Large-capacity automatic tracking radio frequency power supply unmanned charging system and unattended operation method thereof

Abstract

The invention discloses a high-capacity automatic tracking radio frequency power supply unmanned charging system and an unattended operation method thereof, and the system comprises at least one transmitting module which is used for transmitting a dual-frequency pulse signal containing position information; the at least one receiving module is used for receiving the dual-frequency pulse signal and decoding the dual-frequency pulse signal; the server is used for receiving the position information of the receiving module and sending a starting instruction to the transmitting module; the transmitting module is provided with a plurality of independently controllable magnetic resonance transmitting coils, and the power output of each magnetic resonance transmitting coil is dynamically adjusted according to the position information of the receiving module, so that the simultaneous charging of multiple devices is realized without mutual interference. By adopting the high-efficiency radio-frequency power supply magnetic resonance coil design and the automatic tracking technology, large-capacity and high-precision unattended charging can be realized, the charging efficiency and the user experience are remarkably improved, the system can adapt to the charging requirements in various complex environments due to the automatic positioning function, and the system is suitable for popularization and application. The method is suitable for smart home, industrial automation, electric vehicle charging stations and other scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless charging, and in particular to an unmanned charging system for a large-capacity automatic tracking radio frequency power supply and an unmanned charging method thereof. Background Art

[0002] With the popularity of mobile devices and electric vehicles, the demand for efficient and flexible charging solutions is growing. Traditional wired charging methods have many limitations, such as low charging convenience, inconsistent charging interfaces, and easy wear. Wireless charging technology has gradually gained attention due to its advantages such as no need for physical connection and ease of use. However, existing wireless charging technologies mostly use electromagnetic induction, which has problems such as low charging efficiency, short charging distance, and high position accuracy requirements. In addition, most wireless charging systems cannot charge multiple devices at the same time and have poor adaptability to dynamic charging scenarios. Summary of the invention

[0003] In view of the problems existing in the prior art, the present invention provides a large-capacity automatic tracking radio frequency power unmanned charging system and an unmanned charging method thereof.

[0004] In order to achieve the above object, the present invention provides a large-capacity automatic tracking radio frequency power supply unmanned charging system, comprising:

[0005] At least one transmitting module, used to send a dual-frequency pulse signal containing position information and a power flow output;

[0006] At least one receiving module, used to receive and decode the dual-frequency pulse signal and receive electrical power;

[0007] The server is used to receive the location information of the receiving module and send a start instruction to the transmitting module;

[0008] The transmitting module is equipped with a plurality of independently controllable magnetic resonance transmitting coils, and dynamically adjusts the power output of each magnetic resonance transmitting coil according to the position information of the receiving module, thereby realizing charging of multiple devices at the same time without interfering with each other.

[0009] Preferably, the transmitting module further includes:

[0010] Radio frequency power supply, used to generate high-frequency electromagnetic wave signals;

[0011] A dual-frequency pulse signal generator, used for generating a dual-frequency pulse signal containing position information;

[0012] High-precision positioning and tracking module 1, used to determine the real-time position of the receiving module and perform dynamic tracking;

[0013] A power control unit dynamically adjusts the power output of the magnetic resonance transmitting coil according to the position information of the receiving module;

[0014] The signal generated by the dual-frequency pulse signal generator is sent to the receiving module through the magnetic resonance transmitting coil;

[0015] The high-precision positioning and tracking module 1 receives the positioning signal from the receiving module and transmits the position information to the power control unit;

[0016] The power control unit adjusts the power output of the magnetic resonance transmitting coil according to the received position information to optimize the energy transmission efficiency;

[0017] The magnetic resonance transmitting coil is connected to a radio frequency power supply to form an energy transmitting loop to transmit high-frequency electromagnetic wave signals.

[0018] Preferably, the transmitting module further includes:

[0019] A receiving coil, used for receiving high-frequency electromagnetic wave signals emitted by the magnetic resonance transmitting coil;

[0020] A rectifier and filter circuit is used to convert the received high-frequency electromagnetic waves into direct current;

[0021] A battery power detection unit, used to detect the battery power of the connected device;

[0022] The high-precision positioning and tracking module 2 cooperates with the high-precision positioning and tracking module 1 of the transmitting module to receive the positioning signal from the high-precision positioning and tracking module 1 and feed back the position information;

[0023] PWM controller to adjust output current and voltage to suit battery charging requirements;

[0024] The high-frequency electromagnetic wave signal received by the receiving coil is converted into direct current by the rectifying and filtering circuit and supplied to the battery and the battery power detection unit;

[0025] The battery power detection unit sends the battery power information to the server;

[0026] The high-precision positioning and tracking module 2 receives the positioning signal from the high-precision positioning and tracking module 1, and feeds back the position information to the server;

[0027] The server sends a control instruction to the PWM controller according to the received location information and battery power information;

[0028] The PWM controller adjusts the output current and voltage according to the control instruction to achieve constant current and constant voltage charging.

[0029] Preferably, the server comprises:

[0030] Remote monitoring interface, allowing users to query charging status and adjust charging parameters;

[0031] A data analysis engine module is used to analyze the status information of the transmitting module and the receiving module;

[0032] The control instruction generator is used to generate control instructions and send them to the transmitting module and the receiving module.

