Wireless Charging Power Adjustment Method, Device, Equipment and Storage Medium

Abstract

The present invention relates to a method, device, equipment and storage medium for adjusting wireless charging power, including the following steps: continuously collecting charging status information of a receiving-end device to obtain charging status data of the receiving-end device; collecting temperature data of the receiving-end device through a preset temperature sensor to obtain the 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; selecting a corresponding charging power level in the transmitting-end device based on the charging status data; and adjusting the output power of the transmitting-end device based on the charging power level to obtain an adaptive output power, which solves the technical problem that traditional wireless charging systems usually adopt a fixed charging power and cannot dynamically adjust according to the actual needs and environmental conditions of the receiving-end device.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless charging, and particularly to a method, device, equipment and storage medium for adjusting wireless charging power. Background Art

[0002] With the popularization of mobile devices and the development of technology, wireless charging technology has received increasing attention due to its convenience and flexibility. However, in practical applications, some problems in the wireless charging process have gradually emerged, especially those related to charging efficiency and safety. For example, different devices have different requirements for charging power, and existing wireless charging technologies often struggle to achieve precise power matching, resulting in low charging efficiency or overcharging. In addition, thermal management during wireless charging is also an issue that cannot be ignored, as the heat generated during charging may damage the device and even pose a safety hazard. In the practical application of wireless charging technology, how to effectively monitor and control the charging process has become an urgent technical problem. Traditional wireless charging systems usually use a fixed charging power and cannot dynamically adjust according to the actual needs of the receiving device and environmental conditions, which not only limits the improvement of charging efficiency but also affects the user experience. Especially in the case of multiple devices charging simultaneously, 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 like the current battery level and charging speed, but also needs to combine temperature data to evaluate the safety risks during device charging. Through the analysis of these comprehensive data, the most suitable charging strategy can be formulated for different charging scenarios, thus ensuring both charging safety and improving charging efficiency, meeting the user's demand for fast, efficient and safe wireless charging. Such technological progress is of great significance for promoting the widespread application of wireless charging technology. Summary of the Invention

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

[0005] To achieve the above object, the present invention provides a method for adjusting wireless charging power, including the following steps:

[0006] Continuously collect the charging status information of the receiving-end device to obtain the charging status data of the receiving-end device; wherein, the charging status data includes the current battery level and the current charging speed;

[0007] Collect temperature data of the receiving-end device through a preset temperature sensor to obtain the collected temperature data, and perform a timing risk assessment on the receiving-end device based on the collected temperature data to obtain a timing risk assessment value;

[0008] Input the timing risk assessment value into a preset division interval 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;

[0009] When the charging strategy is the normal gear-switching charging strategy, select the corresponding charging power gear in the transmitting-end device based on the charging status data;

[0010] Adjust the output power of the transmitting-end device based on the charging power gear to obtain an adaptive output power, and perform safe charging on the receiving-end device based on the adaptive output power.

[0011] Further, the continuously collecting the charging status information of the receiving-end device to obtain the charging status data of the receiving-end device includes:

[0012] Perform real-time battery level sampling on the battery of the receiving-end device through a preset battery level monitoring module to obtain the current battery level data;

[0013] Calculate the charging speed of the receiving-end device based on the current battery level data to obtain the current charging speed.

[0014] Further, the collected temperature data is a surface temperature distribution matrix, and the temperature sensor is a multi-point thermocouple array sensor. Collecting the temperature data of the receiving-end device through the preset temperature sensor to obtain the 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 includes:

[0015] Perform a temperature scan on the surface of the receiving-end device through 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 in the Y-axis direction of the receiving-end device, 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;

[0016] Reconstruct a thermal map of the receiving-end device based on the surface temperature distribution matrix to obtain a three-dimensional thermal distribution model;

[0017] Noise elimination is performed on the three-dimensional heat distribution model to obtain an optimized heat distribution model;

[0018] Based on the optimized heat distribution model, hotspot identification is performed on the receiving-end device to obtain a set of hotspot area coordinates;

[0019] Temperature prediction is performed on the set of hotspot area coordinates through a preset long short-term memory network to obtain a temperature change trend;

[0020] 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.

[0021] Further, the charging strategy includes a stop charging strategy, a low-gear short-time charging strategy, and a normal gear-switching charging strategy. Inputting the timing risk assessment value into a preset division interval in the transmitting-end device to obtain the charging strategy corresponding to the division interval includes:

[0022] A preset hyperbolic tangent transformation function is used to perform a non-linear transformation on the timing risk assessment value to obtain a timing transformation risk index;

[0023] Based on the timing transformation risk index, a timing risk curve is constructed, and the timing risk curve is mapped into a preset coordinate system to obtain a timing risk curve with a coordinate system; wherein, the ordinate of the coordinate system represents the timing transformation risk index, and the abscissa of the coordinate system represents time;

[0024] The point coordinates in the timing risk curve in the coordinate system are monitored through a preset Kalman filtering algorithm to obtain the ordinate value corresponding to the point coordinates;

[0025] When the ordinate value is within the first interval of the division interval, the stop charging strategy corresponding to the first interval is obtained;

[0026] When the ordinate value is within the second interval of the division interval, the low-gear short-time charging strategy corresponding to the second interval is obtained;

[0027] When the ordinate value is within the third interval of the division interval, the normal gear-switching charging strategy corresponding to the third interval is obtained.

