Wireless control system for intelligent trolley

Abstract

The invention relates to the technical field of communication control, and discloses a wireless control system for an intelligent trolley, which comprises a wireless module, a controller module, a communication adaptive module, a voltage reduction module, an ultrasonic module and a motor driving module. Wherein the communication adaptive module analyzes a communication environment in real time through a machine learning algorithm, and dynamically adjusts communication protocol parameters of the wireless module according to an analysis result so as to adapt to different communication conditions. The module can collect and analyze various key communication environment parameters including signal intensity, noise level, channel occupancy rate and the like, thereby ensuring that the system can maintain high communication efficiency and stable connection in various complex and changeable communication environments. According to the wireless control system, the performance of the intelligent trolley and the universality of application scenes are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of communication control, in particular to a wireless control system for an intelligent car. Background Art

[0002] In recent years, countries around the world have paid more and more attention to the research of mobile intelligent robots. Mobile intelligent robots have broad application prospects in difficult fields such as military reconnaissance, geological survey, mine clearance and harsh environment monitoring. The wireless control system of the intelligent car, as its core component, directly determines its performance and the breadth of its application scenarios.

[0003] The existing system has deficiencies in the adaptability of communication protocols. Traditional wireless control systems often use fixed communication protocol parameters and cannot be dynamically adjusted according to the actual communication environment (such as signal strength and interference). As a result, the communication efficiency and stability of the system are significantly reduced in a complex and changeable communication environment, affecting the remote control and real-time data transmission of the smart car. Therefore, we propose a wireless control system for the smart car. Summary of the invention

[0004] The purpose of the present invention is to provide a wireless control system for an intelligent car to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: a wireless control system for an intelligent car, comprising a wireless module, a controller module, a communication adaptive module, a step-down module, an ultrasonic module and a motor drive module;

[0006] The wireless module is used to realize the data transmission and reception function, wherein when sending data, the data is modulated and frequency synthesized and then sent through the antenna; when receiving data, the collected signal is demodulated and then sent to the controller module;

[0007] The controller module is used to receive instructions from the wireless module and control the operation mode of the smart car according to the instructions;

[0008] The communication adaptive module analyzes the communication environment through a machine learning algorithm and dynamically adjusts the communication protocol parameters of the wireless module according to the analysis results to adapt to different communication conditions;

[0009] The communication adaptive module includes a data acquisition unit, a data preprocessing unit, an online decision unit and a parameter adjustment unit. The data acquisition unit is used to collect key parameters in the communication environment in real time, including signal strength, noise level, channel occupancy, signal-to-noise ratio, bit error rate, delay time, packet loss rate, frequency offset and phase noise; the data preprocessing unit is used to clean the collected communication environment data to process missing values ​​and abnormal values; the online decision unit is used to input the preprocessed communication environment data into a pre-trained parameter prediction model, and the parameter prediction model outputs the optimal communication protocol parameters according to the current communication environment, including the modulation mode, coding rate, transmission power, data packet size, retransmission mechanism, carrier frequency and bandwidth of the communication protocol; the parameter adjustment unit dynamically adjusts the communication protocol parameters of the wireless module according to the output of the parameter prediction model;

[0010] The step-down module is used to adjust the input power supply voltage to a working voltage suitable for stable operation of the system;

[0011] The ultrasonic module is used to detect and measure the distance of obstacles ahead by transmitting and receiving ultrasonic signals;

[0012] The motor drive module is used to receive instructions from the controller module to control the motor movement of the smart car.

[0013] Preferably, the wireless module includes a plurality of data packet parsing units to match and parse a variety of communication protocols and data transmission rates to achieve communication between multiple channels.

[0014] Preferably, the controller module is based on the STM32F103C8T6 chip, and specifically includes:

[0015] The four-way PWM output function of the STM32F103C8T6 chip is used to achieve precise control of the motor of the smart car;

[0016] Utilize the four-way external interrupt input function of the STM32F103C8T6 chip to achieve rapid response to external signals;

[0017] Use the serial port communication function of the STM32F103C8T6 chip to realize communication with other internal modules;

[0018] The high PID calculation speed of the STM32F103C8T6 chip is used to accurately calculate and control the motion state of the smart car;

[0019] The STM32F103C8T6 chip is powered by 3.3V, and peripheral circuits are designed, including reset circuit, clock circuit and filter circuit, to ensure the normal operation of the chip and meet the required functional requirements.

