Wireless charging method and system for self-adaptive power distribution

Abstract

The invention provides a wireless charging method and system for self-adaptive power distribution, and aims to realize efficient and safe simultaneous charging of multiple devices through intelligent identification and dynamic adjustment, the system is composed of six modules, each module is responsible for a specific function and cooperates together to realize automatic management of a charging process, device placement is detected through an infrared sensor, and the charging efficiency is improved. NFC and RFID are utilized to read equipment information, an equipment API and a machine learning algorithm are combined to predict charging requirements, initial power distribution is set and dynamically adjusted, multi-band transmission and beam forming technologies are adopted to improve efficiency, a temperature sensor is integrated to prevent overheating, and power is automatically adjusted or a power supply is cut off when necessary, so that the charging efficiency is improved, and the charging cost is reduced. And the flexibility and the safety of the system are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of wireless charging technology, and in particular to a wireless charging method and system with adaptive power allocation. Background Art

[0002] The wireless charging technology currently on the market mainly relies on the principle of electromagnetic induction, which converts electricity into electromagnetic waves, and then the receiving device converts the electromagnetic waves back into electricity. Traditional wireless charging systems usually have a fixed power output, which means that no matter how many devices are connected or what the battery status of the device is, the charger will charge at a constant power.

[0003] Although wireless charging technology has been widely used in various consumer electronic products, it still faces some challenges, especially in terms of efficiency and user experience. The inability to dynamically adjust the power according to the actual needs of the device leads to excess power in some cases and insufficient power in others. When multiple devices are charged at the same time, some devices may charge too fast while others may charge too slowly due to the fixed power allocation. Charging at high power for a long time may cause the device to overheat, affect battery life and even pose a safety hazard. Summary of the invention

[0004] The purpose of the present invention is to provide a wireless charging method and system with adaptive power allocation.

[0005] The problem to be solved by the present invention is: to solve the problems of low efficiency and unreasonable power allocation in traditional wireless charging, by combining infrared sensing, NFC and RFID identification, machine learning prediction and dynamic power adjustment technology, it can intelligently identify devices placed in the charging area, and predict their charging needs based on the battery status and historical data of the devices. The system adaptively adjusts the power allocation to ensure that when multiple devices are charged at the same time, each device can obtain the optimal charging power, thereby improving the charging efficiency and extending the life of the equipment. The system also integrates temperature monitoring and safety protection mechanisms to ensure the safety of the charging process.

[0006] A wireless charging method with adaptive power allocation, the technical solution adopted is as follows: S1: The wireless charging device is equipped with an infrared sensor to detect whether a device is close to or placed on the charging area. It uses NFC and RFID tag reading to identify the type, model and brand of the device and store the detected information. S2: Based on the information detected in S1, the device model and the maximum charging power it supports are understood, the device's current battery status is queried using the device's API and protocol, and the charging speed and time required by the device are predicted based on the device's historical charging records. S3: Set the initial power allocation value and set an initial power level for each device based on the prediction results in S2. Design an adaptive initial power allocation algorithm to automatically increase the power allocation of a device when it is detected that the power level of a device is lower than the preset threshold. S4: During the charging process, the battery status of all devices is continuously monitored, and the power allocation is adjusted in real time as needed. When the battery of a device is nearly full, its power allocation is reduced, while the power allocation of other devices is increased. When multiple devices are charged at the same time, an algorithm is used to achieve dynamic power allocation; S5: Introduces multi-band transmission technology, selects the optimal frequency for different distances and obstacles, uses beamforming technology to focus the direction of energy transmission, and records data during each charging process, including power distribution, charging time and environmental conditions, and uses these data to train and optimize the adaptive algorithm; S6: Integrated temperature sensors monitor the temperature of the device surface and charging plate to prevent overheating. Configure a safety mechanism that automatically reduces power and cuts off power when the temperature exceeds the safety limit. Implement a regular maintenance plan and check the hardware status.

[0007] Furthermore, the wireless charging device in S1 is equipped with an infrared sensor, and uses NFC and RFID tag reading to identify the type, model and brand of the device, including: S11: Install an infrared sensor covering the entire charging area at the wireless charging base, integrate NFC and RFID reading modules, and ensure that all sensors and readers work normally when the wireless charging device is started, and initialize the corresponding communication protocol stack; S12: When an object enters the detection range of the infrared sensor, the wireless charging device receives the signal and combines the time series data of the sensor to determine whether it is a device placement or a false trigger. Once the device placement behavior is detected, the next step of identification is entered; S13: After the device is placed, the reader establishes a connection with the tag on the device and reads the information stored therein, parses the data read from the tag, including the device model and basic brand information, and obtains information including the maximum charging power supported by the device.

