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

Abstract

The application discloses a large-capacity automatic tracking radio frequency power supply unmanned charging system and an unmanned method thereof, and comprises at least one transmitting module for transmitting a double-frequency pulse signal containing position information, at least one receiving module for receiving the double-frequency pulse signal and decoding, and a server for receiving the position information of the receiving module and sending a starting instruction to the transmitting module; the transmitting module is equipped 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 multiple devices can be charged simultaneously without interfering with each other. By adopting the high-efficiency radio frequency power supply magnetic resonance coil design and the automatic tracking technology, the application can realize large-capacity and high-precision unmanned charging, significantly improve the charging efficiency and user experience, and the automatic positioning function enables the system to adapt to the charging demand in various complex environments, and is suitable for scenes such as smart home, industrial automation and electric vehicle charging station.

Description

TECHNICAL FIELD

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

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

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

[0004] To achieve the above-mentioned purpose, the present application provides a large-capacity automatic tracking radio frequency power unmanned charging system, comprising:

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

[0006] At least one receiving module for receiving the dual-frequency pulse signal and decoding and receiving electric power;

[0007] A server for receiving the position information of the receiving module and sending a start instruction to the transmitting module;

[0008] The transmitting module is equipped 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 as to realize simultaneous charging of multiple devices without interference.

[0009] Preferably, the transmitting module further comprises:

[0010] A radio frequency power source for generating a high-frequency electromagnetic wave driving signal;

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

[0012] A high-precision positioning and tracking module one for determining the real-time position of the receiving module and performing dynamic tracking;

[0013] A power control unit for dynamically adjusting 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 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 the radio frequency power supply to form an energy emission loop and emit high-frequency electromagnetic wave signals.

[0018] Preferably, the transmitting module further comprises:

[0019] The receiving coil is used to receive the high-frequency electromagnetic wave signals emitted by the magnetic resonance transmitting coil;

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

[0021] The battery power detection unit is used to detect the battery power of the connected device;

[0022] The high-precision positioning and tracking module two cooperates with the high-precision positioning and tracking module one of the transmitting module to receive the positioning signal from the high-precision positioning and tracking module one and feedback the position information;

[0023] The PWM controller is used to adjust the output current and voltage to meet the battery charging requirements;

[0024] The high-frequency electromagnetic wave signals received by the receiving coil are converted into direct current by the rectifier filter 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 two receives the positioning signal from the high-precision positioning and tracking module one and feeds back the position information to the server;

[0027] The server sends control instructions to the PWM controller according to the received position information and battery power information;

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

[0029] Preferably, the server comprises:

[0030] The remote monitoring interface allows users to query the charging status and adjust the charging parameters;

[0031] a data analysis engine module for analyzing the state information of the transmitting module and the receiving module;

[0032] a control instruction generator for generating control instructions and sending 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 state information from the transmitting module and the receiving module and processes it; 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 a communication network.

[0035] Preferably, the large-capacity automatic tracking radio frequency power supply unmanned charging system further comprises 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 one and the high-precision positioning and tracking module two each comprise a sensor array and a signal processing unit.

[0037] The sensor array comprises infrared sensors, ultrasonic sensors or camera sensors for real-time monitoring of the position changes of the receiving module.

[0038] The signal processing unit processes the signals collected by the sensor array to extract accurate position information.

[0039] The application also provides a large-capacity automatic tracking magnetic resonance unmanned 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; Step b: perform service route synchronization to ensure communication connection between the transmitting module and the server.

[0041] Step c: power on and initialize the transmitting module and the receiving module, send the local ID to the server for identity verification; at the same time, the transmitting module sends a dual-frequency signal containing the local ID at regular intervals.

[0042] 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.

[0043] Step e: the receiving module detects the battery power, and if the power is lower than a preset threshold, sends a start charging request to the transmitting module.

[0044] Step f: After receiving the start charging request, the transmitting module sends an ID signal to the receiving module through FSK modulation, confirming the start of the charging process.

[0045] Step g: The transmitting module enters the soft start process, adjusts the PWM frequency to control the current and voltage of the magnetic resonance transmitting coil, to achieve safe energy transmission.

[0046] Step h: Real-time position monitoring is performed on the transmitting module and receiving module using the built-in sensor array, automatically adjusting the relative position between the magnetic resonance transmitting coil and the receiving coil to maintain the optimal energy transmission path.