[0033] The remote monitoring interface is connected to the data analysis engine module to provide real-time data display;

[0034] The data analysis engine receives and processes the status information from the transmitting module and the receiving module; the control instruction generator generates a control instruction according to the data analysis result and sends it to the transmitting module and the receiving module through the communication network.

[0035] Preferably, the large-capacity automatic tracking RF power unmanned charging system also includes an artificial intelligence algorithm, which analyzes historical charging data and predicts device power characteristics through the artificial intelligence algorithm, and intelligently adjusts the charging parameters of the transmitting module to optimize energy transmission efficiency.

[0036] Preferably, the high-precision positioning and tracking module 1 and the high-precision positioning and tracking module 2 both include: a sensor array and a signal processing unit;

[0037] The sensor array includes an infrared sensor, an ultrasonic sensor or a camera sensor, which is used to monitor the position change of the receiving module in real time;

[0038] The signal processing unit is used to process the signals collected by the sensor array and extract precise position information.

[0039] The present invention also provides a large-capacity automatic tracking magnetic resonance unattended charging method, comprising the following steps:

[0040] Step a: Start the transmitting module, perform system initialization and timing to prepare for the wireless charging process;

[0041] Step b: Execute service routing synchronization to ensure the communication connection between the transmitting module and the server;

[0042] Step c: The transmitting module and the receiving module are powered on and initialized, and the local ID is sent to the server for identity authentication; at the same time, the transmitting module periodically sends a dual-frequency signal containing the local ID;

[0043] Step d: The receiving module reads the dual-frequency signal and transmits the ID of the transmitting module to the server to identify the transmitting module;

[0044] Step e: The receiving module detects the battery power, and if the power is lower than a preset threshold, sends a request to start charging to the transmitting module;

[0045] Step f: After receiving the request to start charging, the transmitting module sends an ID signal to the receiving module through FSK modulation to confirm that the charging process is started;

[0046] Step g: the transmitting module enters the soft start process and adjusts the PWM frequency to control the current and voltage of the magnetic resonance transmitting coil to achieve safe energy transmission;

[0047] Step h: using a built-in sensor array to perform real-time position monitoring on the transmitting module and the receiving module, and automatically adjusting the relative position between the magnetic resonance transmitting coil and the receiving coil to maintain an optimal energy transmission path;

[0048] Step i: Apply artificial intelligence algorithms to analyze historical charging data, predict the power characteristics of mobile devices, and intelligently adjust charging parameters based on the prediction results to optimize energy transfer efficiency;

[0049] Step j: During the charging process, the current and voltage are monitored in real time. If the current is detected to be greater than or less than 3A or the voltage exceeds 54V, the frequency and duty cycle are automatically adjusted to ensure the safety and stability of the charging process;

[0050] Step k: After completing the charging parameter adjustment, the transmitting module sends the charging status information to the server to achieve remote monitoring and management.

[0051] Preferably, adjusting the PWM frequency in step g includes automatically adjusting the frequency according to the change in the current to maintain the current within a safe range;

[0052] The automatic adjustment process in step h includes:

[0053] Step h101: monitoring the precise position of the receiving module by using an infrared sensor, an ultrasonic sensor or a camera sensor of a sensor array;

[0054] Step h102: dynamically adjusting the power output of the magnetic resonance transmitting coil according to the position information to adapt to the movement of the receiving module.

[0055] Preferably, the artificial intelligence algorithm in step i uses a machine learning model to analyze historical charging data;

[0056] The historical charging data includes charging efficiency, device location and motion trajectory information; and the charging parameters include power output and frequency.

[0057] Preferably, in step k, the charging status information includes current current, voltage and charging status;

[0058] During the charging process, the transmitting module and the receiving module automatically adapt to different loads to realize the charging of different devices such as AGV, mobile phones, and electric vehicles.

[0059] The technical solution of the present invention has the following beneficial effects:

[0060] The present invention is designed based on the principle of high-efficiency radio frequency power supply transmission. By adopting the radio frequency power supply principle to drive the transmission circuit and the dedicated SIC high-frequency power device, the energy transmission efficiency is significantly improved, and large-capacity wireless charging is achieved.

[0061] The present invention adopts a high-efficiency magnetic resonance coil design and adopts a radio frequency transmitting coil structure and materials to significantly improve energy transmission efficiency and achieve large-capacity wireless charging.

[0062] The present invention introduces automatic tracking technology, uses built-in sensors (such as infrared, ultrasonic or camera) to monitor the position changes of the mobile device in real time, and automatically adjusts the relative position between the transmitting coil and the receiving coil to ensure the best energy transmission path.

[0063] The present invention combines artificial intelligence algorithms to learn historical charging data, predict the power characteristics of mobile devices, adjust charging parameters in advance, and further improve charging efficiency and stability.

[0064] The present invention adopts high-efficiency RF power supply magnetic resonance coil design and automatic tracking technology to achieve large-capacity and high-precision wireless charging, significantly improving charging efficiency and user experience. The automatic positioning function enables the system to adapt to charging needs in various complex environments, and is particularly suitable for scenarios such as smart homes, industrial automation, and electric vehicle charging stations.

[0065] The present invention adopts intelligent charging management and remote monitoring, so that users can grasp the charging status anytime and anywhere, improving the practicality and convenience of the system.

[0066] By adopting magnetic resonance technology, the present invention significantly extends the charging distance between the device and the charger and improves the charging flexibility; the system optimizes the energy transmission process, reduces losses, and improves the charging efficiency, and can maintain high efficiency even when multiple devices are charged at the same time.