[0008] Furthermore, in S2, the current battery status of the device is queried, and a machine learning algorithm is used to predict the charging speed and time required for the device under the current circumstances, including: S21: When the device is placed on the wireless charging device, the current battery status, including power percentage and health status, is queried through the device API. During the charging process, the battery status is re-queried regularly and the charging strategy is adjusted according to the battery status. S22: Extract useful features from historical data, including charging time, charging speed, and device type, and use historical data to train the selected model; S23: When the device is placed on the wireless charging device, the charging speed and time required for the device are predicted. As the charging process proceeds, the prediction results are continuously updated according to new data points, and the predicted optimal charging speed and time are used as the basis for the next power allocation.

[0009] Furthermore, the adaptive power initial allocation algorithm is designed in S3, including: S31: Obtain a list of all devices currently placed on the wireless charging pad, and determine each device Maximum power , minimum power , current power , for each device Set an initial power. ; S32: Load the historical charging records of each device from the database , using support vector machine SVM to predict each device Minimum time to fully charge and optimal power ; S33: Set a power threshold , for each device ,when , then the adjusted power ,when , then the adjusted power , the adjusted power Not exceeding and range.

[0010] Furthermore, in S4, when multiple devices are charged simultaneously, an algorithm is used to achieve dynamic power allocation, including: S41: periodically reading the battery status of the device, including the remaining power percentage and the charging rate, through an API interface provided by the device, and updating the status mark of the device according to the new battery status, defining the status of each device as "charging", "nearly fully charged", "fully charged", and "not charged". S42: two thresholds are set, one for judging whether the device is close to full charge, and the other for judging whether the device needs emergency charging. For devices with a charge lower than the emergency threshold, the highest priority is given, that is, the maximum power is allocated. For devices with a charge in the middle range, that is, between the emergency threshold and the close to full charge threshold, power is allocated in proportion to their current charge. For devices with a charge close to full charge, the power allocation is gradually reduced until the device is fully charged and the power supply is stopped; S43: For each device in "charging", calculate its share of power. ,in To distribute power, is the current power level of the ith device, is the maximum charge level of the device, It is the sum of the power differences of all charging devices; When a device finishes charging, a new power allocation is recalculated for all devices still charging.

[0011] Furthermore, a wireless charging system with adaptive power allocation is used to implement any one of the wireless charging methods with adaptive power allocation, and the wireless charging system with adaptive power allocation includes: a device detection and identification module, a battery status query and prediction module, an adaptive power allocation algorithm module, a dynamic power adjustment and monitoring module, an efficiency optimization and feedback module, and a safety monitoring and protection module; Device detection and identification module: detects whether there is a device placed in the charging area, and identifies the type, model and brand information of the device placed in the charging area; Battery status query and prediction module: obtain the current battery status of the device and predict the optimal charging speed and time required for the device; Adaptive power allocation algorithm module: sets an initial power level for each device and dynamically adjusts power allocation based on the actual charging needs of the device; Dynamic power adjustment and monitoring module: dynamically adjust power allocation when multiple devices are charging at the same time, monitor the charging status of the device, and adjust power according to the status; Efficiency optimization and feedback module: select the transmission frequency of the current environment, focus on the direction of energy transmission, improve efficiency, record charging data, and optimize the adaptive algorithm; Safety monitoring and protection module: monitors the temperature of the device and charging board, and takes protective measures when the temperature is too high.