[0047] Step i: Apply artificial intelligence algorithm 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 transmission efficiency.

[0048] Step j: During the charging process, real-time monitoring of current and voltage is performed, and if the current is 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.

[0049] Step k: After completing the charging parameter adjustment, the transmitting module sends charging status information to the server to realize remote monitoring and management.

[0050] Preferably, the adjustment of PWM frequency in step g includes automatically adjusting the frequency according to the current size change to maintain the current within a safe range.

[0051] The automatic adjustment process in step h includes:

[0052] Step h101: Monitor the precise position of the receiving module through infrared sensors, ultrasonic sensors or camera sensors of the sensor array.

[0053] Step h102: According to the position information, dynamically adjust the power output of the magnetic resonance transmitting coil to adapt to the movement of the receiving module.

[0054] Preferably, the artificial intelligence algorithm in step i uses a machine learning model to analyze historical charging data.

[0055] Wherein, the historical charging data includes charging efficiency, device position and motion trajectory information; the charging parameters include power output and frequency.

[0056] Preferably, the charging status information in step k includes current, voltage and charging status.

[0057] During the charging process, the transmitting module and receiving module automatically adapt to different loads to achieve charging for different devices such as AGV, mobile phones and electric vehicles.

[0058] The technical scheme of the present application has the following beneficial effects:

[0059] The present application adopts high-efficiency radio frequency power transmission principle design, uses radio frequency power principle to drive the transmission circuit and special SIC high-frequency power device, significantly improves the energy transmission efficiency, and realizes large-capacity wireless charging.

[0060] The present application adopts high-efficiency magnetic resonance coil design, uses radio frequency transmission coil structure and material, significantly improves the energy transmission efficiency, and realizes large-capacity wireless charging.

[0061] The present application introduces automatic tracking technology, uses built-in sensors (such as infrared, ultrasonic or camera) to monitor the position change of mobile devices in real time, automatically adjusts the relative position between the transmission coil and the receiving coil, and ensures the best energy transmission path.

[0062] The present application combines artificial intelligence algorithm, learns historical charging data, predicts the power characteristics of mobile devices, adjusts charging parameters in advance, and further improves charging efficiency and stability.

[0063] The present application adopts high-efficiency radio frequency power magnetic resonance coil design and automatic tracking technology, can realize large-capacity and high-precision wireless charging, significantly improves the charging efficiency and user experience, and the automatic positioning function makes the system adapt to the charging demand in various complex environments, especially suitable for smart home, industrial automation and electric vehicle charging station and other scenes.

[0064] The present application adopts intelligent charging management and remote monitoring, so that users can master the charging state at any time and anywhere, and improves the practicability and convenience of the system.

[0065] The present application uses magnetic resonance technology, significantly expands the charging distance between the device and the charger, improves the flexibility of charging, optimizes the energy transmission process, reduces the loss, and improves the charging efficiency, even when multiple devices are charging at the same time.

[0066] The present application allows the device to charge in a larger area without precise alignment, making the user use more convenient.

[0067] The present application supports multi-device charging: the system can intelligently identify and adapt to the charging needs of multiple devices, realize simultaneous charging of multiple devices without interference; the application of high-precision positioning tracking module and artificial intelligence algorithm makes the system adapt to the charging needs of mobile devices, maintains the continuity and stability of the charging process; the system analyzes historical charging data using artificial intelligence algorithm, predicts device power characteristics, and automatically adjusts charging parameters to realize intelligent management.

[0068] The application has remote monitoring and control: the user can view the charging state in real time through the remote monitoring interface, and remotely manage and control, improving the manageability of the system. BRIEF DESCRIPTION OF DRAWINGS

[0069] Figure 1 The control module block diagram of the application is shown in the figure;

[0070] Figure 2 The schematic diagram of the magnetic resonance transmitting coil and receiving coil of the application is shown in the figure;

[0071] Figure 3 The schematic diagram of the double-layer structure of the magnetic resonance transmitting coil T of the application is shown in the figure;

[0072] Figure 4 The schematic diagram of the working process of the transmitting module of the application is shown in the figure;

[0073] Figure 5 The schematic diagram of the working process of the receiving module of the application is shown in the figure;

[0074] Figure 6 The schematic diagram of the wireless charging staggered drive main circuit in the transmitting module of the application is shown in the figure;

[0075] Figure 7 The schematic diagram of the rectification filter circuit in the receiving module of the application is shown in the figure;

[0076] Figure 8 The schematic diagram of the precise constant current and constant voltage circuit of the application is shown in the figure;

[0077] Figure 9 The schematic diagram of the double-frequency positioning in the automatic tracking magnetic resonance wireless charging system of the application is shown in the figure. DETAILED DESCRIPTION

[0078] The application is further described below in combination with the drawings and specific embodiments.

