the overall system performance.

[0099] Example 2, please refer to Figure 2 As shown, for the parts not described in detail in this example, refer to the description in Example 1. A vehicle antenna control system for a new energy vehicle is provided, including:

[0100] A data acquisition module for acquiring real-time signal data from N units of the antenna array;

[0101] A data processing module for preliminarily processing the acquired real-time signal data to obtain a comprehensive feature dataset;

[0102] A quality assessment module for training a quality assessment model based on the comprehensive feature dataset to predict a comprehensive communication quality index; and evaluating whether the communication quality meets the standard according to the comprehensive communication quality index;

[0103] A gain adjustment module, if the communication quality does not meet the standard, dynamically adjusts the antenna gain through the beamforming algorithm to obtain a corresponding gain adjustment strategy;

[0104] A strategy implementation module for implementing the corresponding gain adjustment strategy until the communication quality meets the standard.

[0105] 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. Among them, computer-readable code is stored in the memory, and when the computer-readable code is run by one or more processors, it can execute the vehicle antenna control method for a new energy vehicle as described above.

[0106] As Figure 3 shown, the device 500 may include a bus 501, one or more CPUs 502, a ROM 503, a 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 the ROM 503 or the hard disk 507, may store the automotive antenna control method for a new energy vehicle provided by the present application. Further, the device 500 may also include a user interface 508. Of course, Figure 2 the architecture shown is only exemplary. When implementing different devices, one or more components in the Figure 2 shown electronic device may be omitted according to actual needs.

[0107] Example 4. Refer to Figure 4 shown, which is a storage medium 250 according to an embodiment of the present application. A computer-readable instruction is stored on the storage medium 250. When the computer-readable instruction is run by a processor, it can execute the automotive antenna control method for a new energy vehicle according to the embodiment of the present application described with reference to the above drawings. 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, etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0108] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present invention can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0109] In 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 illustrative. For example, the division of the units is only one type, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other form.

[0110] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the said claims.

[0111] Finally: The above description is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within 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; 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 the preset comprehensive communication quality index threshold, it is determined that the communication quality does not meet the standard; 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; 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; 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 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.

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 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.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the electronic device executes the computer program, the vehicle antenna control method for the new energy vehicle described in any one of claims 1 to 6 is implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the vehicle antenna control method for a new energy vehicle according to any one of claims 1 to 6 is implemented.

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