[0012] The beneficial effects of the present invention are as follows: through the adaptive power allocation algorithm, the system can dynamically adjust the power output according to the actual needs of the device, avoid unnecessary energy waste, and improve the overall charging efficiency; Intelligently adjust the charging speed according to the battery status of the device to avoid overcharging or undercharging, which helps to extend the life of the device battery; It can support charging of multiple types of devices at the same time and can intelligently distribute power among multiple devices, allowing users to charge multiple devices of different brands at the same time without worrying about compatibility issues; Use machine learning algorithms to predict the charging needs of devices and continuously optimize the algorithms based on historical data, so that the system can meet users' charging needs more and more accurately; Temperature monitoring and automatic power adjustment mechanisms ensure that no safety accidents will occur due to overheating under any circumstances, increasing the stability and safety of the system. The system's built-in safety mechanism can take timely measures when abnormal conditions are detected to protect the equipment from damage. By optimizing power distribution, unnecessary energy loss is reduced, energy consumption is lowered, and contribution is made to environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A flow chart of a wireless charging method with adaptive power allocation; Figure 2 A block diagram of a wireless charging system with adaptive power allocation. DETAILED DESCRIPTION

[0014] The present invention is further clearly and completely described below, but the protection scope of the present invention is not limited thereto.

[0015] A wireless charging method with adaptive power allocation, the technical solution adopted is as follows: S1: The wireless charging device is equipped with an infrared sensor to detect whether a device is close to or placed on the charging area. It uses NFC and RFID tag reading to identify the type, model and brand of the device and store the detected information. S2: Based on the information detected in S1, the device model and the maximum charging power it supports are understood, the device's current battery status is queried using the device's API and protocol, and the charging speed and time required by the device are predicted based on the device's historical charging records. S3: Set the initial power allocation value and set an initial power level for each device based on the prediction results in S2. Design an adaptive initial power allocation algorithm to automatically increase the power allocation of a device when it is detected that the power level of a device is lower than the preset threshold. S4: During the charging process, the battery status of all devices is continuously monitored, and the power allocation is adjusted in real time as needed. When the battery of a device is nearly full, its power allocation is reduced, while the power allocation of other devices is increased. When multiple devices are charged at the same time, an algorithm is used to achieve dynamic power allocation; S5: Introduces multi-band transmission technology, selects the optimal frequency for different distances and obstacles, uses beamforming technology to focus the direction of energy transmission, and records data during each charging process, including power distribution, charging time and environmental conditions, and uses these data to train and optimize the adaptive algorithm; S6: Integrated temperature sensors monitor the temperature of the device surface and charging plate to prevent overheating. Configure a safety mechanism that automatically reduces power and cuts off power when the temperature exceeds the safety limit. Implement a regular maintenance plan and check the hardware status.

[0016] Furthermore, the wireless charging device in S1 is equipped with an infrared sensor, and uses NFC and RFID tag reading to identify the type, model and brand of the device, including: S11: Install an infrared sensor covering the entire charging area at the wireless charging base, integrate NFC and RFID reading modules, and ensure that all sensors and readers work normally when the wireless charging device is started, and initialize the corresponding communication protocol stack; S12: When an object enters the detection range of the infrared sensor, the wireless charging device receives the signal and combines the time series data of the sensor to determine whether it is a device placement or a false trigger. Once the device placement behavior is detected, the next step of identification is entered; S13: After the device is placed, the reader establishes a connection with the tag on the device and reads the information stored therein, parses the data read from the tag, including the device model and basic brand information, and obtains information including the maximum charging power supported by the device.

[0017] Furthermore, in S2, the current battery status of the device is queried, and a machine learning algorithm is used to predict the charging speed and time required for the device under the current circumstances, including: S21: When the device is placed on the wireless charging device, the current battery status, including power percentage and health status, is queried through the device API. During the charging process, the battery status is re-queried regularly and the charging strategy is adjusted according to the battery status. S22: Extract useful features from historical data, including charging time, charging speed, and device type, and use historical data to train the selected model; S23: When the device is placed on the wireless charging device, the charging speed and time required for the device are predicted. As the charging process proceeds, the prediction results are continuously updated according to new data points, and the predicted optimal charging speed and time are used as the basis for the next power allocation.

[0018] Furthermore, the adaptive power initial allocation algorithm is designed in S3, including: S31: Obtain a list of all devices currently placed on the wireless charging pad, and determine each device Maximum power , minimum power , current power , for each device Set an initial power. ; S32: Load the historical charging records of each device from the database , using support vector machine SVM to predict each device Minimum time to fully charge and optimal power ; S33: Set a power threshold , for each device ,when , then the adjusted power ,when , then the adjusted power , the adjusted power Not exceeding and range.