[0079] REFERENCE Figures 1 to 9 The application provides a large-capacity automatic tracking radio frequency power supply unmanned charging system, which comprises:

[0080] At least one transmitting module 100 is used for transmitting a double-frequency pulse signal containing position information and a power flow output; wherein the high-efficiency magnetic resonance transmitting coil T adopts a double-layer special structure (refer to Figure 3 ), special customized materials and design size, which greatly improves the output efficiency; by optimizing the coil structure and materials, the energy transmission efficiency is significantly improved, and large-capacity wireless charging is realized;

[0081] At least one receiving module 200, for receiving dual-frequency pulse signals and decoding and receiving electric power to determine its precise location; receiving and decoding of receiving module 200: signal receiving: receiving module 200 is designed with a specific receiving coil 201 for receiving dual-frequency pulse signals emitted by transmitting module 100. Signal decoding: the decoder built-in receiving module 200 parses the location information in the dual-frequency pulse signal and sends these information to 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.

[0082] Server 300, for receiving location information of receiving module and sending start instruction to transmitting module;

[0083] The transmitting module 100 is equipped with multiple independently controllable magnetic resonance transmitting coils T, allowing the system to charge multiple devices simultaneously, dynamically adjusting the power output of each magnetic resonance transmitting coil according to the location information of the receiving module, thus realizing multiple device charging without interference,

[0084] Role and function of server 300: location information processing: server 300 receives location information from receiving module 200 and processes it to determine the precise location of receiving module 200; start instruction sending: once receiving a charging request, server 300 will send a start instruction to transmitting module 100 to activate the charging process. Remote monitoring and control: server 300 provides a remote monitoring interface, allowing operators to monitor the charging status in real time and adjust the charging parameters as needed.

[0085] Implementation of multi-device charging device identification: the system can identify multiple receiving modules 200 and allocate independent charging resources for each module. Non-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. Server 300 intelligently schedules the work of transmitting module 100 according to the power demand and location information of receiving module 200, optimizing the overall charging efficiency.

[0086] Further, the transmitting module 100 further comprises:

[0087] High-efficiency radio frequency power supply for generating high-frequency driving electromagnetic wave signals; wherein the high-efficiency radio frequency power supply adopts advanced electronic components and optimized circuit design, which can stably generate high-frequency electromagnetic wave driving signals to provide the required energy for the entire system. The radio frequency power supply has overheat protection and voltage stabilization function, ensuring that it can continuously output high-quality electromagnetic wave driving signals under various load conditions.

[0088] The 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, allowing the receiving module to decode and determine its position relative to the transmitting module 100.

[0089] The high-precision positioning and tracking module one is used to determine the real-time position of the receiving module and perform dynamic tracking. The high-precision positioning and tracking module one utilizes various 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 capability, which can adapt to the movement of the receiving module, ensuring real-time updating and accuracy of position information.

[0090] The microcontroller MCU is used to process and decode signals from the dual-frequency pulse signal generator and the high-precision positioning and tracking module one. The microcontroller MCU receives signals from the dual-frequency pulse signal generator and the high-precision positioning and tracking module, and performs decoding processing. Based on the decoded position information and system state, the MCU controls the power control unit through a pre-set algorithm to achieve precise adjustment of the power of the magnetic resonance transmitting coil.

[0091] 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 maintain high energy transmission efficiency while reducing energy loss and electromagnetic interference.

[0092] The corresponding end of the microcontroller MCU is electrically connected to the radio frequency power supply, the dual-frequency pulse signal generator, the high-precision positioning and tracking module one, and the corresponding end of the power control unit.