[0067] The present invention allows the device to be charged in a larger area without the need for precise alignment, making it more convenient for users to use.

[0068] The present invention supports charging of multiple devices: the system can intelligently identify and adapt to the charging needs of multiple devices, and realize charging of multiple devices at the same time without interfering with each other; the application of high-precision positioning and tracking modules and artificial intelligence algorithms enables the system to adapt to the charging needs of mobile devices and maintain the continuity and stability of the charging process; the system uses artificial intelligence algorithms to analyze historical charging data, predict device power characteristics, and automatically adjust charging parameters to achieve intelligent management.

[0069] The present invention has remote monitoring and control: the user can view the charging status in real time through the remote monitoring interface, and perform remote management and control, thereby improving the manageability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 This is a control module block diagram of the present invention;

[0071] Figure 2 It is a schematic diagram of the magnetic resonance transmitting coil and receiving coil of the present invention;

[0072] Figure 3 This is a schematic diagram of a double-layer structure of the magnetic resonance transmitting coil T of the present invention;

[0073] Figure 4 This is a schematic diagram of the working process of the transmitting module of the present invention;

[0074] Figure 5 This is a schematic diagram of the working process of the receiving module of the present invention;

[0075] Figure 6 This is a schematic diagram of the main circuit of the wireless charging staggered drive in the transmitting module of the present invention;

[0076] Figure 7 The schematic diagram of the rectifier and filter circuit in the receiving module of the present invention;

[0077] Figure 8 This is the principle diagram of the precision constant current and constant voltage circuit of the present invention;

[0078] Fig. 9 This is a schematic diagram of the dual-frequency positioning in the automatic tracking magnetic resonance wireless charging system of the present invention. DETAILED DESCRIPTION

[0079] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0080] Reference Figures 1 to 9 The present invention provides a large-capacity automatic tracking radio frequency power supply unmanned charging system, comprising:

[0081] At least one transmitting module 100 is used to send a dual-frequency pulse signal containing position information and a power flow output; wherein the high-efficiency magnetic resonance transmitting coil T adopts a special double-layer structure (refer to Figure 3 ), special customized materials and design dimensions, greatly improve the output efficiency; by optimizing the coil structure and materials, significantly improve the energy transmission efficiency and achieve large-capacity wireless charging;

[0082] At least one receiving module 200 is used to receive the dual-frequency pulse signal and decode and receive electric power to determine its precise location; Receiving and decoding of the receiving module 200: Signal reception: The receiving module 200 is designed with a specific receiving coil 201, which is used to receive the dual-frequency pulse signal sent by the transmitting module 100. Signal decoding: The decoder built into the receiving module 200 parses the location information in the dual-frequency pulse signal and sends this information to the server 300 for processing. Charging request: When the receiving module detects that the battery power is lower than the preset threshold, it will automatically send a charging request to the server, triggering the transmitting module to start the charging process.

[0083] The server 300 is used to receive the location information of the receiving module and send a start instruction to the transmitting module;

[0084] The transmitting module 100 is equipped with a plurality of independently controllable magnetic resonance transmitting coils T, allowing the system to charge multiple devices at the same time. The power output of each magnetic resonance transmitting coil is dynamically adjusted according to the position information of the receiving module, thereby realizing the simultaneous charging of multiple devices without interfering with each other.

[0085] Role and function of server 300: Position information processing: server 300 receives position information from receiving module 200 and processes it to determine the precise position of receiving module 200; Start command sending: once a charging request is received, server 300 will send a start command to transmitting module 100 to activate the charging process. Remote monitoring and control: server 300 provides a remote monitoring interface, allowing the operator to monitor the charging status in real time and adjust the charging parameters as needed.

[0086] Implementation of multi-device charging Device identification: The system can identify multiple receiving modules 200 and allocate independent charging resources to each module. No interference: By dynamically adjusting the power output of the magnetic resonance transmitting coil, the system ensures that each device can obtain the required energy while avoiding mutual interference. The server 300 intelligently schedules the work of the transmitting module 100 according to the power demand and location information of the receiving module 200 to optimize the overall charging efficiency.

[0087] Furthermore, the transmitting module 100 further includes:

[0088] High-efficiency RF power supplies are used to generate high-frequency electromagnetic wave signals. High-efficiency RF power supplies use advanced electronic components and optimized circuit design to stably generate high-frequency electromagnetic wave signals and provide the required energy for the entire system. RF power supplies have overheat protection and voltage stabilization functions to ensure that high-quality electromagnetic wave signals can be continuously output under various load conditions.

[0089] A dual-frequency pulse signal generator is used to generate a dual-frequency pulse signal containing position information; the dual-frequency pulse signal generator is responsible for generating a pulse signal containing specific position information encoding; the position information is embedded in the pulse signal through a specific encoding method, so that the receiving module can decode and determine its own position relative to the transmitting module 100.

[0090] High-precision positioning and tracking module 1 is used to determine the real-time position of the receiving module and perform dynamic tracking; high-precision positioning and tracking module 1 uses a variety of sensor technologies (such as infrared, ultrasonic or camera) to monitor the real-time position of the receiving module. Dynamic tracking: The module has dynamic tracking capabilities and can adapt to the movement of the receiving module to ensure real-time update and accuracy of the location information.