[0019] Furthermore, in S4, when multiple devices are charged simultaneously, an algorithm is used to achieve dynamic power allocation, including: S41: periodically reading the battery status of the device, including the remaining power percentage and the charging rate, through an API interface provided by the device, and updating the status mark of the device according to the new battery status, defining the status of each device as "charging", "nearly fully charged", "fully charged", and "not charged". S42: two thresholds are set, one for judging whether the device is close to full charge, and the other for judging whether the device needs emergency charging. For devices with a charge lower than the emergency threshold, the highest priority is given, that is, the maximum power is allocated. For devices with a charge in the middle range, that is, between the emergency threshold and the close to full charge threshold, power is allocated in proportion to their current charge. For devices with a charge close to full charge, the power allocation is gradually reduced until the device is fully charged and the power supply is stopped; S43: For each device in "charging", calculate its share of power. ,in To distribute power, is the current power level of the ith device, is the maximum charge level of the device, It is the sum of the power differences of all charging devices; When a device finishes charging, a new power allocation is recalculated for all devices still charging.

[0020] Furthermore, a wireless charging system with adaptive power allocation is used to implement any one of the wireless charging methods with adaptive power allocation, and the wireless charging system with adaptive power allocation includes: a device detection and identification module, a battery status query and prediction module, an adaptive power allocation algorithm module, a dynamic power adjustment and monitoring module, an efficiency optimization and feedback module, and a safety monitoring and protection module; Device detection and identification module: detects whether there is a device placed in the charging area, and identifies the type, model and brand information of the device placed in the charging area; Battery status query and prediction module: obtain the current battery status of the device and predict the optimal charging speed and time required for the device; Adaptive power allocation algorithm module: sets an initial power level for each device and dynamically adjusts power allocation based on the actual charging needs of the device; Dynamic power adjustment and monitoring module: dynamically adjust power allocation when multiple devices are charging at the same time, monitor the charging status of the device, and adjust power according to the status; Efficiency optimization and feedback module: select the transmission frequency of the current environment, focus on the direction of energy transmission, improve efficiency, record charging data, and optimize the adaptive algorithm; Safety monitoring and protection module: monitors the temperature of the device and charging board, and takes protective measures when the temperature is too high.

[0021] The present invention provides a wireless charging method and system with adaptive power allocation, aiming to realize efficient and safe simultaneous charging of multiple devices through intelligent identification and dynamic adjustment. The system consists of six modules, each of which is responsible for a specific function, and works together to realize the automated management of the charging process. The device placement is detected by an infrared sensor, and the device information is read by NFC and RFID. The charging demand is predicted in combination with the device API and machine learning algorithm, the initial power allocation is set and dynamically adjusted, multi-band transmission and beamforming technology are used to improve efficiency, an integrated temperature sensor is used to prevent overheating, and the power is automatically adjusted or the power is cut off when necessary, which not only improves the charging efficiency, but also enhances the flexibility and safety of the system.

Claims

1. A wireless charging method with adaptive power allocation, characterized in that: include: S1: The wireless charging device is equipped with an infrared sensor to detect whether a device is close to or placed on the charging area. It uses NFC and RFID tag reading to identify the type, model and brand of the device and store the detected information. S2: Based on the information detected in S1, the device model and the maximum charging power it supports are understood, the device's current battery status is queried using the device's API and protocol, and the charging speed and time required by the device under the current circumstances are predicted based on the device's historical charging records. S3: Set the initial power allocation value and set an initial power level for each device based on the prediction results in S2. Design an adaptive initial power allocation algorithm to automatically increase the power allocation of a device when it is detected that the power level of a device is lower than the preset threshold. S4: During the charging process, the battery status of all devices is continuously monitored, and the power allocation is adjusted in real time as needed. When the battery of a device is nearly full, its power allocation is reduced, while the power allocation of other devices is increased. When multiple devices are charged at the same time, an algorithm is used to achieve dynamic power allocation; S5: Introduces multi-band transmission technology, selects the optimal frequency for different distances and obstacles, uses beamforming technology to focus the direction of energy transmission, and records data during each charging process, including power distribution, charging time and environmental conditions, and uses these data to train and optimize the adaptive algorithm; S6: Integrated temperature sensors monitor the temperature of the device surface and charging plate to prevent overheating. Configure a safety mechanism that automatically reduces power and cuts off power when the temperature exceeds the safety limit. Implement a regular maintenance plan and check the hardware status.