[0093] The signal generated by the dual-frequency pulse signal generator is sent to the receiving module 200 through the magnetic resonance transmitting coil;

[0094] The high-precision positioning and tracking module one 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 energy transmission efficiency. The magnetic resonance transmitting coil is connected to the radio frequency power supply to form an energy emission loop, emitting high-frequency electromagnetic wave signals.

[0095] Further, the transmitting module further comprises:

[0096] The receiving coil R is used to receive the high-frequency electromagnetic wave signals emitted by the magnetic resonance transmitting coil T. The receiving coil T is designed to efficiently receive the high-frequency electromagnetic wave signals emitted by the magnetic resonance transmitting coil R. It matches the resonance frequency of the magnetic resonance transmitting coil R to achieve maximum energy reception efficiency.

[0097] A rectifier filter circuit is used to convert the received high-frequency electromagnetic waves into direct current.

[0098] A 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, ensuring that the charging process is both safe and efficient. The detected power information is sent to the server 300, providing necessary data support for intelligent charging control.

[0099] A high-precision positioning and tracking module two works with the high-precision positioning and tracking module one of the transmitting module 100, for receiving the positioning signal from the high-precision positioning and tracking module one and feeding back the position information; The high-precision positioning and tracking module two receives the positioning signal from the high-precision positioning and tracking module one, and feeds back the position information of the receiving module to the server 300. Through the cooperative 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.

[0100] A PWM controller is used to adjust the output current and voltage to adapt to the battery charging demand; The PWM controller adjusts the output current and voltage according to the control instructions sent by the server 300, to adapt to the battery charging demand of different devices. Constant current and constant voltage charging: the controller realizes constant current and constant voltage charging, ensuring that the battery is both fast and safe during charging.

[0101] The high-frequency electromagnetic wave signal received by the receiving coil is converted into direct current by the rectifier filter circuit, and is 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 two receives the positioning signal from the high-precision positioning and tracking module one, and feeds back the position information to the server 300; The server sends control instructions 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 result, the server 300 generates control instructions and sends them to the PWM controller through the communication network, realizing intelligent charging management; The PWM controller adjusts the output current and voltage according to the control instructions, to realize constant current and constant voltage charging.

[0102] Further, the server comprises:

[0103] A remote monitoring interface allows users to query the charging status and adjust the charging parameters; The remote monitoring interface provides intuitive user interaction design, allowing users to easily query the charging status and adjust the charging parameters.

[0104] The interface displays real-time data of the transmitting module 100 and the receiving module 200, including battery power, current, voltage, and other key indicators. Parameter adjustment function: users can adjust charging parameters such as charging power, charging time, etc. according to needs to adapt to the charging needs of different devices.

[0105] Data analysis engine module for analyzing state information of the transmitting module 100 and the receiving module 200;

[0106] The data analysis engine module collects state information from the transmitting module 100 and the receiving module 200, including location, power, and signal strength data. 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 motion trajectory and charging needs of the device, providing decision support for the control instruction generator.

[0107] Control instruction generator for generating control instructions and sending 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 system state in real time, quickly generate and update control instructions to ensure the continuity and stability of the charging process. The control instruction generator sends control instructions to the transmitting module 100 and the receiving module 200 through a communication network, achieving remote control and coordination.

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

[0109] The data analysis engine receives state information from the transmitting module and the receiving module and processes it. 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 a communication network. The remote monitoring interface, data analysis engine module, and control instruction generator are highly integrated on the server 300, forming a collaborative system. The server 300 manages the data flow from the transmitting module 100 and the receiving module 200, ensuring the accuracy and timeliness of the data. The server 300 continuously learns and optimizes to improve charging efficiency, reduce energy consumption, and improve the reliability and stability of the system.

[0110] Further, the large-capacity automatic tracking radio frequency power supply unmanned charging system also includes an artificial intelligence algorithm that analyzes historical charging data and predicts device power characteristics, intelligently adjusts the charging parameters of the transmission module to optimize 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 to facilitate further analysis and learning. Trajectory analysis: through machine learning models such as Hidden Markov Model (HMM) or Recurrent Neural Network (RNN), analyze the historical motion data of the device. Prediction model: build a prediction model to predict the possible location and motion trajectory of the device in the future based on its historical location and motion pattern; Parameter optimization: based on the predicted motion trajectory and the current charging state, 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 transmission module in real time to adapt to changes in device 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.