[0020] Preferably, the step of obtaining the parameter prediction model includes:

[0021] Step 1: Collect operation data in the communication environment, including communication environment parameters and corresponding communication protocol parameters;

[0022] Step 2: Standardize the collected data to eliminate the impact of data dimension differences on model training and form a regular data set; and divide the processed data set into training set, validation set and test set;

[0023] Step 3: Select the linear regression model to build a parameter prediction model. The algorithm formula of the linear regression model is:

[0024] y=β0+β1x1+β2x2+...+β n x n +∈

[0025] Among them, y is the target variable, representing the communication protocol parameters; x is the input variable, representing the communication environment parameters; β is the regression coefficient, determined by model training; α is the intercept term; ε is the error term;

[0026] Step 4: Set initial parameters for the selected prediction model and prepare for training;

[0027] Step 5: Use the training set to train the parameter prediction model, and continuously adjust the model parameters through the optimization algorithm. The purpose of model training is to estimate the regression coefficient β so that the error between the predicted value and the true value is minimized; when initializing the model parameters, assign an initial value to the parameter β corresponding to each feature x in the linear regression model, and also assign an initial value to the intercept term α; use the data in the training set and continuously iterate and update the parameters of the linear regression model through the gradient descent algorithm to minimize the loss function value between the true value and the predicted value.

[0028] Use the validation set to validate the trained model and adjust the model parameters or structure based on the validation results; use the test set to comprehensively evaluate the optimized model using the set evaluation indicators to determine whether the model performance meets the expected standards;

[0029] Step 6: Save the trained and verified parameter prediction model for subsequent use in real-time prediction and optimization of the communication protocol parameters of the wireless module in the communication adaptive module.

[0030] Preferably, in step 2, the Z-score standardization method is used to subtract the mean of each numerical data and divide it by its standard deviation so that the processed data conforms to the standard normal distribution. The formula for standardization is: Among them, X is the original data, μ is the mean of the original data, σ is the standard deviation of the original data, and Z is the standardized data.

[0031] Preferably, in step 5, the mean square error MSE algorithm is used to evaluate the output error of the parameter prediction model. The specific algorithm is:

[0032]

[0033] Where n is the number of data points, Y pred,i is the communication protocol parameter predicted by the model, Y actual,i is the actual communication protocol parameter; the prediction accuracy of the model is evaluated according to the MSE value. The smaller the MSE value, the higher the prediction accuracy of the model.

[0034] Preferably, the data and processing unit uses a linear interpolation algorithm to fill in missing values; and uses a threshold judgment method to fill in outliers, treating data points that exceed a preset threshold range as outliers and replacing them with the mean of their neighboring points.

[0035] Preferably, the ultrasonic module uses an ultrasonic sensor HC_SR04 module for ranging, and the specific method is:

[0036] Use the Trig pin of the HC_SR04 module to receive a high-level pulse signal of more than 10 microseconds to trigger the module to emit ultrasonic pulses;

[0037] After receiving the trigger signal, the HC_SR04 module transmits 8 40 Hz ultrasonic pulses;

[0038] After the ultrasonic wave is transmitted, the HC_SR04 module receives the echo signal, and after receiving the signal, the Echo pin of the HC_SR04 module is set to a high level state to indicate that the echo signal has been received;

[0039] The Echo pin is used to generate a high-level pulse. The width of the pulse indicates the time required for ultrasonic wave to be transmitted and echo to be received.

[0040] By detecting the high-level pulse width of the Echo pin, the ultrasonic propagation time is calculated, and then the distance between the target and the ultrasonic module is calculated based on the sound speed and time.

