Wireless charging power adjusting method and device, equipment and storage medium

Abstract

The invention relates to a wireless charging power adjusting method and device, equipment and a storage medium, and the method comprises the following steps: carrying out the continuous collection of the charging state information of receiving end equipment, and obtaining the charging state data of the receiving end equipment; performing temperature data acquisition on the receiving end equipment through a preset temperature sensor to obtain acquired temperature data, and performing time sequence risk assessment on the receiving end equipment based on the acquired temperature data to obtain a time sequence risk assessment value; inputting the time sequence risk assessment value into a preset division interval in the transmitting terminal equipment to obtain a charging strategy corresponding to the division interval; selecting a corresponding charging power gear in the transmitting terminal equipment based on the charging state data; and performing output power adjustment on the transmitting terminal equipment based on the charging power gear to obtain adaptive output power, thereby solving the technical problem that a conventional wireless charging system generally adopts fixed charging power and cannot perform dynamic adjustment according to actual requirements and environmental conditions of the receiving terminal equipment.

Description

Technical Field

[0001] The present invention relates to the field of wireless charging technology, and in particular to a wireless charging power adjustment method, device, equipment and storage medium. Background Art

[0002] With the popularization of mobile devices and the development of technology, wireless charging technology has attracted more and more attention due to its convenience and flexibility. However, in practical applications, some problems in the wireless charging process have gradually emerged, especially those in terms of charging efficiency and safety. For example, different devices have different requirements for charging power, and existing wireless charging technologies often find it difficult to achieve accurate power matching, resulting in low charging efficiency or overcharging. In addition, thermal management in the wireless charging process is also an issue that cannot be ignored, as the heat generated during the charging process may damage the device and even cause safety hazards. In the practical application of wireless charging technology, how to effectively monitor and control the charging process has become a technical problem that needs to be solved urgently. Traditional wireless charging systems usually use a fixed charging power and cannot be dynamically adjusted according to the actual needs and environmental conditions of the receiving device, which not only limits the improvement of charging efficiency, but also affects the user experience. Especially when multiple devices are charged at the same time, how to ensure that each device can obtain the most suitable charging power and avoid battery damage or reduced charging efficiency due to improper charging power has become an important direction for the development of wireless charging technology.

[0003] Therefore, it is particularly important to develop a method that can intelligently adjust the charging power. This method not only needs to be able to monitor the charging status of the receiving device in real time, such as key parameters such as the current power and charging speed, but also needs to combine temperature data to evaluate the safety risks of the device during the charging process. Through the analysis of these comprehensive data, the most appropriate charging strategy can be formulated for different charging scenarios, thereby ensuring the safety of charging and improving the charging efficiency, meeting the user's demand for fast, efficient and safe wireless charging. Such technological progress is of great significance to promoting the widespread application of wireless charging technology. Summary of the invention

[0004] The main purpose of the present invention is to provide a wireless charging power adjustment method, device, equipment and storage medium, which solves the technical problem that traditional wireless charging systems usually use a fixed charging power and cannot be dynamically adjusted according to the actual needs and environmental conditions of the receiving device.

[0005] To achieve the above object, the present invention provides a wireless charging power adjustment method, comprising the following steps: Continuously collect charging status information of the receiving device to obtain charging status data of the receiving device; wherein the charging status data includes current power and current charging speed; Collecting temperature data of the receiving end device through a preset temperature sensor to obtain collected temperature data, and performing a timing risk assessment on the receiving end device based on the collected temperature data to obtain a timing risk assessment value; Input the timing risk assessment value into the preset division interval in the transmitting end device to obtain the charging strategy corresponding to the division interval; wherein the charging strategy includes a stop charging strategy, a low-gear short-time charging strategy and a normal gear switching charging strategy; When the charging strategy is a normal gear switching charging strategy, selecting a corresponding charging power gear in the transmitting end device based on the charging status data; The output power of the transmitting device is adjusted based on the charging power gear to obtain an adaptive output power, and the receiving device is safely charged based on the adaptive output power.

[0006] Furthermore, the continuously collecting charging status information of the receiving device to obtain the charging status data of the receiving device includes: The preset power monitoring module samples the battery power of the receiving device in real time to obtain the current power data; Based on the current power data, the charging speed of the receiving device is calculated to obtain the current charging speed.

[0007] Further, the collected temperature data is a surface temperature distribution matrix, the temperature sensor is a multi-point thermocouple array sensor, the temperature data of the receiving end device is collected by a preset temperature sensor to obtain the collected temperature data, and the timing risk assessment of the receiving end device is performed based on the collected temperature data to obtain a timing risk assessment value, including: The temperature of the surface of the receiving end device is scanned by the multi-point thermocouple array sensor to obtain a surface temperature distribution matrix; wherein the rows of the surface temperature distribution matrix represent the coordinates of the receiving end device in the Y-axis direction, the columns of the surface temperature distribution matrix represent the coordinates in the X-axis direction, and the elements in the surface temperature distribution matrix represent temperature values; Reconstructing a thermal map of the receiving device based on the surface temperature distribution matrix to obtain a three-dimensional thermal distribution model; Eliminating noise from the three-dimensional heat distribution model to obtain an optimized heat distribution model; Perform hotspot identification on the receiving-end device based on the optimized thermal distribution model to obtain a hotspot area coordinate set; The temperature of the hot spot area coordinate set is predicted by a preset long short-term memory network to obtain a temperature change trend; Based on the temperature change trend, a timing thermal runaway risk assessment is performed on the receiving end device to obtain a timing risk assessment value.

[0008] Furthermore, the charging strategy includes a stop charging strategy, a low gear short-time charging strategy and a normal gear switching charging strategy. The timing risk assessment value is input into a preset division interval in the transmitting end device to obtain a charging strategy corresponding to the division interval, including: Using a preset hyperbolic tangent transformation function to perform a nonlinear transformation on the time series risk assessment value to obtain a time series transformation risk index; A time series risk curve is constructed based on the time series transformation risk index, and the time series risk curve is mapped into a preset coordinate system to obtain a time series risk curve with a coordinate system; wherein the ordinate of the coordinate system represents the time series transformation risk index, and the abscissa of the coordinate system represents time; Monitor the point coordinates in the time series risk curve in the coordinate system through a preset Kalman filter algorithm to obtain the ordinate value corresponding to the point coordinates; When the ordinate value is within a first interval of the divided intervals, a charging stop strategy corresponding to the first interval is obtained; When the ordinate value is within the second interval of the divided intervals, a low-gear short-time charging strategy corresponding to the second interval is obtained; When the ordinate value is within a third interval in the divided intervals, a normal gear-switching charging strategy corresponding to the third interval is obtained.

[0009] Furthermore, the normal gear switching charging strategy includes a high gear charging strategy, a middle gear charging strategy and a low gear charging strategy. When the charging strategy is the normal gear switching charging strategy, the corresponding charging power gear in the transmitting end device is selected based on the charging status data, including: When the charging strategy is a normal gear-switching charging strategy, a real-time charging demand analysis is performed on the receiving-end device based on the charging status data through a preset charging strategy model to obtain a charging demand index; Calculating a magnitude relationship between the charging demand index and a preset charging demand index range, and selecting a corresponding charging power gear in the transmitting end device according to the magnitude relationship, wherein selecting a corresponding charging power gear in the transmitting end device according to the magnitude relationship includes: When the magnitude relationship is that the charging demand index is higher than the preset charging demand index range, the charging power gear corresponding to the high-speed charging strategy is adopted; When the magnitude relationship is that the charging demand index is within the preset charging demand index range, the charging power gear corresponding to the mid-gear charging strategy is adopted; When the magnitude relationship is that the charging demand index is lower than a preset charging demand index range, the charging power gear corresponding to the low-gear charging strategy is adopted.