[0112] Through continuous learning and optimization, the artificial intelligence algorithm can adapt to different usage scenarios and user behaviors, continuously improving the overall efficiency of the system. The system collects user feedback and preferences as an input for algorithm optimization, and the artificial intelligence algorithm provides personalized charging strategies and services based on the specific needs and preferences of users. By analyzing the charging data of the device, the artificial intelligence algorithm can identify abnormal patterns and predict potential failures and maintenance needs. The artificial intelligence algorithm is tightly integrated with the server's data analysis engine module, remote monitoring interface, and control instruction generator, forming a collaborative intelligent system.

[0113] The artificial intelligence algorithm has multiple applications in the wireless charging system, from data collection, motion trajectory prediction, intelligent adjustment of charging parameters to efficiency optimization, user customization services, and system monitoring and fault prediction, all of which demonstrate the important role of artificial intelligence in improving system performance and user experience.

[0114] Further, the high-precision positioning and tracking module one and the high-precision positioning and tracking module two each include: a sensor array, a signal processing unit;

[0115] The sensor array includes infrared sensors, ultrasonic sensors, or camera sensors for real-time monitoring of the position changes of the receiving module; the infrared sensors are used to monitor the infrared signals emitted by the receiving module to determine its position and movement direction; the ultrasonic sensors send and receive sound wave signals, measure the round-trip time of the sound waves, and thus calculate the distance of the receiving module. The camera captures images, and through image recognition and processing technology, the characteristics of the receiving module are identified and its position is determined.

[0116] The signal processing unit is used to process the signals collected by the sensor array and extract accurate position information.

[0117] The signal processing unit is responsible for synchronizing signals from different sensors to ensure data consistency and accuracy. Through data fusion technology, the data from different sensors are combined 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 signals and extract pure position information.

[0118] The high-precision positioning and tracking module one and the high-precision positioning and tracking module two also include tracking algorithms 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 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 of the energy transmission process to achieve optimal energy coupling. The high-precision positioning and tracking module can maintain high-precision positioning and tracking capability in various environments, including but not limited to dynamic charging scenarios. The high-precision positioning and tracking module one and the high-precision positioning and tracking module two extract the characteristics of the receiving module from the images captured by the camera through image processing technology. The high-precision positioning and tracking module one and the high-precision positioning and tracking module two 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 are designed to consider different environmental conditions such as light changes and temperature fluctuations to maintain stable performance. The system design has a fault-tolerant mechanism, so even if some sensors fail, the data from other sensors can be compensated to ensure continuous positioning.

[0119] The application also provides a large-capacity automatic tracking magnetic resonance unattended charging method, which includes the following steps:

[0120] Step a: start the transmitting module, perform system initialization and timing to prepare for the wireless charging process; in step a, the transmitting module is started and performs system initialization, including hardware self-checking, software configuration, and timer setting, to prepare for the wireless charging process.

[0121] Step b: Perform service route synchronization to ensure stable communication connection between the transmitting module 100 and the server 300; wherein performing service route synchronization ensures stable communication connection between the transmitting module 100 and the server 300, providing guarantee for data transmission and instruction reception.

[0122] Step c: Power on and initialize the transmitting module and the receiving module, send the local ID to the server for identity verification; at the same time, the transmitting module sends a dual-frequency signal containing the local ID at regular intervals;

[0123] 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;

[0124] Step e: The receiving module 200 detects the battery power, if the power is lower than the preset threshold, sends a start charging request to the transmitting module 100; the receiving module 200 detects the battery power, if the power is lower than the preset threshold, automatically sends a start charging request to the transmitting module 100, triggering the charging process.

[0125] Step f: After the transmitting module 100 receives the start charging request, it sends an ID signal to the receiving module through FSK modulation to confirm the start of the charging process;

[0126] Step g: The transmitting module 100 enters the soft start process, adjusts the PWM frequency to control the current and voltage of the magnetic resonance transmitting coil, to realize safe energy transmission;

[0127] Step h: Real-time position monitoring is performed on the transmitting module 100 and the receiving module 200 using the built-in sensor array, automatically adjusting the relative position between the magnetic resonance transmitting coil and the receiving coil to maintain the optimal energy transmission path;

[0128] Step i: Apply artificial intelligence algorithm to analyze historical charging data, predict the power characteristics of mobile devices, and intelligently adjust charging parameters according to the prediction results to optimize energy transmission efficiency;

[0129] Step j: During the charging process, real-time monitoring of current and voltage is performed, if the detected current is 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;

[0130] Step k: After completing the charging parameter adjustment, the transmitting module sends charging status information to the server to realize remote monitoring and management.