[0041] Preferably, the system also includes a lamp bead module, which uses an LED light group containing multiple colors to display the operating status of the smart car. The lamp bead module is connected to the controller module through a serial communication method and a single data line; the lamp bead module receives the brightness and color data sent by the controller module; and based on the received data, the brightness and color of the LED light group are controlled.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] By introducing the communication adaptive module, this system can analyze the communication environment in real time and dynamically adjust the communication protocol parameters of the wireless module, thereby ensuring that the system can maintain efficient communication efficiency and stable connection in various complex and changeable communication environments, significantly improving the remote control experience and real-time data transmission performance of the smart car. The communication adaptive module can collect and analyze a variety of key communication environment parameters including signal strength, noise level, channel occupancy, etc., so that the system can automatically optimize the communication settings according to the current environment and effectively respond to communication challenges in different application scenarios, such as signal interference and weak signal areas. By optimizing the communication protocol parameters, this system can reduce the delay time of data transmission, reduce the packet loss rate, ensure that the controller module receives the instructions from the wireless module in a timely and accurate manner, and thus improve the response speed and control accuracy of the smart car. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is the overall structure diagram of the present invention;

[0045] Figure 2 Diagram of the construction and training steps of the parameter prediction model;

[0046] Figure 3 This is the moving direction diagram of the universal wheel;

[0047] Figure 4 This is a physical picture of the smart car. DETAILED DESCRIPTION

[0048] 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.

[0049] See also Figure 1-4 The present invention provides a technical solution: a wireless control system for an intelligent car, including a wireless module, a controller module, a communication adaptive module, a step-down module, an ultrasonic module and a motor drive module.

[0050] The wireless module is responsible for the data transmission and reception functions. When sending data, the wireless module modulates and synthesizes the data and sends it out through the antenna. When receiving data, the wireless module demodulates and processes the collected signal and sends it to the controller module.

[0051] The controller module is used to receive instructions from the wireless module and control the operation mode of the smart car according to these instructions. The controller module parses the instructions and generates corresponding control signals to drive the smart car to perform various actions.

[0052] The communication adaptive module analyzes the current communication environment through a machine learning algorithm, and dynamically adjusts the communication protocol parameters of the wireless module according to the analysis results to adapt to different communication conditions. The communication adaptive module includes a data acquisition unit, a data preprocessing unit, an online decision unit, and a parameter adjustment unit. The data acquisition unit collects key parameters in the communication environment in real time, such as signal strength, noise level, channel occupancy, signal-to-noise ratio, bit error rate, delay time, packet loss rate, frequency offset, and phase noise. The data preprocessing unit cleans the collected communication environment data to process missing values ​​and outliers to ensure the accuracy and reliability of the data. The online decision unit inputs the preprocessed communication environment data into a pre-trained parameter prediction model, which outputs the optimal communication protocol parameters according to the current communication environment, including the modulation mode, coding rate, transmission power, data packet size, retransmission mechanism, carrier frequency, and bandwidth of the communication protocol. The parameter adjustment unit dynamically adjusts the communication protocol parameters of the wireless module according to the output of the parameter prediction model to optimize the communication performance.

[0053] The step-down module is used to reduce the input power supply voltage to a working voltage suitable for stable operation of the system, ensuring the normal operation of each component of the system.

[0054] The ultrasonic module detects and measures obstacles ahead by transmitting and receiving ultrasonic signals. The ultrasonic module sends ultrasonic signals and receives the reflected signals, and determines the distance of obstacles by calculating the time difference of the signals.

[0055] The motor drive module is used to receive instructions from the controller module to control the motor movement of the smart car. The motor drive module generates corresponding drive signals according to the instructions to drive the motor to rotate, thereby controlling the driving direction and speed of the smart car.

[0056] The present invention will be further described below in conjunction with Examples 1 to 4:

[0057] Embodiment 1:

[0058] The wireless module includes multiple data packet parsing units to match and parse multiple communication protocols and data transmission rates to achieve communication between multiple channels. The wireless module is a 2.4GHz low-power, low-cost SPI interface wireless transceiver module that uses radio frequency communication to achieve wireless data transmission.

[0059] The basic features of the wireless module are:

[0060] ①2.4GHz band: The operating frequency of the wireless module is 2.4GHz, with no frequency band requirements.