[0091] The microcontroller MCU is used to process and decode the signals from the dual-frequency pulse signal generator and the high-precision positioning and tracking module. The microcontroller MCU receives the signals from the dual-frequency pulse signal generator and the high-precision positioning and tracking module and performs decoding processing. According to the decoded position information and system status, the MCU controls the power control unit through a preset algorithm to achieve precise adjustment of the power of the magnetic resonance transmitting coil.

[0092] The power control unit dynamically adjusts the power output of the magnetic resonance transmitting coil T according to the position information of the receiving module 100; by adjusting the power output in real time, the system can reduce energy loss and electromagnetic interference while maintaining efficient energy transmission.

[0093] The corresponding end of the microcontroller MCU is electrically connected to the corresponding end of the radio frequency power supply, the dual-frequency pulse signal generator, the high-precision positioning tracking module 1, and the power control unit respectively;

[0094] The signal generated by the dual-frequency pulse signal generator is sent to the receiving module 200 through the magnetic resonance transmitting coil;

[0095] The high-precision positioning tracking module receives the positioning signal from the receiving module and transmits the position information to the power control unit; the power control unit adjusts the power output of the magnetic resonance transmitting coil according to the received position information to optimize the energy transmission efficiency; the magnetic resonance transmitting coil is connected to the radio frequency power supply to form an energy transmission loop to transmit high-frequency electromagnetic wave signals.

[0096] Furthermore, the transmitting module also includes:

[0097] The receiving coil R is used to receive the high-frequency electromagnetic wave signal emitted by the magnetic resonance transmitting coil T. The receiving coil T is designed to efficiently receive the high-frequency electromagnetic wave signal emitted by the magnetic resonance transmitting coil R. It matches the resonance frequency of the magnetic resonance transmitting coil R to achieve maximum energy receiving efficiency.

[0098] A rectifier and filter circuit is used to convert the received high-frequency electromagnetic waves into direct current;

[0099] The battery power detection unit is used to detect the battery power of the connected device; the battery power detection unit monitors the battery power of the connected device in real time to ensure that the charging process is both safe and efficient. The detected power information is sent to the server 300 to provide necessary data support for intelligent charging control.

[0100] The high-precision positioning and tracking module 2 cooperates with the high-precision positioning and tracking module 1 of the transmitting module 100 to receive the positioning signal from the high-precision positioning and tracking module 1 and feedback the position information; the high-precision positioning and tracking module 2 receives the positioning signal from the high-precision positioning and tracking module 1, and feeds back the position information of the receiving module to the server 300. Through the collaborative work of the two modules, the system can more accurately monitor the position change of the receiving module and adjust the power output of the transmitting module in real time.

[0101] The PWM controller is used to adjust the output current and voltage to meet the battery charging requirements. The PWM controller adjusts the output current and voltage according to the control instructions sent by the server 300 to meet the battery charging requirements of different devices. Constant current and constant voltage charging: The controller implements constant current and constant voltage charging to ensure that the battery is fast and safe during the charging process.

[0102] The high-frequency electromagnetic wave signal received by the receiving coil is converted into direct current by the rectifier and filter circuit, and supplied to the battery and the battery power detection unit; the battery power detection unit sends the battery power information to the server 300; the high-precision positioning and tracking module 2 receives the positioning signal from the high-precision positioning and tracking module 1, and feeds back the position information to the server 300; the server sends a control instruction to the PWM controller according to the received position information and battery power information; the server integrates a data analysis engine to analyze the position information and battery power information of the receiving module 100, and according to the analysis results, the server 300 generates a control instruction and sends it to the PWM controller through the communication network to realize intelligent charging management; the PWM controller adjusts the output current and voltage according to the control instruction to realize constant current and constant voltage charging.

[0103] Furthermore, the server includes:

[0104] The remote monitoring interface allows users to query the charging status and adjust the charging parameters; the remote monitoring interface provides an intuitive user interaction design, allowing users to easily query the charging status and adjust the charging parameters. The interface displays real-time data of the transmitting module 100 and the receiving module 200, including key indicators such as battery power, current, and voltage. Parameter adjustment function: Users can adjust charging parameters such as charging power, charging time, etc. as needed to meet the charging needs of different devices.

[0105] A data analysis engine module, used to analyze the status information of the transmitting module 100 and the receiving module 200;

[0106] The data analysis engine module collects status information from the transmitting module 100 and the receiving module 200, including data such as location, power, and signal strength. The data analysis engine module uses data analysis algorithms to process and analyze the collected data to identify patterns and trends in the charging process. Based on the analysis results, the data analysis engine can predict the movement trajectory and charging requirements of the device, providing decision support for the control instruction generator.

[0107] The control instruction generator is used to generate control instructions and send them to the transmitting module 100 and the receiving module 200. The control instruction generator automatically generates control instructions based on the output of the data analysis engine module to optimize the charging process. The control instruction generator can respond to changes in the system state in real time, quickly generate and update control instructions, and ensure the continuity and stability of the charging process. The control instruction generator is sent to the transmitting module 100 and the receiving module 200 through the communication network to achieve remote control and coordination.