[0131] From the above method, the entire charging method realizes high automation, from system initialization to remote monitoring, reduces manual intervention, improves efficiency, and through the introduction of artificial intelligence algorithms, the system can predict and adapt to the movement of the device, achieving more flexible and efficient charging; Real-time monitoring and automatic adjustment mechanism ensures the safety of the charging process, preventing overcharging or overheating and other risks; The remote monitoring function of the server provides a convenient management method for users, allowing them to monitor the charging status in real time and make necessary adjustments.

[0132] Further, the adjustment of the PWM frequency in step g includes automatically adjusting the frequency according to the current size change to maintain the current within a safe range; Automatic adjustment of PWM frequency, current monitoring: The system continuously monitors the current size through the magnetic resonance transmitting coil to ensure real-time monitoring of the charging process. Frequency adjustment mechanism: According to the monitored current size, the system automatically adjusts the PWM (Pulse Width Modulation) frequency. For example, if the current exceeds the preset safety threshold, the system will reduce the PWM frequency to reduce the current.

[0133] By dynamically adjusting the frequency, the system ensures that the current and voltage are always maintained within a safe range, preventing damage to the battery or charging equipment.

[0134] Further, the automatic adjustment process in step h includes:

[0135] 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, each sensor provides complementary position information for different environmental conditions and use scenarios.

[0136] Step h102: According to the position information, dynamically adjust the power output of the magnetic resonance transmitting coil to adapt to the movement of the receiving module.

[0137] Further, the artificial intelligence algorithm in step i uses a machine learning model to analyze historical charging data;

[0138] Wherein, the historical charging data includes charging efficiency, device position and motion trajectory information, providing input for the model, identifying patterns and trends in the charging process through training the model, and predicting future charging needs and behavior of the device; According to the analysis results, intelligently adjust charging parameters such as power output and frequency 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.

[0139] Further, the charging status information in step k includes the current current, voltage and charging status;

[0140] During the charging process, the transmitting module and the receiving module automatically adapt to different loads to achieve charging of different devices such as AGVs, mobile phones, and electric vehicles. For example, by adjusting the charging protocol or using different charging strategies, the system can intelligently allocate resources in a multi-device charging scenario to ensure that each device can obtain appropriate charging conditions. During the automatic adaptation to different loads, the system always balances the safety and efficiency of charging to avoid overcharging or undercharging; users can customize charging parameters through a remote monitoring interface according to their individual 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 state in real time. The charging state information is fed back to the server, including but not limited to the battery's charging percentage, estimated charging completion time, etc. Through the remote monitoring interface, users can view the charging status in real time and perform remote management and control.

[0141] Reference Figure 6 The schematic diagram of the wireless charging interleaved drive main circuit in the transmitting module 100 in the embodiment of the application; wherein A, B respectively input 50-130 kHz square wave signals with a duty cycle of 50%, there is a dead time, the interleaved drive mos tube Q1, mos tube Q2, and then through the resonance circuit composed of capacitor C1, magnetic resonance transmitting coil L1 to generate high-frequency electromagnetic wave signals. Reference Figure 7 The receiving end of the receiving module is through the resonance circuit composed of capacitor C2 and receiving coil L2 with the same frequency as the transmitting end to efficiently receive high-frequency electromagnetic wave signals, and then through the rectifier filter circuit composed of diodes D1 and D2 and capacitor C3 to generate direct current power, which in turn drives other loads, and through the control and power adjustment of the main control MCU, it can automatically adapt to various different loads such as AGVs, mobile phones, electric vehicles, etc., with strong flexibility; finally there is a first-stage precision constant-current constant-voltage circuit (reference Figure 8 ), by adjusting the duty cycle of mos tube Q3 drive, and then adjusting the current and voltage of output OUT+-, and then through the loop control of microcontroller MCU, precise constant current and constant voltage are realized, which is suitable for lithium battery and other scenarios that require precise constant current charging. Reference Figure 9 The working principle diagram of the automatic tracking magnetic resonance wireless charging double-frequency positioning: the transmitting part transmits double-frequency pulses representing position information, the receiving end receives and decodes, sends the server, determines that the receiving end has arrived, and then starts transmitting power transmission. Only above the transmitting end can it be received, completely avoiding the problem of receiving positioning interference.