2. A wireless charging method with adaptive power allocation as claimed in claim 1, characterized in that: The wireless charging device in S1 is equipped with an infrared sensor and uses NFC and RFID tag reading to identify the type, model and brand of the device, including: S11: Install an infrared sensor covering the entire charging area at the wireless charging base, integrate NFC and RFID reading modules, and ensure that all sensors and readers work normally when the wireless charging device is started, and initialize the corresponding communication protocol stack; S12: When an object enters the detection range of the infrared sensor, the wireless charging device receives the signal and combines the time series data of the sensor to determine whether it is a device placement or a false trigger. Once the device placement behavior is detected, the next step of identification is entered; S13: After the device is placed, the reader establishes a connection with the tag on the device and reads the information stored therein, parses the data read from the tag, including the device model and basic brand information, and obtains information including the maximum charging power supported by the device.

3. The wireless charging method with adaptive power allocation according to claim 1, characterized in that: In S2, the current battery status of the device is queried, and the charging speed and time required for the device under the current circumstances are predicted using a machine learning algorithm, including: S21: When the device is placed on the wireless charging device, the current battery status, including power percentage and health status, is queried through the device API. During the charging process, the battery status is re-queried regularly and the charging strategy is adjusted according to the battery status. S22: Extract useful features from historical data, including charging time, charging speed, and device type, and use historical data to train the selected model; S23: When the device is placed on the wireless charging device, the charging speed and time required for the device are predicted. As the charging process proceeds, the prediction results are continuously updated according to new data points, and the predicted optimal charging speed and time are used as the basis for the next power allocation.

4. The wireless charging method with adaptive power allocation according to claim 1, characterized in that: The adaptive power initial allocation algorithm is designed in S3, including: S31: Obtain a list of all devices currently placed on the wireless charging pad, and determine each device Maximum power , minimum power , current power , for each device Set an initial power. ; S32: Load the historical charging records of each device from the database , using support vector machine SVM to predict each device Minimum time to fully charge and optimal power ; S33: Set a power threshold , for each device ,when , then the adjusted power ,when , then the adjusted power , the adjusted power Not exceeding and range.

5. The wireless charging method with adaptive power allocation according to claim 1, characterized in that: In the S4, when multiple devices are charged simultaneously, an algorithm is used to achieve dynamic power allocation, including: S41: periodically reading the battery status of the device, including the remaining power percentage and the charging rate, through the API interface provided by the device, and updating the status mark of the device according to the new battery status, defining the status of each device as "charging", "nearly fully charged", "fully charged", and "not charged"; S42: two thresholds are set, one for judging whether the device is close to full charge, and the other for judging whether the device needs emergency charging. For devices with a charge lower than the emergency threshold, the highest priority is given, that is, the maximum power is allocated. For devices with a charge in the middle range, that is, between the emergency threshold and the close to full charge threshold, power is allocated in proportion to their current charge. For devices with a charge close to full charge, the power allocation is gradually reduced until the device is fully charged and the power supply is stopped; S43: For each device in "charging", calculate its share of power. ,in To distribute power, is the current power level of the ith device, is the maximum charge level of the device, It is the sum of the power differences of all charging devices; When a device finishes charging, a new power allocation is recalculated for all devices still charging.

6. A wireless charging system with adaptive power distribution, characterized in that: A wireless charging method for implementing any one of the above-mentioned adaptive power allocations, wherein the wireless charging system for adaptive power allocation comprises: a device detection and identification module, a battery status query and prediction module, an adaptive power allocation algorithm module, a dynamic power adjustment and monitoring module, an efficiency optimization and feedback module, and a safety monitoring and protection module; Device detection and identification module: detects whether there is a device placed in the charging area, and identifies the type, model and brand information of the device placed in the charging area; Battery status query and prediction module: obtain the current battery status of the device and predict the optimal charging speed and time required for the device; Adaptive power allocation algorithm module: sets an initial power level for each device and dynamically adjusts power allocation based on the actual charging needs of the device; Dynamic power adjustment and monitoring module: dynamically adjust power allocation when multiple devices are charging at the same time, monitor the charging status of the device, and adjust power according to the status; Efficiency optimization and feedback module: select the transmission frequency of the current environment, focus on the direction of energy transmission, improve efficiency, record charging data, and optimize the adaptive algorithm; Safety monitoring and protection module: monitors the temperature of the device and charging board, and takes protective measures when the temperature is too high.

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