[0010] Furthermore, the transmitting end device is provided with a charging coil and a resonant circuit, and the output power of the transmitting end device is adjusted based on the charging power level to obtain an adaptive output power, including: By using a preset power mapping algorithm, power mapping conversion is performed on the transmitting end device based on the charging power gear to obtain an initial power parameter; Performing impedance matching adjustment on the resonant circuit based on the initial power parameter to obtain matching resonance parameters; Dynamically optimizing the matching resonance parameters to obtain an optimized operating frequency; Modulating the magnetic field intensity of the transmitting end device based on the optimized operating frequency to obtain a modulated magnetic field distribution; Performing homogenization processing on the modulated magnetic field distribution to obtain a uniform magnetic field; The coil current of the charging coil is closed-loop controlled based on the uniform magnetic field to obtain the adaptive output power.

[0011] Furthermore, the performing closed-loop control of the coil current of the charging coil based on the uniform magnetic field to obtain the adaptive output power includes: The uniform magnetic field is measured in real time by a preset magnetic sensor to obtain the real-time magnetic field strength; The real-time magnetic field strength is converted into an expected value of the coil current of the corresponding charging coil through a preset magnetic-electric conversion model; Adjusting the initial current of the charging coil based on the expected value of the coil current to obtain a preliminary adjusted current; Performing spectrum characteristic analysis on the preliminary adjustment current to obtain spectrum characteristic data; Eliminating harmonic components of the preliminary adjustment current based on the frequency spectrum characteristic data to obtain a coil current after harmonic elimination; Obtaining the adaptive output power based on the coil current after harmonic elimination and the real-time magnetic field strength; The adaptive output power is calculated using the following formula: Among them, the represents the adaptive output power, Represents the coil current after harmonic elimination, over time change Indicates the real-time magnetic field strength over time change represents the complex exponential function, is the angular frequency corresponding to the operating frequency of the transmitting device, which is ,in is the operating frequency, It refers to a complete working cycle time, that is, .

[0012] The present invention also provides a wireless charging power adjustment device, comprising: A collection module, used to continuously collect charging status information of the receiving device to obtain charging status data of the receiving device; wherein the charging status data includes the current power and the current charging speed; A collection module, used to collect temperature data of the receiving end device through a preset temperature sensor to obtain collected temperature data, and to perform a timing risk assessment on the receiving end device based on the collected temperature data to obtain a timing risk assessment value; A division module, used to input the timing risk assessment value into a division interval preset in the transmitting end device to obtain a charging strategy corresponding to the division interval; wherein the charging strategy includes a stop charging strategy, a low-gear short-time charging strategy, and a normal gear switching charging strategy; A selection module, configured to select a corresponding charging power gear in the transmitting end device based on the charging status data when the charging strategy is a normal gear switching charging strategy; The adjustment module is used to adjust the output power of the transmitting end device based on the charging power gear to obtain an adaptive output power, and safely charge the receiving end device based on the adaptive output power.

[0013] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0014] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0015] The wireless charging power regulation method provided by the present invention comprises the following steps: continuously collecting charging status information of a receiving-end device to obtain charging status data of the receiving-end device; wherein the charging status data includes the current power and the current charging speed; collecting temperature data of the receiving-end device through a preset temperature sensor to obtain collected temperature data, and performing a timing risk assessment on the receiving-end device based on the collected temperature data to obtain a timing risk assessment value; inputting the timing risk assessment value into a preset division interval in a transmitting-end device to obtain a charging strategy corresponding to the division interval; wherein the charging strategy includes a stop charging strategy, a low-gear short-time charging strategy, and a normal gear switching charging strategy; When the charging strategy is a normal gear switching charging strategy, the corresponding charging power gear in the transmitter device is selected based on the charging status data; the output power of the transmitter device is adjusted based on the charging power gear to obtain an adaptive output power, and the receiving device is safely charged based on the adaptive output power. The above technical means solve the technical problem that the traditional wireless charging system usually adopts a fixed charging power and cannot be dynamically adjusted according to the actual needs and environmental conditions of the receiving device. The method realizes that by inputting the timing risk assessment value into the preset division interval in the transmitter device, it can provide personalized charging solutions for receiving devices of different models and brands. This means that no matter what type of mobile device the user uses, they can enjoy efficient and safe wireless charging services, which enhances the compatibility and universality of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a schematic diagram of the steps of a wireless charging power adjustment method in one embodiment of the present invention; Figure 2 is a structural block diagram of a wireless charging power adjustment device in one embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0017] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] like Figure 1 As shown, Figure 1 It is a schematic diagram of the steps of a wireless charging power adjustment method in one embodiment of the present invention; In one embodiment of the present invention, a wireless charging power adjustment method is provided, comprising the following steps: Step S1, continuously collect charging status information of the receiving device to obtain charging status data of the receiving device; wherein the charging status data includes the current power and the current charging speed.

[0020] Specifically, the charging status information of the receiving device is continuously collected to obtain the charging status data of the receiving device. This process is the basis of the entire wireless charging power regulation method. Specifically, this means that during the charging process, the transmitting device will continuously send requests to the receiving device to obtain its current charging status information, which includes but is not limited to the current power and the current charging speed. In this way, the transmitting end can understand the charging progress and battery status of the receiving device in real time, providing a basis for subsequent power regulation. For example, in an application scenario of wireless charging of a smartphone, when the mobile phone is placed on a wireless charging pad and starts charging, the charging pad (i.e., the transmitting end) will query the mobile phone (i.e., the receiving end) through a wireless communication protocol at regular time intervals (such as a few seconds or tens of seconds) for its current power percentage and charging rate. If the mobile phone is in the fast charging stage, the charging pad may receive higher charging speed data; conversely, if the mobile phone is close to full power, the charging speed will be significantly reduced. Based on these real-time updated charging status data, the charging pad can make more accurate power regulation decisions to ensure that both efficient charging can be achieved during the entire charging process, and the safety of the device can be guaranteed to avoid overcharging or undercharging.

[0021] Step S2: collecting temperature data of the receiving-end device through a preset temperature sensor to obtain collected temperature data, and performing a timing risk assessment on the receiving-end device based on the collected temperature data to obtain a timing risk assessment value.

[0022] Specifically, the temperature data of the receiving device is collected by a preset temperature sensor to obtain the collected temperature data, and the timing risk assessment of the receiving device is performed based on the collected temperature data to obtain the timing risk assessment value. This is a key step to ensure the safety of the device during wireless charging. In actual operation, the temperature sensor will be pre-installed in the key parts of the receiving device, such as near the battery or around the charging coil. These locations are usually the places where the device is most likely to generate heat when charging. When the receiving device starts charging, the temperature sensor will continue to monitor the temperature changes in these areas and transmit the collected temperature data to the transmitting device in real time through the wireless communication protocol. After receiving these temperature data, the transmitting device will immediately start the built-in risk assessment algorithm, which will calculate a timing risk assessment value based on the historical temperature data and the current temperature change trend. This assessment value reflects the possibility of overheating of the device in the current charging state. For example, in the application scenario of wireless charging of smartphones, if the temperature of the mobile phone suddenly rises at a certain moment during the charging process and exceeds the preset safety threshold, the transmitting device will calculate a higher timing risk assessment value. Based on this evaluation value, the transmitter device can quickly take measures, such as reducing the charging power or suspending charging, to prevent the device from being damaged by overheating. In this way, not only can the safety of the charging process be ensured, but the service life of the device can also be extended, improving the user experience.

[0023] Step S3, inputting the timing risk assessment value into the preset division interval in the transmitting end device to obtain the charging strategy corresponding to the division interval; wherein the charging strategy includes a stop charging strategy, a low gear short-time charging strategy and a normal gear switching charging strategy.