[0061] ②Multi-channel support: supports 125 channels, which can effectively prevent conflicts with other wireless devices.

[0062] ③SPI interface: SPI interface is used to communicate with the microcontroller, which facilitates the communication between the controller module and the wireless module.

[0063] ④Low power consumption: The wireless module has very small power consumption in low power state and is suitable for battery-driven occasions.

[0064] ⑤Small size: The wireless module is small in size and suitable for small area applications

[0065] The wireless module uses radio frequency communication, supports multiple communication protocols and data transmission rates, and can communicate with each other across multiple channels to eliminate mutual interference.

[0066] The wireless module and the controller module use SPI for communication. The microcontroller can transmit data to the wireless module and receive data from other modules. The transmitted data is modulated and frequency synthesized, and then transmitted via the antenna; the collected signal is demodulated and processed, and then sent to the controller module. The wireless module realizes the conversion between the two modes of transmission and reception, and can operate at lower power, thus saving energy. The wireless module is responsible for sending and receiving data packets, including data verification, address matching and other functions, to ensure the accuracy and security of the data.

[0067] The controller module is based on STM32F103C8T6, and uses multiple on-chip resources such as four-way PWM output, four-way external interrupt input, one-way serial communication, and has a high PID calculation speed. The STM32F103C8T6 chip adopts a 64-pin design and has a large internal storage capacity, which can well meet the needs of this system. In addition, the STM32F103C8T6 chip has the characteristics of low price, which has certain guiding significance for the development of small cars.

[0068] In order to ensure the normal operation of the STM32F103C8T6 chip, it needs to be powered by 3.3V, and its peripheral circuits are designed, such as reset circuit, clock source, filter capacitor, etc. By reasonably laying out the circuit, the normal operation of the STM32F103C8T6 chip can be guaranteed and the required functional requirements can be met.

[0069] The four-way PWM output function of the STM32F103C8T6 chip is used to achieve precise control of the motor of the smart car. By configuring the PWM output parameters of the chip, such as frequency, duty cycle, etc., the speed and direction of the motor can be precisely controlled, thereby achieving precise control of the movement of the smart car.

[0070] The four-way external interrupt input function of the STM32F103C8T6 chip is used to achieve a quick response to external signals. The external interrupt input function allows the chip to immediately interrupt the current program when receiving an external signal and execute the interrupt service program corresponding to the external signal, thereby achieving a quick response and processing of external signals.

[0071] The serial port communication function of the STM32F103C8T6 chip is used to realize communication with other internal modules. The serial port communication function allows the chip to transmit and exchange data with other modules, thereby realizing the collaborative work and function realization between modules.

[0072] The high PID calculation speed of the STM32F103C8T6 chip is used to accurately calculate and control the motion state of the smart car. The PID control algorithm is a commonly used control algorithm that achieves accurate control of the system output by calculating the proportion, integration, and differentiation of the system error. The STM32F103C8T6 chip has high-speed computing power, which can realize fast calculation and execution of the PID algorithm, thereby achieving accurate control of the motion state of the smart car.

[0073] The STM32F103C8T6 chip is powered by 3.3V, and peripheral circuits are designed, including reset circuit, clock circuit and filter circuit, to ensure the normal operation of the chip and meet the required functional requirements. The reset circuit is used to reset the chip when an abnormality occurs and restore the normal working state of the chip; the clock circuit is used to provide the clock signal required for the chip to work; the filter circuit is used to filter the power supply, provide a stable power supply voltage, and ensure the normal operation of the chip.

[0074] Embodiment 2:

[0075] See attached Figure 2 , the steps to obtain the parameter prediction model include:

[0076] In order to obtain the training data required for the parameter prediction model, it is first necessary to collect the operating data in the communication environment, which includes the communication environment parameters and the corresponding communication protocol parameters. Communication environment parameters may include signal strength, noise level, channel occupancy, signal-to-noise ratio, bit error rate, delay time, packet loss rate, frequency offset and phase noise, etc. These parameters reflect the real-time status of the communication environment. The communication protocol parameters include modulation mode, coding rate, transmission power, data packet size, retransmission mechanism, carrier frequency and bandwidth, etc. These parameters determine the performance of the wireless module in a specific communication environment. By actually running the wireless control system of the smart car and recording these parameters under different communication environments, a data set for model training can be formed.