[0142] As can be seen from the above embodiments, the application adopts high-efficiency radio frequency power transmission principle design, and by using radio frequency power principle to drive the transmitting circuit and special SIC high-frequency power devices, the energy transmission efficiency is significantly improved, and large-capacity wireless charging is realized.

[0143] The application adopts a high-efficiency magnetic resonance coil design, significantly improves the energy transmission efficiency by using a radio frequency transmitting coil structure and material, and realizes large-capacity wireless charging.

[0144] The application introduces an automatic tracking technology, uses built-in sensors (such as infrared, ultrasonic or camera) to monitor the position change of the mobile device in real time, automatically adjusts the relative position between the transmitting coil and the receiving coil, and ensures the best energy transmission path.

[0145] The application combines artificial intelligence algorithms, learns historical charging data, predicts the power characteristics of the mobile device, and adjusts the charging parameters in advance to further improve the charging efficiency and stability.

[0146] The application can realize large-capacity and high-precision wireless charging by using high-efficiency radio frequency power supply magnetic resonance coil design and automatic tracking technology, significantly improve the charging efficiency and user experience, and the automatic positioning function enables the system to adapt to the charging demand in various complex environments, especially suitable for smart home, industrial automation and electric vehicle charging station scenes.

[0147] The application uses intelligent charging management and remote monitoring, so that users can master the charging status at any time and anywhere, and improves the practicability and convenience of the system.

[0148] The application significantly expands the charging distance between the device and the charger by using the magnetic resonance technology, improves the flexibility of charging, optimizes the energy transmission process, reduces the loss, and improves the charging efficiency, even when multiple devices are charging at the same time.

[0149] The application allows the device to charge in a larger area without precise alignment, making it more convenient for users to use.

[0150] The application supports multi-device charging: the system can intelligently identify and adapt to the charging needs of multiple devices, realize simultaneous charging of multiple devices without interference; the application of high-precision positioning and tracking module and artificial intelligence algorithm enables the system to adapt to the charging needs of mobile devices, maintains the continuity and stability of the charging process; the system analyzes historical charging data using artificial intelligence algorithms, predicts device power characteristics, and automatically adjusts charging parameters to realize intelligent management.

[0151] The application has remote monitoring and control: users can view the charging status in real time through the remote monitoring interface, perform remote management and control, and improve the manageability of the system.

[0152] The above merely describes the preferred embodiments of the present application, and is not intended to limit the patent scope of the present application. Any equivalent structural changes made according to the content of the present application specification and drawings, or direct / indirect application in other related technical fields, are included in the patent protection scope of the present application.

Claims

1. A large-capacity automatic tracking radio frequency power source unmanned charging system, characterized by, The application relates to a multi-device charging system, comprising: at least one transmitting module for transmitting dual-frequency pulse signals containing position information and power flow output; at least one receiving module for receiving dual-frequency pulse signals and decoding and receiving electric power; a server for receiving position information of the receiving module and sending starting instructions to the transmitting module; the transmitting module is equipped with multiple independently controllable magnetic resonance transmitting coils, and power output of each magnetic resonance transmitting coil is dynamically adjusted according to position information of the receiving module, so that multiple devices can be simultaneously charged without interference; the transmitting module further comprises: a radio frequency power supply for generating a high-frequency electromagnetic wave driving signal; a dual-frequency pulse signal generator for generating dual-frequency pulse signals containing position information; a high-precision positioning and tracking module I for determining real-time positions of the receiving module and performing dynamic tracking; a power control unit for dynamically adjusting power output of the magnetic resonance transmitting coil according to position information of the receiving module; the signal generated by the dual-frequency pulse signal generator is transmitted to the receiving module through the magnetic resonance transmitting coil; the high-precision positioning and tracking module I receives a positioning signal from the receiving module and transmits position information to the power control unit; the power control unit adjusts power output of the magnetic resonance transmitting coil according to the received position information to optimize energy transmission efficiency; the magnetic resonance transmitting coil is connected with the radio frequency power supply to form an energy transmitting loop and emit a high-frequency electromagnetic wave signal; the transmitting module further comprises: a receiving coil for receiving the high-frequency electromagnetic wave signal emitted by the magnetic resonance transmitting coil; a rectifier and filter circuit for converting the received high-frequency electromagnetic wave into direct current; a battery power detection unit for detecting battery power of a connected device; a high-precision positioning and tracking module II for receiving a positioning signal from the high-precision positioning and tracking module I of the transmitting module and feeding back position information; a PWM controller for adjusting output current and voltage to adapt to battery charging requirements; 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 transmits battery power information to the server; the high-precision positioning and tracking module II receives the positioning signal from the high-precision positioning and tracking module I and feeds back position information to the server; the server sends control instructions to the PWM controller according to the received position information and battery power information; the PWM controller adjusts output current and voltage according to the control instructions to realize constant-current and constant-voltage charging.