[0024] Specifically, the transmitter device has multiple preset division intervals, each of which corresponds to a different charging strategy, including a stop charging strategy, a low-gear short-time charging strategy, and a normal gear switching charging strategy. When the transmitter device receives the timing risk assessment value, it will immediately map the value to the corresponding division interval to determine the most appropriate charging strategy under the current circumstances. For example, in the application scenario of wireless charging of smartphones, it is assumed that three division intervals are set inside the transmitter device: 0-50 represents a low-risk area, 51-80 represents a medium-risk area, and 81-100 represents a high-risk area. If the timing risk assessment value falls in the low-risk area, the transmitter device will choose the normal gear switching charging strategy, which means that it can continue to provide appropriate charging power according to the current charging state. If the assessment value falls in the medium-risk area, the transmitter device may choose a low-gear short-time charging strategy, that is, temporarily reduce the charging power to observe the temperature change of the device to ensure that the device will not be damaged due to long-term high temperature. If the assessment value reaches the high-risk area, the transmitter device will immediately execute the stop charging strategy and completely interrupt the charging process until the device temperature drops to a safe range before resuming charging. In this way, the transmitting device can dynamically adjust the charging strategy based on real-time temperature data and risk assessment results, which not only ensures the safety of charging, but also maximizes the charging efficiency and meets the user's needs for fast and stable charging.

[0025] Step S4: When the charging strategy is a normal gear switching charging strategy, a corresponding charging power gear in the transmitting end device is selected based on the charging status data.

[0026] Specifically, when the transmitter device determines that the normal switching gear charging strategy should be adopted according to the timing risk assessment value, it will further refer to the charging status data of the receiving device, including the current power and the current charging speed, to determine the most suitable charging power gear. This is because different charging states correspond to different charging requirements. Reasonable power gear selection can maximize charging efficiency while avoiding overcharging or undercharging. For example, in the application scenario of wireless charging of smartphones, it is assumed that the transmitter device supports multiple charging power gears, such as 5W, 10W, 15W, etc. When the mobile phone is just starting to charge, the power is low and the charging speed is slow. At this time, the transmitter device may choose a high power gear of 15W to quickly increase the power of the mobile phone. As the charging process proceeds, the power of the mobile phone gradually increases, and the charging speed will also change. When the power of the mobile phone reaches about 70%, the charging speed may slow down. At this time, the transmitter device may switch to a medium power gear of 10W to balance the charging speed and battery health. Finally, when the phone's battery is close to 100%, in order to prevent overcharging and protect the battery, the transmitter device will adjust to a low power level of 5W again to ensure a smooth end to the charging process. In this way, the transmitter device can dynamically select the most appropriate charging power level based on the real-time charging status of the receiver device, which not only improves the charging efficiency but also ensures the safety and stability of the charging process. This intelligent charging management method greatly improves the user experience and makes wireless charging more intelligent and reliable.

[0027] Step S5: adjusting the output power of the transmitting device based on the charging power gear to obtain an adaptive output power, and safely charging the receiving device based on the adaptive output power.

[0028] Specifically, when the transmitter device selects the appropriate charging power level according to the charging status data and timing risk assessment value of the receiver device, it will accurately adjust its output power according to the selected power level to generate adaptive output power. This process involves the power control module inside the transmitter device, which adjusts the parameters of its output circuit according to the selected power level to ensure that the output power meets the current charging requirements. For example, in the application scenario of wireless charging of smartphones, it is assumed that the transmitter device has selected a medium power level of 10W according to the charging status and temperature data of the mobile phone. Next, the power control module of the transmitter device will adjust its output circuit to accurately set the output power to 10W. In this way, the transmitter device can stably supply power to the mobile phone at a power of 10W, ensuring that the charging process is both efficient and safe. If the temperature of the mobile phone suddenly rises during the charging process and the timing risk assessment value enters the medium risk zone, the transmitter device may reselect a low power level of 5W. At this time, the power control module will adjust the output circuit again to reduce the output power to 5W to reduce heat and ensure that the mobile phone will not be damaged by overheating. In this way, the transmitter device can dynamically adjust the output power according to the real-time charging status and temperature data of the receiver device, generating adaptive output power, thereby ensuring the charging efficiency while ensuring the safety of the charging process. This intelligent power adjustment mechanism not only improves the overall performance of the charging system, but also greatly enhances the user experience, making wireless charging more intelligent, efficient and safe.

[0029] In a specific embodiment, the continuously collecting charging status information of the receiving device to obtain the charging status data of the receiving device includes: The preset power monitoring module samples the battery power of the receiving device in real time to obtain the current power data; Based on the current power data, the charging speed of the receiving device is calculated to obtain the current charging speed.

[0030] Specifically, first, the battery of the receiving device is sampled in real time through a preset power monitoring module to obtain the current power data. The power monitoring module is usually a hardware component integrated inside the receiving device, which can read the current power of the battery at a very high frequency (such as multiple times per second). This module will regularly send requests to the battery management system to obtain the remaining power percentage of the battery and store these data in the memory of the device. For example, in the application scenario of wireless charging of smartphones, when the mobile phone is placed on the wireless charging pad to start charging, the power monitoring module inside the mobile phone will continuously read the current power of the battery and transmit these data to the charging pad (i.e., the transmitting device) in real time through the wireless communication protocol. In this way, the transmitting device can understand the power changes of the mobile phone at any time and provide accurate data support for subsequent power regulation. Secondly, based on the current power data, the charging speed of the receiving device is calculated to obtain the current charging speed. The calculation of the charging speed is achieved by comparing the results of two consecutive power samplings. Specifically, the power monitoring module will perform two power samplings at a fixed time interval (such as every 10 seconds or every 30 seconds), and then calculate the power difference between the two samples and divide it by the time interval to obtain the average charging speed during this period. For example, assuming that during a charging process, the power monitoring module of the mobile phone measures 20% of the power at the 10th second and 22% at the 20th second, then during these 10 seconds, the charging speed of the mobile phone is (22% - 20%) / 10 seconds = 0.2% per second. In this way, the transmitter device can understand the charging speed of the receiving device in real time, so as to better adjust the charging strategy. These two links are closely combined to form a continuous collection process of charging status information. Through real-time power sampling and charging speed calculation, the transmitter device can fully grasp the charging status of the receiving device and provide accurate data support for subsequent power adjustment. For example, in the application scenario of wireless charging of smart phones, when the mobile phone just starts to charge, the power monitoring module will frequently read the battery power and calculate a higher charging speed. Based on these data, the transmitter device will select a higher charging power gear to quickly increase the power of the mobile phone. As the charging process progresses, the battery level of the mobile phone gradually increases and the charging speed will gradually slow down. At this time, the transmitting device will adjust the charging power in time according to the latest charging speed data to ensure that the charging process is both efficient and safe.

[0031] In a specific embodiment, the collected temperature data is a surface temperature distribution matrix, the temperature sensor is a multi-point thermocouple array sensor, the temperature data of the receiving end device is collected by a preset temperature sensor to obtain the collected temperature data, and the timing risk assessment of the receiving end device is performed based on the collected temperature data to obtain a timing risk assessment value, including: The temperature of the surface of the receiving end device is scanned by the multi-point thermocouple array sensor to obtain a surface temperature distribution matrix; wherein the rows of the surface temperature distribution matrix represent the coordinates of the receiving end device in the Y-axis direction, the columns of the surface temperature distribution matrix represent the coordinates in the X-axis direction, and the elements in the surface temperature distribution matrix represent temperature values; Reconstructing a thermal map of the receiving device based on the surface temperature distribution matrix to obtain a three-dimensional thermal distribution model; Eliminating noise from the three-dimensional heat distribution model to obtain an optimized heat distribution model; Perform hotspot identification on the receiving-end device based on the optimized thermal distribution model to obtain a hotspot area coordinate set; The temperature of the hot spot area coordinate set is predicted by a preset long short-term memory network to obtain a temperature change trend; Based on the temperature change trend, a timing thermal runaway risk assessment is performed on the receiving end device to obtain a timing risk assessment value.