[0077] The collected raw data may have problems such as data dimension differences, missing values ​​or outliers, which will have an adverse effect on model training. Therefore, the collected data needs to be preprocessed. First, data standardization is performed to eliminate the impact of data dimension differences on model training. Data standardization can convert the data into a distribution with a mean of 0 and a standard deviation of 1, so that the numerical ranges between different features remain consistent. Secondly, the processed data set is divided into training set, validation set and test set. The training set is used to train the model, the validation set is used to adjust the model parameters or structure, and the test set is used to evaluate the performance of the model.

[0078] After data preprocessing is completed, select the linear regression algorithm to build a parameter prediction model. The algorithm formula of the linear regression model is:

[0079] y=β0+β1x1+β2x2+...+β n x n +∈

[0080] Among them, y is the target variable, representing the communication protocol parameters; x is the input variable, representing the communication environment parameters; β is the regression coefficient, determined by model training; α is the intercept term; ε is the error term. The purpose of the linear regression model is to find the best regression coefficient β and intercept term α so that the error between the predicted value and the true value is minimized.

[0081] After building the parameter prediction model, you need to set initial parameters for the prediction model and prepare for training, including assigning an initial value to the parameter β corresponding to each feature x in the linear regression model. The initial value can be randomly generated. At the same time, you also need to assign an initial value to the intercept term α. After setting the initial parameters, you can use the training set to train the parameter prediction model.

[0082] The parameter prediction model is trained using the training set. The purpose of the training is to continuously adjust the model parameters through the optimization algorithm to minimize the error between the predicted value and the true value. During the training process, the gradient descent algorithm is used to iteratively update the parameters of the linear regression model to minimize the loss function value between the true value and the predicted value. After the training is completed, a preliminary parameter prediction model will be obtained.

[0083] The preliminary model obtained above may still have some problems, such as overfitting or underfitting. Therefore, it is necessary to use a validation set to validate the trained model. The purpose of validation is to check the generalization ability of the model, that is, the performance of the model on unseen data. If the model does not perform well on the validation set, the model parameters or structure need to be adjusted.

[0084] After the adjustment is completed, the optimized model needs to be fully evaluated using the test set. The purpose of the test set is to provide an independent data set that does not participate in model training and verification to evaluate the performance of the model. Through the set evaluation indicators, such as mean square error, accuracy, etc., it can be judged whether the model performance meets the expected standards.

[0085] If the performance of the model on the test set meets the expected standards, the trained and verified parameter prediction model can be saved for subsequent use in the communication adaptation module to predict and optimize the communication protocol parameters of the wireless module in real time. In actual applications, the communication adaptation module will collect communication environment parameters in real time and input them into the parameter prediction model. The model will output the optimal communication protocol parameters based on the current communication environment. The communication adaptation module will then dynamically adjust the communication protocol parameters of the wireless module based on these parameters to adapt to different communication conditions.

[0086] In the data preprocessing stage, in order to eliminate the impact of data dimension differences on model training, the Z-score standardization method is used to process the collected numerical data. The Z-score standardization method is a commonly used data standardization technology, and its processing formula is:

[0087]

[0088] Among them, X is the original data, μ is the mean of the original data, σ is the standard deviation of the original data, and Z is the standardized data. Through this method, each numerical data is converted into a standard normal distribution, that is, the mean is 0 and the standard deviation is 1, thereby eliminating the difference in the numerical range between different features, making the model training more accurate and effective.

[0089] After the model training is completed, in order to evaluate the performance of the parameter prediction model, the mean square error (MSE) algorithm is used to evaluate the output error of the model. The MSE algorithm is a commonly used regression model evaluation indicator, and its calculation formula is:

[0090]

[0091] Where n is the number of data points, Y pred,i is the communication protocol parameter predicted by the model, Y actual,i is the actual communication protocol parameter. The MSE value represents the mean square error between the model prediction value and the actual value. The smaller the value, the higher the prediction accuracy of the model and the better the performance. By calculating the MSE value, the performance of the parameter prediction model can be quantitatively evaluated to determine whether the model meets the expected standards.