2. The large capacity automatic tracking RF power supply unmanned charging system according to claim 1, characterized in that, the server comprises: a remote monitoring interface allowing a user to query charging states and adjust charging parameters; a data analysis engine module for analyzing state information of the transmitting module and the receiving module; a control instruction generator for generating control instructions and sending the control instructions to the transmitting module and the receiving module; the remote monitoring interface is connected with the data analysis engine module and provides real-time data display; The data analysis engine receives state information from the transmitting module and the receiving module and processes it; 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.

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

4. The large capacity automatic tracking RF power supply unmanned charging system according to claim 2, characterized in that, The high-precision positioning and tracking module one and the high-precision positioning and tracking module two each include a sensor array and a signal processing unit. The sensor array includes infrared sensors, ultrasonic sensors, or camera sensors for real-time monitoring of the position changes of the receiving module. The signal processing unit processes the signals collected by the sensor array to extract accurate position information.

5. A large-capacity automatic tracking RF power source unattended charging method applied to the large-capacity automatic tracking RF power source unattended charging system of any one of claims 1-4, characterized in that, The method includes the following steps: Step a: Start the transmitting module, perform system initialization and timing, and prepare for the charging process; Step b: Perform service route synchronization to ensure communication connection between the transmitting module and the server; Step c: Power on the transmitting module and the receiving module and initialize them, send the local ID to the server for identity verification, and the transmitting module sends a dual-frequency signal containing the local ID at regular intervals; 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 below the preset threshold, sends a start charging request to the server-transmitting module; Step f: After the transmitting module receives the start charging request, it enters the confirmation charging process; Step g: The transmitting module enters the soft start process, adjusts the PWM frequency to control the current and voltage of the magnetic resonance transmitting coil, to achieve safe energy transmission; Step h: Real-time position monitoring is performed on the transmitting module and the receiving module using the built-in sensor array, and the relative position between the magnetic resonance transmitting coil and the receiving coil is automatically adjusted to maintain the optimal energy transmission path; Step i: Apply an artificial intelligence algorithm to analyze historical charging data and predict the power characteristics of mobile devices, and intelligently adjust the charging parameters based on the prediction results to optimize energy transmission efficiency; Step j: During the charging process, real-time monitoring of current and voltage is performed, and if the detected current is 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 the transmitting module completes the charging parameter adjustment, it sends charging status information to the server to realize remote monitoring and management.

6. The unattended charging method of claim 5, wherein, The adjustment of the PWM frequency in step g includes automatically adjusting the frequency based on the current size change to maintain the current within a safe range; The automatic adjustment process in step h includes: Step h101: Monitor the precise position of the receiving module through the infrared sensors, ultrasonic sensors, or camera sensors of the sensor array; Step h102: Based on the position information, dynamically adjust the power output of the magnetic resonance transmitting coil to adapt to the movement of the receiving module.

7. The unattended charging method of claim 5, wherein, The artificial intelligence algorithm in step i uses a machine learning model to analyze historical charging data; The historical charging data includes charging efficiency, device position and motion trajectory information; and the charging parameter includes power output and frequency.

8. The unattended charging method of claim 7, wherein, The charging state information in step k includes current, voltage and charging state; During the charging process, the transmitting module and the receiving module automatically adapt to different loads to realize charging of different devices such as AGV, mobile phone and electric vehicle.

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