[0032] Specifically, the collected temperature data is a surface temperature distribution matrix, the temperature sensor is a multi-point thermocouple array sensor, the temperature data of the receiving device is collected by a preset temperature sensor to obtain the collected temperature data, and the timing risk assessment of the receiving device is performed based on the collected temperature data to obtain the timing risk assessment value. This process is an important step to ensure the safe operation of the wireless charging system. Specifically, this process involves multiple links: temperature scanning, heat map reconstruction, noise elimination, hot spot identification, temperature prediction and timing thermal runaway risk assessment. First, the surface of the receiving device is scanned by the multi-point thermocouple array sensor to obtain a surface temperature distribution matrix. The multi-point thermocouple array sensor is a high-precision temperature measurement device, which can arrange multiple temperature sensing points on the surface of the receiving device, and each sensing point will collect the temperature data of the point in real time. These temperature data are organized into a matrix form, called a surface temperature distribution matrix. In this matrix, the row represents the coordinate of the receiving device in the Y-axis direction, the column represents the coordinate in the X-axis direction, and each element in the matrix represents the temperature value of the point. For example, in the application scenario of wireless charging of smartphones, when the mobile phone is placed on the wireless charging pad to start charging, the multi-point thermocouple array sensor will cover the entire back of the mobile phone, and each sensing point will collect the temperature data of the point in real time, and organize the data into a two-dimensional matrix. Assuming that the size of the matrix is ​​10x10, there are 100 temperature sensing points, and the temperature value of each point will be recorded to form a 10x10 surface temperature distribution matrix. Next, the receiving device is reconstructed based on the surface temperature distribution matrix to obtain a three-dimensional thermal distribution model. Thermal map reconstruction refers to converting the two-dimensional surface temperature distribution matrix into a three-dimensional thermal distribution model to more intuitively display the temperature distribution of the device surface. This process can be achieved through an interpolation algorithm, that is, interpolating between known temperature sensing points to generate a smooth temperature distribution surface. For example, assuming that in a 10x10 surface temperature distribution matrix, the temperature of some points is higher and the temperature of other points is lower. Through the interpolation algorithm, a three-dimensional thermal distribution model can be generated to clearly show which areas have higher temperatures and which areas have lower temperatures. This three-dimensional thermal distribution model can not only help us better understand the temperature distribution on the surface of the equipment, but also provide a basis for subsequent analysis. Then, the three-dimensional thermal distribution model is subjected to noise elimination to obtain an optimized thermal distribution model. In the actual process of collecting temperature data, due to the influence of various factors, such as sensor errors, environmental interference, etc., the collected temperature data may contain some noise. These noises will affect the subsequent analysis results, so noise elimination processing is required. Commonly used noise elimination methods include filter processing, smoothing algorithms, etc. For example, a low-pass filter can be used to filter the temperature data in the three-dimensional thermal distribution model to remove high-frequency noise and retain low-frequency signals.After the noise elimination process, the optimized thermal distribution model is more accurate and can more realistically reflect the temperature distribution of the device surface. Next, the receiving end device is subjected to hot spot identification based on the optimized thermal distribution model to obtain a hot spot area coordinate set. Hot spot identification refers to finding areas with abnormally high temperatures in the optimized thermal distribution model, which are called hot spots. Hot spot identification can be achieved by setting a temperature threshold, that is, if the temperature of a certain area exceeds the preset threshold, the area is considered to be a hot spot. For example, assuming that in the optimized thermal distribution model, an area with a temperature exceeding 40°C is considered a hot spot, then all points with a temperature exceeding 40°C can be found by traversing each point in the model, and the coordinates of these points are recorded to form a hot spot area coordinate set. The coordinate information of these hot spot areas will be used for subsequent temperature prediction and risk assessment. Subsequently, the hot spot area coordinate set is subjected to temperature prediction by a preset long short-term memory network to obtain a temperature change trend. Long short-term memory network (LSTM) is a special recurrent neural network that is particularly suitable for processing time series data. In this step, the LSTM network predicts the temperature change trend of the hot spot area in the future period of time based on the historical temperature data and the hot spot area coordinate set. For example, assuming that we have collected temperature data of the hotspot area in the past 10 minutes, the LSTM network will predict the temperature change of the hotspot area in the next 10 minutes based on these data. In this way, potential overheating risks can be discovered in advance, providing a basis for subsequent risk assessment. Finally, the receiving device is subjected to a sequential thermal runaway risk assessment based on the temperature change trend to obtain a sequential risk assessment value. Sequential thermal runaway risk assessment refers to assessing the risk of thermal runaway of the device in the future based on the predicted temperature change trend. Specifically, the sequential risk assessment value can be calculated by setting a series of risk assessment indicators, such as the temperature rise rate, the maximum temperature value, etc. For example, if the prediction results show that the temperature of a hotspot area will rise rapidly in the next 10 minutes and may exceed the safe temperature upper limit of the device, the sequential risk assessment value of the area will be set to high risk. Based on these risk assessment values, the transmitting device can take corresponding measures, such as reducing the charging power or suspending charging, to prevent the device from being damaged due to overheating. Through the above steps, the wireless charging system can monitor the temperature changes of the receiving device in real time, detect potential overheating risks in time, and take corresponding measures to ensure the safety and reliability of the charging process.

[0033] In a specific embodiment, the charging strategy includes a stop charging strategy, a low gear short-time charging strategy and a normal gear switching charging strategy. The timing risk assessment value is input into a preset division interval in the transmitting end device to obtain a charging strategy corresponding to the division interval, including: Using a preset hyperbolic tangent transformation function to perform a nonlinear transformation on the time series risk assessment value to obtain a time series transformation risk index; A time series risk curve is constructed based on the time series transformation risk index, and the time series risk curve is mapped into a preset coordinate system to obtain a time series risk curve with a coordinate system; wherein the ordinate of the coordinate system represents the time series transformation risk index, and the abscissa of the coordinate system represents time; Monitor the point coordinates in the time series risk curve in the coordinate system through a preset Kalman filter algorithm to obtain the ordinate value corresponding to the point coordinates; When the ordinate value is within a first interval of the divided intervals, a charging stop strategy corresponding to the first interval is obtained; When the ordinate value is within the second interval of the divided intervals, a low-gear short-time charging strategy corresponding to the second interval is obtained; When the ordinate value is within a third interval in the divided intervals, a normal gear-switching charging strategy corresponding to the third interval is obtained.