[0092] Embodiment 3:

[0093] The ultrasonic module uses an ultrasonic sensor HC_SR04 module for distance measurement. HC_SR04 is a relatively common distance measurement module at present. Its principle is to use ultrasonic signals as signals and then use the signal transmission time to measure the distance.

[0094] HC_SR04 main features include:

[0095] ① Accurate distance measurement: The HC-SR04 module can provide relatively accurate distance measurement, usually between 2 cm and 400 cm.

[0096] ②Easy to use: Using the HC-SR04 module for ranging usually only requires a few simple pin connections and a small amount of programming code.

[0097] ③ Low cost: The HC-SR04 module is inexpensive and suitable for projects of all budgets.

[0098] ④High performance: fast response speed, able to complete ranging operations in a short time.

[0099] How HC_SR04 works:

[0100] When the Trig pin receives a high-level pulse of more than 10 microseconds, the module sends out 8 40 Hz ultrasonic pulses.

[0101] After the ultrasonic wave is emitted, the received echo signal is processed and set to a high level, thereby realizing the detection of the target.

[0102] After receiving the echo signal, the Echo pin generates a high-level pulse indicating the time required for the ultrasonic wave to be transmitted and the echo to be transmitted.

[0103] By detecting the pulse width of the echo pin, the echo loop time is obtained, and then the echo distance is obtained.

[0104] The system also includes a lamp bead module, which uses an LED light group containing multiple colors to display the operating status of the smart car. The lamp bead module uses a WS2812B light source. WS2812B is a lamp bead module that integrates RGB LED and control circuit. It has high integration, simple control, easy programming, and continuous cascading. The brightness and color of multiple WS2812B lamp beads can be controlled through only one data line.

[0105] WS2812B consists of an RGB LED and a control chip, and is connected to the controller module via serial communication and a single data line. Each WS2812B lamp bead module has a unique address, and when transmitting data, the controller sends the corresponding brightness and color data according to the address.

[0106] Working principle of WS2812B: The controller decodes the data while sending it. Based on the received data, the module controls the brightness and color of the RGB LED. Each luminous ball consists of 3 LEDs (red, green, and blue), which can produce a variety of color effects according to the brightness and color changes of the light-emitting diodes.

[0107] The buck module is a common DC-DC converter with the characteristics of small size, high efficiency and good stability. It is used to reduce high voltage to a lower voltage. It can convert the input DC voltage into a lower output DC voltage to meet the power supply voltage requirements of electronic equipment.

[0108] The buck module consists of an input voltage terminal, an output voltage terminal and some control circuits. The working principle of the buck module is basically to use switching devices (such as MOSFET), inductors and capacitors to achieve power conversion. The specific working process is as follows:

[0109] (1) providing the input voltage from the input end to the step-down module, and filtering and stabilizing the voltage through the input filter circuit;

[0110] (2) The control circuit turns the switch element on and off according to the set output voltage and load changes to generate a higher frequency switching voltage.

[0111] (3) In the on state of the switching device, the input voltage is stored by the inductor, and a portion of it is stored in the capacitor.

[0112] (4) When the switching device is in an off state, the inductor releases the stored energy and transmits it to the output terminal via the output circuit, and the capacitor provides the stored charge to the load.

[0113] (5) The stability and efficiency of the output voltage can be adjusted as required and can be adjusted by controlling the duty cycle and frequency of the switching voltage.

[0114] The motor drive module uses a dual-channel H-bridge DC motor driver to control the DC motor. The motor drive module is a system that controls the input signal and can complete the forward, reverse, brake, disconnect and other functions of the motor. The working principle of the motor drive module is to adjust the voltage and current of the motor by controlling the input signal, thereby controlling the speed and direction of the motor. The internal integrated H-bridge drive circuit can make the motor forward, reverse or brake according to the control of the input signal. In addition, the motor drive module also has overcurrent protection and thermal shutdown protection functions to protect the motor and driver from damage.