[0108] The remote monitoring interface is connected to the data analysis engine module to provide real-time data display;

[0109] The data analysis engine receives and processes the status information from the transmitting module and the receiving module; the control instruction generator generates control instructions based on the data analysis results and sends them to the transmitting module and the receiving module through the communication network. The remote monitoring interface, the data analysis engine module and the control instruction generator are highly integrated on the server 300 to form a collaborative system; the server 300 manages the data flow from the transmitting module 100 and the receiving module 200 to ensure the accuracy and timeliness of the data. The server 300 improves the charging efficiency, reduces energy loss, and improves the reliability and stability of the system through continuous learning and optimization.

[0110] Furthermore, the large-capacity automatic tracking RF power unmanned charging system also includes an artificial intelligence algorithm, which analyzes historical charging data and predicts the power characteristics of the device through the artificial intelligence algorithm, and intelligently adjusts the charging parameters of the transmitting module to optimize the energy transmission efficiency. In this embodiment, the artificial intelligence algorithm collects data from various modules in the system, including historical charging data, device usage patterns, battery performance indicators, etc. Data preprocessing: The algorithm cleans, standardizes and extracts features from the collected data for further analysis and learning. Trajectory analysis: Analyze the historical motion data of the device through machine learning models such as hidden Markov models (HMM) or recurrent neural networks (RNN). Prediction model: Construct a prediction model to predict the possible position and motion trajectory of the device in the future based on the historical position and motion pattern of the device; parameter optimization: Based on the predicted motion trajectory and the current charging status, the artificial intelligence algorithm calculates the optimal charging parameters, such as power output, frequency and duty cycle.

[0111] The algorithm adjusts the charging parameters of the transmitter module in real time to adapt to changes in the device's location and dynamic changes in charging needs. The algorithm evaluates the system efficiency under different charging parameters and identifies the main sources of energy loss. Through continuous learning and optimization, the AI ​​algorithm can adapt to different usage scenarios and user behaviors and continuously improve the overall efficiency of the system. The system collects user feedback and preferences as an input for algorithm optimization. The AI ​​algorithm provides personalized charging strategies and services based on the user's specific needs and preferences. By analyzing the device's charging data, the AI ​​algorithm can identify abnormal patterns and predict potential failures and maintenance needs. The AI ​​algorithm is tightly integrated with the server's data analysis engine module, remote monitoring interface, and control instruction generator to form a collaborative intelligent system. The multi-faceted application of AI algorithms in wireless charging systems, from data collection, motion trajectory prediction, intelligent adjustment of charging parameters to efficiency optimization, user-customized services, and system monitoring and fault prediction, all reflect the important role of AI in improving system performance and user experience.

[0112] Furthermore, the high-precision positioning and tracking module 1 and the high-precision positioning and tracking module 2 both include: a sensor array and a signal processing unit;

[0113] The sensor array includes infrared sensors, ultrasonic sensors or camera sensors, which are used to monitor the position changes of the receiving module in real time; infrared sensors are used to monitor the infrared signals emitted by the receiving module to determine its position and moving direction; ultrasonic sensors send and receive sound wave signals, measure the round-trip time of the sound waves, and thus calculate the distance to the receiving module. The camera is used to capture images, and through image recognition and processing technology, the characteristics of the receiving module are identified and its position is determined.

[0114] The signal processing unit is used to process the signals collected by the sensor array and extract accurate location information. The signal processing unit is responsible for synchronizing the signals from different sensors to ensure the consistency and accuracy of the data. Through data fusion technology, it combines the data from different sensors to improve the accuracy and robustness of positioning. The signal processing unit has a noise filtering function to remove noise and interference in the sensor signal and extract pure location information.

[0115] The high-precision positioning and tracking module 1 and the high-precision positioning and tracking module 2 both include a tracking algorithm for analyzing position information, predicting the motion trajectory of the receiving module, and adjusting the positioning strategy of the transmitting unit in real time. The high-precision positioning and tracking module works in conjunction with the power control unit to achieve: dynamically adjusting the power output of the magnetic resonance transmitting coil according to the real-time position of the receiving module to adapt to the movement of the receiving module; ensuring the efficiency and stability during the energy transmission process, and achieving optimal energy coupling. The high-precision positioning and tracking module can: maintain high-precision positioning and tracking capabilities in a variety of environments, including but not limited to dynamically changing charging scenarios. The high-precision positioning and tracking module 1 and the high-precision positioning and tracking module 2 use image processing technology to extract the features of the receiving module from the image captured by the camera. The high-precision positioning and tracking module 1 and the high-precision positioning and tracking module 2 share the monitored position information to achieve more comprehensive positioning coverage. The two modules work together to improve the accuracy and reliability of the overall positioning system through algorithm optimization. The sensor array and signal processing unit design take into account different environmental conditions, such as light changes, temperature fluctuations, etc., to maintain stable performance. The system is designed with a fault-tolerant mechanism, and even if some sensors fail, they can be compensated by the data of other sensors to ensure the continuity of positioning.

[0116] The present invention also provides a large-capacity automatic tracking magnetic resonance unattended charging method, comprising the following steps:

[0117] Step a: Start the transmitting module, perform system initialization and timing to prepare for the wireless charging process; In step a, the transmitting module starts and performs system initialization, including hardware self-test, software configuration and timer setting, to prepare for the wireless charging process.

[0118] Step b: Execute service route synchronization to ensure the communication connection between the transmitting module 100 and the server 300; wherein executing service route synchronization ensures the stability of the communication connection between the transmitting module 100 and the server 300, providing guarantee for data transmission and instruction reception.