[0034] Specifically, the charging strategy includes a stop charging strategy, a low-gear short-time charging strategy, and a normal gear switching charging strategy. The timing risk assessment value is input into the preset division interval in the transmitting end device to obtain the charging strategy corresponding to the division interval. This process ensures that the wireless charging system can dynamically adjust the charging strategy according to the real-time state of the device, thereby ensuring the safety and efficiency of charging. Specifically, this process involves multiple steps: nonlinear transformation, construction of a timing risk curve, Kalman filter monitoring, and selection of a division interval strategy. First, the timing risk assessment value is nonlinearly transformed using a preset hyperbolic tangent transformation function to obtain a timing transformation risk index. The hyperbolic tangent transformation function is a commonly used nonlinear transformation function that can map the input timing risk assessment value to a specific range, usually between -1 and 1. This nonlinear transformation helps to highlight high risk values ​​while compressing low risk values, making risk assessment more sensitive. For example, in the application scenario of wireless charging of smart phones, assuming that the timing risk assessment value at a certain moment is 0.8, it can be converted into a timing transformation risk index through the hyperbolic tangent transformation function, assuming that the converted value is 0.95. This value not only retains the information of the original risk assessment value, but also enhances the discrimination of high risk values ​​through nonlinear transformation. Next, a time series risk curve is constructed based on the time series transformation risk index, and the time series risk curve is mapped to a preset coordinate system to obtain a time series risk curve with a coordinate system. The time series risk curve is a risk index curve that changes over time, which can intuitively show the risk changes of the device during the charging process. In the preset coordinate system, the ordinate represents the time series transformation risk index and the abscissa represents time. For example, suppose that within the first 10 minutes after the start of charging, the change of the time series transformation risk index is as follows: 0.2 in the first minute, 0.3 in the second minute, 0.4 in the third minute, and so on, until 0.8 in the tenth minute. These data can be drawn into a time series risk curve, showing that the risk index gradually increases over time. Then, the point coordinates in the time series risk curve in the coordinate system are monitored by a preset Kalman filter algorithm to obtain the ordinate value corresponding to the point coordinates. The Kalman filter algorithm is a commonly used state estimation method that can effectively remove noise and extract the true risk value in the time series risk curve. Through the Kalman filter algorithm, each point on the timing risk curve can be monitored in real time to obtain its corresponding ordinate value. For example, assuming that at the 5th minute, the coordinates of the point on the timing risk curve are (5, 0.5), the Kalman filter algorithm can be used to obtain the true ordinate value of the point, assuming it is 0.52. This value more accurately reflects the true risk status of the device at the 5th minute. Finally, according to the position of the ordinate value in the divided interval, the corresponding charging strategy is selected.Specifically, when the ordinate value is within the first interval of the divided interval, the stop charging strategy corresponding to the first interval is obtained; when the ordinate value is within the second interval of the divided interval, the low-gear short-time charging strategy corresponding to the second interval is obtained; when the ordinate value is within the third interval of the divided interval, the normal switching gear charging strategy corresponding to the third interval is obtained. Assume that three division intervals are preset inside the transmitter device: 0-0.3 represents a low-risk area, 0.3-0.7 represents a medium-risk area, and 0.7-1.0 represents a high-risk area. For example, in the application scenario of wireless charging of smartphones, it is assumed that at the 5th minute, the time series transformation risk index obtained by the Kalman filter algorithm is 0.52, which falls within the medium-risk area (0.3-0.7), so the transmitter device will choose a low-gear short-time charging strategy. This means that the charging board will temporarily reduce the charging power, for example, from 15W to 10W, to observe the temperature change of the device to ensure that the device will not be damaged due to long-term high temperature. If the timing change risk index rises to 0.85 at the 10th minute, falling within the high-risk zone (0.7-1.0), the transmitter device will immediately execute the stop charging strategy and completely interrupt the charging process until the device temperature drops to a safe range before resuming charging. In the early stage of charging, assuming that the timing change risk index is 0.2, falling within the low-risk zone (0-0.3), the transmitter device will choose the normal gear switching charging strategy and continue to charge at the current charging power. Through the above steps, the wireless charging system can dynamically adjust the charging strategy based on the real-time temperature data and risk assessment results of the receiving device to ensure that the charging process is both efficient and safe.

[0035] In a specific embodiment, the normal gear switching charging strategy includes a high gear charging strategy, a middle gear charging strategy and a low gear charging strategy. When the charging strategy is the normal gear switching charging strategy, the corresponding charging power gear in the transmitting end device is selected based on the charging status data, including: When the charging strategy is a normal gear-switching charging strategy, a real-time charging demand analysis is performed on the receiving-end device based on the charging status data through a preset charging strategy model to obtain a charging demand index; Calculating a magnitude relationship between the charging demand index and a preset charging demand index range, and selecting a corresponding charging power gear in the transmitting end device according to the magnitude relationship, wherein selecting a corresponding charging power gear in the transmitting end device according to the magnitude relationship includes: When the magnitude relationship is that the charging demand index is higher than the preset charging demand index range, the charging power gear corresponding to the high-speed charging strategy is adopted; When the magnitude relationship is that the charging demand index is within the preset charging demand index range, the charging power gear corresponding to the mid-gear charging strategy is adopted; When the magnitude relationship is that the charging demand index is lower than a preset charging demand index range, the charging power gear corresponding to the low-gear charging strategy is adopted.

[0036] Specifically, the normal gear switching charging strategy includes a high-gear charging strategy, a medium-gear charging strategy and a low-gear charging strategy. When the charging strategy is a normal gear switching charging strategy, the corresponding charging power gear in the transmitting device is selected based on the charging status data. This process ensures that the wireless charging system can dynamically adjust the charging power according to the real-time charging demand of the receiving device, thereby achieving efficient and safe charging. Specifically, this process involves multiple steps: real-time charging demand analysis, charging demand index calculation, size relationship judgment and charging power gear selection. First, when the charging strategy is a normal gear switching charging strategy, the receiving device is analyzed for real-time charging demand based on the charging status data through a preset charging strategy model to obtain a charging demand index. The charging strategy model is a pre-designed algorithm model that can calculate an index reflecting the current charging demand of the device, i.e., the charging demand index, based on the charging status data such as the current power and charging speed of the receiving device. The higher the index, the greater the current charging demand of the device, and the higher the charging power required; conversely, the lower the index, the smaller the charging demand, and the charging power can be appropriately reduced. For example, in the application scenario of wireless charging of smart phones, assuming that the current power of the mobile phone is 30% and the charging speed is 0.5% per second, a charging demand index can be obtained through the calculation of the charging strategy model, assuming it is 0.7. This index reflects the degree of charging demand of the mobile phone in the current charging state. Next, the size relationship between the charging demand index and the preset charging demand index range is calculated, and the corresponding charging power gear in the transmitting end device is selected according to the size relationship. The preset charging demand index range is usually a fixed interval used to divide different charging demand levels. For example, it is assumed that the preset charging demand index range is 0.3 to 0.7. According to the size relationship between the charging demand index and this range, different charging power gears can be selected. Specifically, when the size relationship is that the charging demand index is higher than the preset charging demand index range, the charging power gear corresponding to the high-level charging strategy is adopted. This means that if the charging demand index exceeds the preset upper limit value, it indicates that the current charging demand of the device is very high and a higher charging power needs to be provided. For example, assuming that the charging demand index is 0.8, which exceeds the preset upper limit of 0.7, the transmitter device will select a high-level charging strategy, such as a 15W charging power level, to quickly increase the power of the device. When the size relationship is that the charging demand index is within the preset charging demand index range, the charging power level corresponding to the mid-level charging strategy is adopted. This means that if the charging demand index falls within the preset range, it indicates that the current charging demand of the device is moderate and a medium charging power can be provided.For example, assuming that the charging demand index is 0.5, which falls within the preset range of 0.3 to 0.7, the transmitter device will select a mid-range charging strategy, such as a 10W charging power gear, to balance the charging speed and battery health. When the size relationship is that the charging demand index is lower than the preset charging demand index range, the charging power gear corresponding to the low-range charging strategy is adopted. This means that if the charging demand index is lower than the preset lower limit, it indicates that the current charging demand of the device is low, and the charging power can be appropriately reduced. For example, assuming that the charging demand index is 0.2, which is lower than the preset lower limit of 0.3, the transmitter device will select a low-range charging strategy, such as a 5W charging power gear, to avoid overcharging and protect the battery. Through the above steps, the wireless charging system can dynamically adjust the charging power according to the real-time charging status of the receiving device to ensure that the charging process is both efficient and safe. This intelligent charging management method not only improves the overall performance of the charging system, but also greatly improves the user experience, making wireless charging more intelligent, efficient and safe. For example, in the application scenario of wireless charging of smartphones, assuming that when the phone starts charging, the current power is 20%, the charging speed is 0.6% per second, and the charging demand index calculated by the charging strategy model is 0.8. Since this index is higher than the preset charging demand index range of 0.3 to 0.7, the transmitter device will select a high-level charging strategy, such as a 15W charging power level, to quickly increase the power of the phone. As the charging process progresses, the power of the phone gradually increases. Assuming that after 10 minutes of charging, the current power is 70%, the charging speed drops to 0.3% per second, and the charging demand index calculated by the charging strategy model is 0.5. Since this index falls within the preset charging demand index range of 0.3 to 0.7, the transmitter device will select a mid-level charging strategy, such as a 10W charging power level, to balance the charging speed and battery health. Finally, when the phone's power is close to 100%, assuming that the current power is 95%, the charging speed drops to 0.1% per second, and the charging demand index calculated by the charging strategy model is 0.2. Since this index is lower than the preset charging demand index range of 0.3 to 0.7, the transmitter device will choose a low-level charging strategy, such as a 5W charging power level, to avoid overcharging and protect the battery. In this way, the wireless charging system can dynamically adjust the charging power according to the real-time charging status of the receiving device to ensure that the charging process is both efficient and safe.