[0115] Embodiment 4:

[0116] See attached Figure 3 and 4 The wheels of the smart car use 48mm Mecanum wheels with 3mm couplings. The universal Mecanum wheel is a Mecanum gear drive axle consisting of a Mecanum gear. It has a large contact area and good handling performance, which can achieve smooth directional control and efficient movement.

[0117] 3mm coupling is a device used to connect the shaft and the wheel, used in the shaft-wheel transmission system. It is usually made of flexible materials (such as rubber), has a certain flexibility and reliable connection, and can withstand the torsion and offset between the shaft and the wheel.

[0118] The combination of a 48mm McReel and a 3mm coupling can transfer the rotational force of the motor to the McReel, allowing the robot or small vehicle to control the direction and move. The function of the coupling is to connect the motor shaft and the McReel shaft, while allowing a certain axial and radial offset, thereby reducing the vibration and friction of the transmission system and improving the transmission efficiency and reliability.

[0119] The motor is controlled by combining a motor, a 48mm wheel and a 3mm coupling, and then two motor drive modules are used. Finally, in the control program, the speed and direction of each motor are controlled using PWM control to achieve multiple movement directions such as forward, backward, left turn, right turn and various oblique movement directions.

[0120] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0121] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A wireless control system for an intelligent car, characterized in that: It includes a wireless module, a controller module, a communication adaptive module, a buck module, an ultrasonic module and a motor drive module; The wireless module is used to realize the data transmission and reception function, wherein when sending data, the data is modulated and frequency synthesized and then sent through the antenna; when receiving data, the collected signal is demodulated and then sent to the controller module; The controller module is used to receive instructions from the wireless module and control the operation mode of the smart car according to the instructions; The communication adaptive module analyzes the communication environment through a machine learning algorithm and dynamically adjusts the communication protocol parameters of the wireless module according to the analysis results to adapt to different communication conditions; The communication adaptive module includes a data acquisition unit, a data preprocessing unit, an online decision unit and a parameter adjustment unit. The data acquisition unit is used to collect key parameters in the communication environment in real time, including signal strength, noise level, channel occupancy, signal-to-noise ratio, bit error rate, delay time, packet loss rate, frequency offset and phase noise; the data preprocessing unit is used to clean the collected communication environment data to process missing values ​​and abnormal values; the online decision unit is used to input the preprocessed communication environment data into a pre-trained parameter prediction model, and the parameter prediction model outputs the optimal communication protocol parameters according to the current communication environment, including the modulation mode, coding rate, transmission power, data packet size, retransmission mechanism, carrier frequency and bandwidth of the communication protocol; the parameter adjustment unit dynamically adjusts the communication protocol parameters of the wireless module according to the output of the parameter prediction model; The step-down module is used to adjust the input power supply voltage to a working voltage suitable for stable operation of the system; The ultrasonic module is used to detect and measure the distance of obstacles ahead by transmitting and receiving ultrasonic signals; The motor drive module is used to receive instructions from the controller module to control the motor movement of the smart car.

2. A wireless control system for an intelligent vehicle according to claim 1, characterized in that: The wireless module includes a plurality of data packet parsing units to match and parse a variety of communication protocols and data transmission rates to achieve communication between a plurality of channels.

3. A wireless control system for an intelligent car according to claim 2, characterized in that: The controller module is based on the STM32F103C8T6 chip, and specifically includes: The four-way PWM output function of the STM32F103C8T6 chip is used to achieve precise control of the motor of the smart car; Utilize the four-way external interrupt input function of the STM32F103C8T6 chip to achieve rapid response to external signals; Use the serial port communication function of the STM32F103C8T6 chip to realize communication with other internal modules; The high PID calculation speed of the STM32F103C8T6 chip is used to accurately calculate and control the motion state of the smart car; The STM32F103C8T6 chip is powered by 3.3V, and peripheral circuits are designed, including reset circuit, clock circuit and filter circuit, to ensure the normal operation of the chip and meet the required functional requirements.