[0119] Step c: The transmitting module and the receiving module are powered on and initialized, and the local ID is sent to the server for identity authentication; at the same time, the transmitting module periodically sends a dual-frequency signal containing the local ID;

[0120] Step d: The receiving module reads the dual-frequency signal and transmits the ID of the transmitting module to the server to identify the transmitting module;

[0121] Step e: The receiving module 200 detects the battery power. If the power is lower than the preset threshold, a request to start charging is sent to the transmitting module 100; The receiving module 200 detects the battery power. If the power is lower than the preset threshold, a request to start charging is automatically sent to the transmitting module 100 to trigger the charging process.

[0122] Step f: After receiving the request to start charging, the transmitting module 100 sends an ID signal to the receiving module through FSK modulation to confirm that the charging process is started;

[0123] Step g: the transmitting module 100 enters a soft start process and adjusts the PWM frequency to control the current and voltage of the magnetic resonance transmitting coil to achieve safe energy transmission;

[0124] Step h: using the built-in sensor array to perform real-time position monitoring on the transmitting module 100 and the receiving module 200, and automatically adjusting the relative position between the magnetic resonance transmitting coil and the receiving coil to maintain the best energy transmission path;

[0125] Step i: Apply artificial intelligence algorithms to analyze historical charging data, predict the power characteristics of mobile devices, and intelligently adjust charging parameters based on the prediction results to optimize energy transfer efficiency;

[0126] Step j: During the charging process, the current and voltage are monitored in real time. If the current is detected to be greater than or less than 3A or the voltage exceeds 54V, the frequency and duty cycle are automatically adjusted to ensure the safety and stability of the charging process;

[0127] Step k: After completing the charging parameter adjustment, the transmitting module sends the charging status information to the server to achieve remote monitoring and management.

[0128] From the above method, it can be seen that the entire charging method has achieved a high degree of automation, from system initialization to remote monitoring, which reduces manual intervention and improves efficiency. Through the introduction of artificial intelligence algorithms, the system can predict and adapt to the movement of the equipment to achieve more flexible and efficient charging; real-time monitoring and automatic adjustment mechanisms ensure the safety of the charging process and prevent risks such as overcharging or overheating; the remote monitoring function of the server provides users with a convenient management method, which can understand the charging status in real time and make necessary adjustments.

[0129] Furthermore, the adjustment of the PWM frequency in step g includes automatically adjusting the frequency according to the change of the current to maintain the current within a safe range; automatic adjustment of the PWM frequency, current monitoring: the system continuously monitors the current through the magnetic resonance transmitting coil to ensure real-time monitoring of the charging process. Frequency adjustment mechanism: according to the monitored current, the system automatically adjusts the PWM (pulse width modulation) frequency. For example, if the current exceeds a preset safety threshold, the system will reduce the PWM frequency to reduce the current.

[0130] By dynamically adjusting the frequency, the system ensures that the current and voltage are always maintained within a safe range to prevent damage to the battery or charging equipment.

[0131] Furthermore, the automatic adjustment process in step h includes:

[0132] Step h101: Monitor the precise position of the receiving module through the infrared sensor, ultrasonic sensor or camera sensor of the sensor array; using the infrared, ultrasonic or camera sensors in the sensor array, the system monitors the precise position of the receiving module in real time, and each sensor provides complementary position information for different environmental conditions and usage scenarios.

[0133] Step h102: dynamically adjusting the power output of the magnetic resonance transmitting coil according to the position information to adapt to the movement of the receiving module.

[0134] Further, the artificial intelligence algorithm in step i analyzes historical charging data using a machine learning model;

[0135] Among them, historical charging data includes charging efficiency, device location and movement trajectory information, which provides input for the model. By training the model, it can identify patterns and trends in the charging process and predict the future charging needs and behaviors of the device. Based on the analysis results, the charging parameters such as power output and frequency are intelligently adjusted to optimize energy transmission efficiency and charging speed. The artificial intelligence algorithm can monitor the charging process in real time and make adaptive adjustments based on real-time data to ensure the optimization of the charging process.

[0136] Further, in step k, the charging status information includes the current current, voltage and charging status;

[0137] During the charging process, the transmitting module and the receiving module automatically adapt to different loads to realize the charging of different devices such as AGV, mobile phones, and electric vehicles. For example, by adjusting the charging protocol or using different charging strategies, in the scenario where multiple devices are charged at the same time, the system can intelligently allocate resources to ensure that each device can obtain suitable charging conditions. In the process of automatically adapting to different loads, the system always balances the safety and efficiency of charging to avoid overcharging or undercharging; users can customize charging parameters through the remote monitoring interface according to personal needs, and the artificial intelligence algorithm will optimize the charging process according to user customization; during the charging process, the system monitors and records the current current, voltage, and charging status in real time. Feedback the charging status information to the server, including but not limited to the battery charging percentage, estimated charging completion time, etc. Through the remote monitoring interface, users can view the charging status in real time and conduct remote management and control.