[0037] In a specific embodiment, a charging coil and a resonant circuit are provided in the transmitting end device, and the output power of the transmitting end device is adjusted based on the charging power level to obtain an adaptive output power, including: By using a preset power mapping algorithm, power mapping conversion is performed on the transmitting end device based on the charging power gear to obtain an initial power parameter; Performing impedance matching adjustment on the resonant circuit based on the initial power parameter to obtain matching resonance parameters; Dynamically optimizing the matching resonance parameters to obtain an optimized operating frequency; Modulating the magnetic field intensity of the transmitting end device based on the optimized operating frequency to obtain a modulated magnetic field distribution; Performing homogenization processing on the modulated magnetic field distribution to obtain a uniform magnetic field; The coil current of the charging coil is closed-loop controlled based on the uniform magnetic field to obtain the adaptive output power.

[0038] Specifically, first, the transmitter device is power mapped and converted based on the charging power level by a preset power mapping algorithm to obtain initial power parameters. The power mapping algorithm is an algorithm that converts the charging power level into specific power parameters, including voltage, current, etc. Through the power mapping algorithm, the transmitter device can convert the selected charging power level (such as 5W, 10W, 15W) into specific initial power parameters, which will be used for subsequent power adjustment. For example, in the application scenario of wireless charging of smartphones, assuming that the transmitter device selects a charging power level of 10W, the power mapping algorithm can convert 10W into specific voltage and current values, assuming 12V and 0.83A. These initial power parameters will serve as the basis for subsequent adjustments. Next, the resonant circuit is impedance matched and adjusted based on the initial power parameters to obtain matching resonant parameters. The resonant circuit is an important component of the wireless charging system. It adjusts the impedance of the circuit to match the frequencies of the transmitter and the receiver, thereby improving the energy transmission efficiency. Impedance matching adjustment refers to adjusting the inductance and capacitance values ​​of the resonant circuit according to the initial power parameters to achieve the best matching state. For example, assuming that the initial power parameters are 12V and 0.83A, by adjusting the inductance and capacitance values ​​of the resonant circuit, the resonant frequency can be matched with the resonant frequency of the receiving device, thereby ensuring the maximum efficiency of energy transmission. The adjusted parameters are called matching resonance parameters, which will be used for subsequent dynamic optimization. Then, the matching resonance parameters are dynamically optimized to obtain the optimized operating frequency. Dynamic optimization refers to further adjusting the operating frequency of the resonant circuit according to the matching resonance parameters to ensure that the transmitter and the receiver always maintain the best resonant state during the actual charging process. Dynamic optimization can be achieved through a feedback control algorithm, which monitors the working state of the resonant circuit in real time and adjusts the operating frequency as needed. For example, assuming that the matching resonance parameters are 12V, 0.83A and a resonant frequency of 100kHz, the resonant frequency can be adjusted in real time through a dynamic optimization algorithm to ensure that the best resonant state is always maintained during the charging process, thereby improving the energy transmission efficiency. Next, the magnetic field intensity of the transmitter device is modulated based on the optimized operating frequency to obtain a modulated magnetic field distribution. Magnetic field intensity modulation refers to adjusting the magnetic field intensity of the transmitter device according to the optimized operating frequency to ensure the stability and efficiency of energy transmission. By adjusting the magnetic field strength, the distribution of the magnetic field can be optimized to make it more concentrated and uniform. For example, assuming that the optimized operating frequency is 100kHz, the magnetic field strength of the transmitting device can be adjusted by magnetic field strength modulation to form a stable magnetic field distribution in the charging area, thereby improving the efficiency and stability of energy transmission. Then, the modulated magnetic field distribution is homogenized to obtain a uniform magnetic field. Homogenization refers to making the modulated magnetic field distribution more uniform through specific algorithms or physical means to avoid the situation where the local magnetic field is too strong or too weak.The formation of a uniform magnetic field can improve the uniformity and efficiency of energy transmission and reduce energy loss during charging. For example, assuming that the modulated magnetic field distribution is strong in some areas and weak in other areas, the magnetic field strength of the entire charging area can be made consistent through homogenization, thereby ensuring the uniformity and efficiency of energy transmission. Finally, the coil current closed-loop control is performed on the charging coil based on the uniform magnetic field to obtain the adaptive output power. Coil current closed-loop control refers to real-time monitoring of the current of the charging coil through a feedback control system, and adjusting the current value as needed to ensure the stability and accuracy of the output power. Through coil current closed-loop control, the current of the charging coil can be accurately adjusted so that the output power matches the selected charging power level, thereby achieving adaptive output power. For example, assuming that a uniform magnetic field has been formed, through coil current closed-loop control, the current of the charging coil can be monitored in real time, and the current value can be adjusted as needed to ensure that the output power is always maintained at a level of 10W, thereby achieving efficient and safe charging. Through the above steps, the wireless charging system can accurately adjust the output power according to the selected charging power level to ensure that the charging process is both efficient and safe. This intelligent power adjustment mechanism not only improves the overall performance of the charging system, but also greatly enhances the user experience, making wireless charging more intelligent, efficient and safe. For example, in the application scenario of wireless charging of smartphones, assuming that the mobile phone needs to be charged at a power of 10W, the transmitter device first converts 10W into the initial power parameters of 12V and 0.83A through the power mapping algorithm. Then, through impedance matching adjustment, the inductance and capacitance values ​​of the resonant circuit are adjusted to the best matching state, and the matching resonant parameters are obtained. Next, the operating frequency of the resonant circuit is adjusted through the dynamic optimization algorithm to ensure that it is always maintained in the best resonant state during the charging process. Subsequently, the magnetic field strength of the transmitter device is adjusted through magnetic field intensity modulation to form a stable magnetic field distribution. By homogenizing the modulated magnetic field distribution, the magnetic field strength of the entire charging area is made consistent. Finally, through the coil current closed-loop control, the current of the charging coil is monitored in real time, and the current value is adjusted to ensure that the output power is always maintained at the level of 10W, thereby achieving efficient and safe charging. In this way, the wireless charging system can dynamically adjust the output power according to the real-time charging needs of the receiving device to ensure that the charging process is both efficient and safe. This intelligent power adjustment mechanism not only improves the overall performance of the charging system, but also greatly enhances the user experience, making wireless charging more intelligent, efficient and safe.