4. A wireless control system for an intelligent car according to claim 1, characterized in that: The steps to obtain the parameter prediction model include: Step 1: Collect operation data in the communication environment, including communication environment parameters and corresponding communication protocol parameters; Step 2: Standardize the collected data to eliminate the impact of data dimension differences on model training and form a regular data set; and divide the processed data set into training set, validation set and test set; Step 3: Select the linear regression model to build a parameter prediction model. The algorithm formula of the linear regression model is: y=β0+β1x1+β2x2+...+β n x n +∈ Among them, y is the target variable, representing the communication protocol parameters; x is the input variable, representing the communication environment parameters; β is the regression coefficient, determined by model training; α is the intercept term; ε is the error term; Step 4: Set initial parameters for the selected prediction model and prepare for training; Step 5: Use the training set to train the parameter prediction model, and continuously adjust the model parameters through the optimization algorithm. The purpose of model training is to estimate the regression coefficient β so that the error between the predicted value and the true value is minimized; when initializing the model parameters, assign an initial value to the parameter β corresponding to each feature x in the linear regression model, and also assign an initial value to the intercept term α; use the data in the training set and continuously iterate and update the parameters of the linear regression model through the gradient descent algorithm to minimize the loss function value between the true value and the predicted value. Use the validation set to validate the trained model and adjust the model parameters or structure based on the validation results; use the test set to comprehensively evaluate the optimized model using the set evaluation indicators to determine whether the model performance meets the expected standards; Step 6: Save the trained and verified parameter prediction model for subsequent use in real-time prediction and optimization of the communication protocol parameters of the wireless module in the communication adaptive module.

5. A wireless control system for an intelligent vehicle according to claim 4, characterized in that: In step 2, the Z-score standardization method is used to subtract the mean of each numerical data and divide it by its standard deviation, so that the processed data conforms to the standard normal distribution. The formula for standardization is: Among them, X is the original data, μ is the mean of the original data, σ is the standard deviation of the original data, and Z is the standardized data.

6. A wireless control system for an intelligent vehicle according to claim 5, characterized in that: In step 5, the mean square error (MSE) algorithm is used to evaluate the output error of the parameter prediction model. The specific algorithm is: Where n is the number of data points, Y pred,i is the communication protocol parameter predicted by the model, Y actual,i is the actual communication protocol parameter; the prediction accuracy of the model is evaluated according to the MSE value. The smaller the MSE value, the higher the prediction accuracy of the model.

7. A wireless control system for an intelligent vehicle according to claim 1, characterized in that: The data and processing unit uses a linear interpolation algorithm to fill in missing values; and uses a threshold judgment method to fill in abnormal values, treating data points that exceed a preset threshold range as abnormal values ​​and replacing them with the mean of their neighboring points.

8. The wireless control system for a smart car according to claim 1, characterized in that: The ultrasonic module uses the ultrasonic sensor HC_SR04 module for distance measurement. The specific method is as follows: Use the Trig pin of the HC_SR04 module to receive a high-level pulse signal of more than 10 microseconds to trigger the module to emit ultrasonic pulses; After receiving the trigger signal, the HC_SR04 module transmits 8 40 Hz ultrasonic pulses; After the ultrasonic wave is transmitted, the HC_SR04 module receives the echo signal, and after receiving the signal, the Echo pin of the HC_SR04 module is set to a high level state to indicate that the echo signal has been received; The Echo pin is used to generate a high-level pulse. The width of the pulse indicates the time required for ultrasonic wave to be transmitted and echo to be received. By detecting the high-level pulse width of the Echo pin, the ultrasonic propagation time is calculated, and then the distance between the target and the ultrasonic module is calculated based on the sound speed and time.

9. A wireless control system for an intelligent car according to claim 8, characterized in that: The system also includes a lamp bead module, which uses a LED light group containing multiple colors to display the running status of the smart car, and the lamp bead module is connected to the controller module through a serial communication method and a single data line; The lamp bead module receives the brightness and color data sent by the controller module; based on the received data, the brightness and color of the LED lamp group are controlled.

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