[0138] Reference Figure 6 This is a schematic diagram of the wireless charging staggered driving main circuit in the transmitting module 100 in an embodiment of the present invention; A and B respectively input 50--130kHz square wave signals, with a duty cycle of 50%, and a dead time, staggered driving MOS tube Q1 and MOS tube Q2, and then through the resonant circuit composed of capacitor C1 and magnetic resonance transmitting coil L1, a high-frequency electromagnetic wave signal is generated. Figure 7 The receiving end of the receiving module efficiently receives high-frequency electromagnetic wave signals through a resonant circuit composed of capacitor C2 and receiving coil L2 with the same frequency as the transmitting end, and then generates DC power through a rectifier filter circuit composed of diodes D1 D2 and capacitor C3 to drive other loads. Through the control and power adjustment of the main control MCU, it can automatically adapt to various loads, such as AGV, mobile phones, electric vehicles, etc., with strong flexibility; finally, there is a first-level precision constant current and constant voltage circuit (refer to Figure 8 ), by adjusting the duty cycle of the MOS tube Q3 drive, and then adjusting the output OUT+- current and voltage, and then through the loop control of the microcontroller MCU, accurate constant current and constant voltage are achieved, which is suitable for lithium batteries and other scenes that require precise constant current charging. Fig. 9 The working principle diagram of dual-frequency positioning for automatic tracking of magnetic resonance wireless charging: the transmitting part transmits dual-frequency pulses representing the location information, the receiving end receives and decodes, and sends it to the server, confirming that the receiving end is in place, and then starts transmitting and transmitting power. It can only be received when it is above the transmitting end, completely avoiding the problem of receiving and positioning interference.

[0139] It can be seen from the above embodiments that the present invention is designed based on the high-efficiency RF power transmission principle. By adopting the RF power principle to drive the transmission circuit and the dedicated SIC high-frequency power device, the energy transmission efficiency is significantly improved to achieve large-capacity wireless charging.

[0140] The present invention adopts a high-efficiency magnetic resonance coil design and adopts a radio frequency transmitting coil structure and materials to significantly improve energy transmission efficiency and achieve large-capacity wireless charging.

[0141] The present invention introduces automatic tracking technology, uses built-in sensors (such as infrared, ultrasonic or camera) to monitor the position changes of the mobile device in real time, and automatically adjusts the relative position between the transmitting coil and the receiving coil to ensure the best energy transmission path.

[0142] The present invention combines artificial intelligence algorithms to learn historical charging data, predict the power characteristics of mobile devices, adjust charging parameters in advance, and further improve charging efficiency and stability.

[0143] The present invention adopts high-efficiency RF power supply magnetic resonance coil design and automatic tracking technology to achieve large-capacity and high-precision wireless charging, significantly improving charging efficiency and user experience. The automatic positioning function enables the system to adapt to charging needs in various complex environments, and is particularly suitable for scenarios such as smart homes, industrial automation, and electric vehicle charging stations.

[0144] The present invention adopts intelligent charging management and remote monitoring, so that users can grasp the charging status anytime and anywhere, improving the practicality and convenience of the system.

[0145] By adopting magnetic resonance technology, the present invention significantly extends the charging distance between the device and the charger and improves the charging flexibility; the system optimizes the energy transmission process, reduces losses, and improves the charging efficiency, and can maintain high efficiency even when multiple devices are charged at the same time.

[0146] The present invention allows the device to be charged in a larger area without the need for precise alignment, making it more convenient for users to use.

[0147] The present invention supports charging of multiple devices: the system can intelligently identify and adapt to the charging needs of multiple devices, and realize charging of multiple devices at the same time without interfering with each other; the application of high-precision positioning and tracking modules and artificial intelligence algorithms enables the system to adapt to the charging needs of mobile devices and maintain the continuity and stability of the charging process; the system uses artificial intelligence algorithms to analyze historical charging data, predict device power characteristics, and automatically adjust charging parameters to achieve intelligent management.

[0148] The present invention has remote monitoring and control: the user can view the charging status in real time through the remote monitoring interface, and perform remote management and control, thereby improving the manageability of the system.

[0149] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A large-capacity automatic tracking radio frequency power unmanned charging system, characterized in that: include: At least one transmitting module, used to send a dual-frequency pulse signal containing position information and a power flow output; At least one receiving module, used to receive and decode the dual-frequency pulse signal and receive electrical power; The server is used to receive the location information of the receiving module and send a start instruction to the transmitting module; The transmitting module is equipped with a plurality of independently controllable magnetic resonance transmitting coils, and dynamically adjusts the power output of each magnetic resonance transmitting coil according to the position information of the receiving module, thereby realizing charging of multiple devices at the same time without interfering with each other.

2. The large-capacity automatic tracking radio frequency power unmanned charging system according to claim 1 is characterized in that: The transmitting module also includes: Radio frequency power supply, used to generate high-frequency electromagnetic wave signals; A dual-frequency pulse signal generator, used for generating a dual-frequency pulse signal containing position information; High-precision positioning and tracking module 1, used to determine the real-time position of the receiving module and perform dynamic tracking; A power control unit dynamically adjusts the power output of the magnetic resonance transmitting coil according to the position information of the receiving module; The signal generated by the dual-frequency pulse signal generator is sent to the receiving module through the magnetic resonance transmitting coil; The high-precision positioning and tracking module 1 receives the positioning signal from the receiving module and transmits the position information to the power control unit; The power control unit adjusts the power output of the magnetic resonance transmitting coil according to the received position information to optimize the energy transmission efficiency; The magnetic resonance transmitting coil is connected to a radio frequency power supply to form an energy transmitting loop to transmit high-frequency electromagnetic wave signals.