[0039] In a specific embodiment, performing closed-loop control on the coil current of the charging coil based on the uniform magnetic field to obtain the adaptive output power includes: The uniform magnetic field is measured in real time by a preset magnetic sensor to obtain the real-time magnetic field strength; The real-time magnetic field strength is converted into an expected value of the coil current of the corresponding charging coil through a preset magnetic-electric conversion model; Adjusting the initial current of the charging coil based on the expected value of the coil current to obtain a preliminary adjusted current; Performing spectrum characteristic analysis on the preliminary adjustment current to obtain spectrum characteristic data; Eliminating harmonic components of the preliminary adjustment current based on the frequency spectrum characteristic data to obtain a coil current after harmonic elimination; Obtaining the adaptive output power based on the coil current after harmonic elimination and the real-time magnetic field strength; The adaptive output power is calculated using the following formula: Among them, the represents the adaptive output power, Represents the coil current after harmonic elimination, over time change Indicates the real-time magnetic field strength over time change represents the complex exponential function, is the angular frequency corresponding to the operating frequency of the transmitting device, which is ,in is the operating frequency, It refers to a complete working cycle time, that is, .

[0040] Specifically, first, the uniform magnetic field is measured in real time by a preset magnetic sensor to obtain the real-time magnetic field strength. A magnetic sensor is a device that can detect the strength of a magnetic field and can monitor the magnetic field strength generated by a transmitting device in real time. These magnetic sensors are usually distributed around the charging coil to ensure that the magnetic field strength of the entire charging area can be accurately measured. For example, in the application scenario of wireless charging of a smartphone, assuming that the transmitting device has formed a uniform magnetic field through the previous steps, the magnetic sensor will measure the strength of this uniform magnetic field in real time and transmit the data to the control unit. Assume that at a certain moment, the real-time magnetic field strength measured by the magnetic sensor is 0.5 Tesla. Next, the real-time magnetic field strength is converted into the expected value of the coil current of the corresponding charging coil through a preset magnetic-electric conversion model. The magnetic-electric conversion model is a mathematical model that can calculate the required coil current value based on the magnetic field strength. Through this model, the real-time measured magnetic field strength can be converted into the expected value of the current of the charging coil, thereby guiding the subsequent current regulation. For example, assuming that the formula of the magnetic-electric conversion model is ,in is the expected value of the coil current, is the real-time magnetic field strength, and k is a constant. If k = 2 A / Tesla and the real-time magnetic field strength B = 0.5 Tesla, then the expected value of the coil current is Ampere. Then, the initial current of the charging coil is adjusted based on the expected value of the coil current to obtain a preliminary adjusted current. The initial current refers to the current value of the charging coil before adjustment. The initial current can be adjusted to the expected value of the coil current by adjusting the circuit. For example, assuming that the initial current is 0.8 ampere and the expected value of the coil current is 1 ampere, the initial current can be adjusted from 0.8 ampere to 1 ampere by adjusting the circuit to obtain a preliminary adjusted current. Next, the preliminary adjusted current is subjected to a spectrum characteristic analysis to obtain spectrum characteristic data. Spectral characteristic analysis refers to converting the time domain signal of the preliminary adjusted current into a frequency domain signal by Fourier transform and other methods, thereby analyzing its spectrum characteristics. This step can help identify the harmonic components in the preliminary adjusted current and provide a basis for subsequent harmonic elimination. For example, assuming that the time domain signal of the preliminary adjusted current is i(t), its spectrum characteristic data I(f) can be obtained by Fourier transform, where f represents frequency. Then, the preliminary adjusted current is subjected to harmonic component elimination based on the spectrum characteristic data to obtain the coil current after harmonic elimination. Harmonic component elimination refers to removing the harmonic components in the preliminary adjustment current through filters and other methods to make the current purer. For example, assuming that the spectrum characteristic data shows that there are obvious 50Hz harmonic components in the preliminary adjustment current, these harmonic components can be filtered out through a low-pass filter or other filters to obtain the coil current after harmonic elimination. Finally, the adaptive output power is obtained based on the coil current after harmonic elimination and the real-time magnetic field strength. The adaptive output power refers to the final output power calculated based on the coil current and the real-time magnetic field strength after harmonic elimination. The specific calculation formula is as follows: The following formula is used to calculate the adaptive output power; Among them, the represents the adaptive output power, Represents the coil current after harmonic elimination, over time change Indicates the real-time magnetic field strength over time change represents the complex exponential function, is the angular frequency corresponding to the operating frequency of the transmitting device, which is ,in is the operating frequency, It refers to a complete working cycle time, that is, .

[0041] For example, assuming the coil current after harmonic elimination is, is 1 ampere, the real-time magnetic field strength B(t) is 0.5 Tesla, and the operating frequency f is 100 kHz, then the angular frequency = 100 × 10 3 Radians / second, a complete working cycle time T = ==10μs. Through the above formula, the adaptive output power can be calculated . Through the above steps, the wireless charging system can accurately adjust the current of the charging coil according to the real-time measurement results of the uniform magnetic field, thereby achieving efficient and safe charging. This intelligent current control mechanism not only improves the overall performance of the charging system, but also greatly improves the user experience, making wireless charging more intelligent, efficient and safe. For example, in the application scenario of wireless charging of smartphones, assuming that the mobile phone needs to be charged at a power of 10W, the transmitting device first measures the strength of the uniform magnetic field in real time through a magnetic sensor, assuming it is 0.5 Tesla. Then, through the magneto-electric conversion model, the real-time magnetic field strength is converted into the expected value of the coil current, assuming it is 1 ampere. Next, through the adjustment circuit, the initial current is adjusted from 0.8 amperes to 1 ampere to obtain the preliminary adjustment current. By analyzing the spectral characteristics of the preliminary adjustment current, it is found that there are 50Hz harmonic components. These harmonic components are filtered out by a low-pass filter to obtain the coil current after harmonic elimination. Finally, based on the coil current and real-time magnetic field strength after harmonic elimination, the adaptive output power is calculated by the above formula to ensure that the output power is always maintained at the level of 10W, thereby achieving efficient and safe charging. In this way, the wireless charging system can dynamically adjust the output power according to the real-time charging needs of the receiving device to ensure that the charging process is both efficient and safe. This intelligent power adjustment mechanism not only improves the overall performance of the charging system, but also greatly enhances the user experience, making wireless charging more intelligent, efficient and safe.

[0042] The wireless charging power adjustment method in the embodiment of the present invention is described above. The wireless charging power adjustment system in the embodiment of the present invention is described below. Figure 2 , an embodiment of the wireless charging power regulation system in the embodiment of the present invention includes: The collecting module 21 is used to continuously collect the charging status information of the receiving device to obtain the charging status data of the receiving device; wherein the charging status data includes the current power and the current charging speed; The acquisition module 22 is used to acquire temperature data of the receiving end device through a preset temperature sensor to obtain acquired temperature data, and to perform a timing risk assessment on the receiving end device based on the acquired temperature data to obtain a timing risk assessment value; The division module 23 is used to input the timing risk assessment value into the division interval preset in the transmitting end device to obtain the charging strategy corresponding to the division interval; wherein the charging strategy includes a stop charging strategy, a low-gear short-time charging strategy and a normal gear switching charging strategy; A selection module 24, configured to select a corresponding charging power gear in the transmitting end device based on the charging status data when the charging strategy is a normal gear switching charging strategy; The adjustment module 25 is used to adjust the output power of the transmitting end device based on the charging power gear to obtain an adaptive output power, and safely charge the receiving end device based on the adaptive output power.

[0043] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to the description in the above method embodiment, which will not be repeated here.

[0044] Reference Figure 3 The present invention also provides a computer device in an embodiment, wherein the internal structure of the computer device can be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0045] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0046] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0047] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0048] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0049] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A wireless charging power adjustment method, characterized in that: The following steps are involved: Continuously collect charging status information of the receiving device to obtain charging status data of the receiving device; wherein the charging status data includes current power and current charging speed; Collecting temperature data of the receiving end device through a preset temperature sensor to obtain collected temperature data, and performing a timing risk assessment on the receiving end device based on the collected temperature data to obtain a timing risk assessment value; Input the timing risk assessment value into the preset division interval in the transmitting end device to obtain the charging strategy corresponding to the division interval; wherein the charging strategy includes a stop charging strategy, a low-gear short-time charging strategy and a normal gear switching charging strategy; When the charging strategy is a normal gear switching charging strategy, selecting a corresponding charging power gear in the transmitting end device based on the charging status data; The output power of the transmitting device is adjusted based on the charging power gear to obtain an adaptive output power, and the receiving device is safely charged based on the adaptive output power.

2. The wireless charging power adjustment method according to claim 1, characterized in that: The continuously collecting the charging status information of the receiving device to obtain the charging status data of the receiving device includes: The preset power monitoring module samples the battery power of the receiving device in real time to obtain the current power data; Based on the current power data, the charging speed of the receiving device is calculated to obtain the current charging speed.

3. The wireless charging power adjustment method according to claim 1, characterized in that: The collected temperature data is a surface temperature distribution matrix, the temperature sensor is a multi-point thermocouple array sensor, the temperature data of the receiving end device is collected by a preset temperature sensor to obtain the collected temperature data, and the timing risk assessment of the receiving end device is performed based on the collected temperature data to obtain a timing risk assessment value, including: The temperature of the surface of the receiving end device is scanned by the multi-point thermocouple array sensor to obtain a surface temperature distribution matrix; wherein the rows of the surface temperature distribution matrix represent the coordinates of the receiving end device in the Y-axis direction, the columns of the surface temperature distribution matrix represent the coordinates in the X-axis direction, and the elements in the surface temperature distribution matrix represent temperature values; Reconstructing a thermal map of the receiving device based on the surface temperature distribution matrix to obtain a three-dimensional thermal distribution model; Eliminating noise from the three-dimensional heat distribution model to obtain an optimized heat distribution model; Perform hotspot identification on the receiving-end device based on the optimized thermal distribution model to obtain a hotspot area coordinate set; The temperature of the hot spot area coordinate set is predicted by a preset long short-term memory network to obtain a temperature change trend; Based on the temperature change trend, a timing thermal runaway risk assessment is performed on the receiving end device to obtain a timing risk assessment value.

4. The wireless charging power adjustment method according to claim 1, characterized in that: The charging strategy includes a stop charging strategy, a low gear short-time charging strategy and a normal gear switching charging strategy. The timing risk assessment value is input into a preset division interval in the transmitting end device to obtain a charging strategy corresponding to the division interval, including: Using a preset hyperbolic tangent transformation function to perform a nonlinear transformation on the time series risk assessment value to obtain a time series transformation risk index; A time series risk curve is constructed based on the time series transformation risk index, and the time series risk curve is mapped into a preset coordinate system to obtain a time series risk curve with a coordinate system; wherein the ordinate of the coordinate system represents the time series transformation risk index, and the abscissa of the coordinate system represents time; Monitor the point coordinates in the time series risk curve in the coordinate system through a preset Kalman filter algorithm to obtain the ordinate value corresponding to the point coordinates; When the ordinate value is within a first interval of the divided intervals, a charging stop strategy corresponding to the first interval is obtained; When the ordinate value is within the second interval of the divided intervals, a low-gear short-time charging strategy corresponding to the second interval is obtained; When the ordinate value is within a third interval in the divided intervals, a normal gear-switching charging strategy corresponding to the third interval is obtained.

5. The wireless charging power adjustment method according to claim 4, characterized in that: The normal gear switching charging strategy includes a high gear charging strategy, a middle gear charging strategy and a low gear charging strategy. When the charging strategy is the normal gear switching charging strategy, the corresponding charging power gear in the transmitting end device is selected based on the charging status data, including: When the charging strategy is a normal gear-switching charging strategy, a real-time charging demand analysis is performed on the receiving-end device based on the charging status data through a preset charging strategy model to obtain a charging demand index; Calculating a magnitude relationship between the charging demand index and a preset charging demand index range, and selecting a corresponding charging power gear in the transmitting end device according to the magnitude relationship, wherein selecting a corresponding charging power gear in the transmitting end device according to the magnitude relationship includes: When the magnitude relationship is that the charging demand index is higher than the preset charging demand index range, the charging power gear corresponding to the high-speed charging strategy is adopted; When the magnitude relationship is that the charging demand index is within the preset charging demand index range, the charging power gear corresponding to the mid-gear charging strategy is adopted; When the magnitude relationship is that the charging demand index is lower than a preset charging demand index range, the charging power gear corresponding to the low-gear charging strategy is adopted.

6. The wireless charging power adjustment method according to claim 1, characterized in that: The transmitting end device is provided with a charging coil and a resonant circuit, and the output power of the transmitting end device is adjusted based on the charging power level to obtain an adaptive output power, including: By using a preset power mapping algorithm, power mapping conversion is performed on the transmitting end device based on the charging power gear to obtain an initial power parameter; Performing impedance matching adjustment on the resonant circuit based on the initial power parameter to obtain matching resonance parameters; Dynamically optimizing the matching resonance parameters to obtain an optimized operating frequency; Modulating the magnetic field intensity of the transmitting end device based on the optimized operating frequency to obtain a modulated magnetic field distribution; Performing homogenization processing on the modulated magnetic field distribution to obtain a uniform magnetic field; The coil current of the charging coil is closed-loop controlled based on the uniform magnetic field to obtain the adaptive output power.

7. The wireless charging power adjustment method according to claim 6, characterized in that: The performing closed-loop control of the coil current of the charging coil based on the uniform magnetic field to obtain the adaptive output power includes: The uniform magnetic field is measured in real time by a preset magnetic sensor to obtain the real-time magnetic field strength; The real-time magnetic field strength is converted into an expected value of the coil current of the corresponding charging coil through a preset magnetic-electric conversion model; Adjusting the initial current of the charging coil based on the expected value of the coil current to obtain a preliminary adjusted current; Performing spectrum characteristic analysis on the preliminary adjustment current to obtain spectrum characteristic data; Eliminating harmonic components of the preliminary adjustment current based on the frequency spectrum characteristic data to obtain a coil current after harmonic elimination; Obtaining the adaptive output power based on the coil current after harmonic elimination and the real-time magnetic field strength; The adaptive output power is calculated using the following formula: Among them, the represents the adaptive output power, Represents the coil current after harmonic elimination, over time change Indicates the real-time magnetic field strength over time change represents the complex exponential function, is the angular frequency corresponding to the operating frequency of the transmitting device, which is ,in is the operating frequency, It refers to a complete working cycle time, that is, .

8. A wireless charging power adjustment device, characterized in that: include: A collection module, used to continuously collect charging status information of the receiving device to obtain charging status data of the receiving device; wherein the charging status data includes the current power and the current charging speed; A collection module, used to collect temperature data of the receiving end device through a preset temperature sensor to obtain collected temperature data, and to perform a timing risk assessment on the receiving end device based on the collected temperature data to obtain a timing risk assessment value; A division module, used to input the timing risk assessment value into a division interval preset in the transmitting end device to obtain a charging strategy corresponding to the division interval; wherein the charging strategy includes a stop charging strategy, a low-gear short-time charging strategy, and a normal gear switching charging strategy; A selection module, configured to select a corresponding charging power gear in the transmitting end device based on the charging status data when the charging strategy is a normal gear switching charging strategy; The adjustment module is used to adjust the output power of the transmitting end device based on the charging power gear to obtain an adaptive output power, and safely charge the receiving end device based on the adaptive output power.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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