3. The large-capacity automatic tracking radio frequency power unmanned charging system according to claim 2 is characterized in that: The transmitting module also includes: A receiving coil, used for receiving high-frequency electromagnetic wave signals emitted by the magnetic resonance transmitting coil; A rectifier and filter circuit is used to convert the received high-frequency electromagnetic waves into direct current; A battery power detection unit, used to detect the battery power of the connected device; The high-precision positioning and tracking module 2 cooperates with the high-precision positioning and tracking module 1 of the transmitting module to receive the positioning signal from the high-precision positioning and tracking module 1 and feed back the position information; PWM controller to adjust output current and voltage to suit battery charging requirements; The high-frequency electromagnetic wave signal received by the receiving coil is converted into direct current by the rectifying and filtering circuit and supplied to the battery and the battery power detection unit; The battery power detection unit sends the battery power information to the server; The high-precision positioning and tracking module 2 receives the positioning signal from the high-precision positioning and tracking module 1, and feeds back the position information to the server; The server sends a control instruction to the PWM controller according to the received location information and battery power information; The PWM controller adjusts the output current and voltage according to the control instruction to achieve constant current and constant voltage charging.

4. The large-capacity automatic tracking radio frequency power unmanned charging system according to claim 3 is characterized in that: The server comprises: Remote monitoring interface, allowing users to query charging status and adjust charging parameters; A data analysis engine module is used to analyze the status information of the transmitting module and the receiving module; A control instruction generator, used for generating control instructions and sending them to the transmitting module and the receiving module; The remote monitoring interface is connected to the data analysis engine module to provide real-time data display; The data analysis engine receives and processes the status information from the transmitting module and the receiving module; the control instruction generator generates a control instruction according to the data analysis result and sends it to the transmitting module and the receiving module through the communication network.

5. The large-capacity automatic tracking radio frequency power unmanned charging system according to claim 1 is characterized in that: The large-capacity automatic tracking RF power unmanned charging system also includes an artificial intelligence algorithm, which analyzes historical charging data and predicts device power characteristics, and intelligently adjusts the charging parameters of the transmitting module to optimize energy transmission efficiency.

6. The large-capacity automatic tracking radio frequency power unmanned charging system according to claim 4 is characterized in that: The high-precision positioning and tracking module 1 and the high-precision positioning and tracking module 2 both include: a sensor array and a signal processing unit; The sensor array includes an infrared sensor, an ultrasonic sensor or a camera sensor, which is used to monitor the position change of the receiving module in real time; The signal processing unit is used to process the signals collected by the sensor array and extract precise position information.

7. A large-capacity automatic tracking magnetic resonance unattended charging method, characterized in that: The following steps are involved: Step a: Start the transmitter module, perform system initialization and timing to prepare for the charging process; Step b: Execute service routing synchronization to ensure the communication connection between the transmitting module and the server; Step c: The transmitting module and the receiving module are powered on and initialized, and the local ID is sent to the server for identity authentication; at the same time, the transmitting module periodically sends a dual-frequency signal containing the local ID; Step d: The receiving module reads the dual-frequency signal and transmits the ID of the transmitting module to the server to identify the transmitting module; Step e: The receiving module detects the battery power, and if the power is lower than a preset threshold, sends a request to start charging to the server-transmitting module; Step f: After receiving the request to start charging, the transmitting module enters the charging confirmation process; Step g: the transmitting module enters the soft start process and adjusts the PWM frequency to control the current and voltage of the magnetic resonance transmitting coil to achieve safe energy transmission; Step h: using a built-in sensor array to perform real-time position monitoring on the transmitting module and the receiving module, and automatically adjusting the relative position between the magnetic resonance transmitting coil and the receiving coil to maintain an optimal energy transmission path; Step i: Apply artificial intelligence algorithms to analyze historical charging data, predict the power characteristics of mobile devices, and intelligently adjust charging parameters based on the prediction results to optimize energy transfer efficiency; Step j: During the charging process, the current and voltage are monitored in real time. If the current is detected to be greater than or less than 3A or the voltage exceeds 54V, the frequency and duty cycle are automatically adjusted to ensure the safety and stability of the charging process; Step k: After completing the charging parameter adjustment, the transmitting module sends the charging status information to the server to achieve remote monitoring and management.

8. The large-capacity automatic tracking magnetic resonance unattended charging method according to claim 7 is characterized in that: The adjusting of the PWM frequency in step g includes automatically adjusting the frequency according to the change of the current to maintain the current within a safe range; The automatic adjustment process in step h includes: Step h101: monitoring the precise position of the receiving module by using an infrared sensor, an ultrasonic sensor or a camera sensor of a sensor array; Step h102: dynamically adjusting the power output of the magnetic resonance transmitting coil according to the position information to adapt to the movement of the receiving module.

9. The large-capacity automatic tracking magnetic resonance unattended charging method according to claim 7 is characterized in that: The artificial intelligence algorithm in step i analyzes historical charging data using a machine learning model; The historical charging data includes charging efficiency, device location and motion trajectory information; and the charging parameters include power output and frequency.

10. The large-capacity automatic tracking magnetic resonance unattended charging method according to claim 7 is characterized in that: In step k, the charging status information includes the current current, voltage and charging status; During the charging process, the transmitting module and the receiving module automatically adapt to different loads to realize the charging of different devices such as AGV, mobile phones, and electric vehicles.

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