[0028] Further, 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 the normal gear-switching charging strategy, selecting the corresponding charging power gear in the transmitting-end device based on the charging state data includes:

[0029] When the charging strategy is the normal gear-switching charging strategy, through a preset charging strategy model, based on the charging status data, the real-time charging demand of the receiving device is analyzed to obtain a charging demand index;

[0030] Calculate the size relationship between the charging demand index and a preset charging demand index range, and select the corresponding charging power gear in the transmitting device according to the size relationship. Among them, selecting the corresponding charging power gear in the transmitting device according to the size relationship includes:

[0031] When the size relationship is that the charging demand index is higher than the preset charging demand index range, then adopt the charging power gear corresponding to the high gear charging strategy;

[0032] When the size relationship is that the charging demand index is within the preset charging demand index range, then adopt the charging power gear corresponding to the medium gear charging strategy;

[0033] When the size relationship is that the charging demand index is lower than the preset charging demand index range, then adopt the charging power gear corresponding to the low gear charging strategy.

[0034] Further, a charging coil and a resonant circuit are provided in the transmitting device. The output power of the transmitting device is adjusted based on the charging power gear to obtain an adaptive output power, including:

[0035] Through a preset power mapping algorithm, based on the charging power gear, power mapping conversion is performed on the transmitting device to obtain an initial power parameter;

[0036] Based on the initial power parameter, impedance matching adjustment is performed on the resonant circuit to obtain a matching resonant parameter;

[0037] Dynamically optimize the matching resonant parameter to obtain an optimized operating frequency;

[0038] Based on the optimized operating frequency, magnetic field intensity modulation is performed on the transmitting device to obtain a modulated magnetic field distribution;

[0039] Perform homogenization processing on the modulated magnetic field distribution to obtain a uniform magnetic field;

[0040] Based on the uniform magnetic field, closed-loop control of the coil current is performed on the charging coil to obtain the adaptive output power.

[0041] Further, the closed-loop control of the coil current is performed on the charging coil based on the uniform magnetic field to obtain the adaptive output power, including:

[0042] Through a preset magnetic sensor, the uniform magnetic field is measured in real time to obtain a real-time magnetic field intensity;

[0043] Convert the real-time magnetic field intensity into an expected value of the coil current of the charging coil through a preset magnetic-electric conversion model;

[0044] Adjust the initial current of the charging coil based on the expected value of the coil current to obtain a preliminarily adjusted current;

[0045] Conduct spectral characteristic analysis on the preliminarily adjusted current to obtain spectral characteristic data;

[0046] Eliminate the harmonic components of the preliminarily adjusted current based on the spectral characteristic data to obtain the coil current after harmonic elimination;

[0047] Obtain the adaptive output power based on the coil current after harmonic elimination and the real-time magnetic field intensity;

[0048] Calculate the adaptive output power using the following formula;

[0049]

[0050] wherein the represents the adaptive output power, represents the coil current after harmonic elimination, varying with time changing represents the real-time magnetic field intensity, varying with time changing represents the complex exponential function, is the angular frequency corresponding to the operating frequency of the transmitting-end device, which is where is the operating frequency, refers to the time of a complete operating cycle, that is .

[0051] The present invention also provides a wireless charging power adjustment device, including:

[0052] A collection module for continuously collecting charging status information of the receiving-end device to obtain charging status data of the receiving-end device; wherein, the charging status data includes the current battery level and the current charging speed;

[0053] An acquisition module for collecting temperature data of the receiving-end device through a preset temperature sensor to obtain the acquired temperature data, and performing a timing risk assessment on the receiving-end device based on the acquired temperature data to obtain a timing risk assessment value;

[0054] A partitioning module, configured to input the timing risk assessment value into a preset partitioning interval in the transmitting end device to obtain a charging strategy corresponding to the partitioning interval; wherein, the charging strategy includes a charging stop strategy, a low-gear short-time charging strategy, and a normal gear-switching charging strategy;

[0055] A selection module, configured to, when the charging strategy is a normal gear-switching charging strategy, select a corresponding charging power gear in the transmitting end device based on the charging state data;

[0056] An adjustment module, configured to adjust the output power of the transmitting end device based on the charging power gear to obtain an adaptive output power, and perform safe charging on the receiving end device based on the adaptive output power.

[0057] The present invention further provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of the method described in any one of the above are implemented.

[0058] 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 steps of the method described in any one of the above are implemented.

[0059] The wireless charging power adjustment method provided by the present invention includes the following steps: continuously collecting charging status information of the receiving-end device to obtain charging status data of the receiving-end device; wherein, the charging status data includes the current battery level and the current charging speed; collecting temperature data of the receiving-end device through a preset temperature sensor to obtain the 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 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; when the charging strategy is the normal gear-switching charging strategy, selecting a corresponding charging power gear in the transmitting-end device based on the charging status data; adjusting the output power of the transmitting-end device based on the charging power gear to obtain an adaptive output power, and performing safe charging on the receiving-end device based on the adaptive output power. Through the above technical means, the technical problem that the traditional wireless charging system usually uses a fixed charging power and cannot dynamically adjust according to the actual needs and environmental conditions of the receiving-end device is solved. The method realizes that by inputting the timing risk assessment value into the preset division interval in the transmitting-end device, personalized charging schemes can be provided for receiving-end devices of different models and brands. This means that no matter which type of mobile device the user uses, they can enjoy efficient and safe wireless charging services, enhancing the compatibility and universality of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 is a schematic diagram of the steps of the wireless charging power adjustment method in an embodiment of the present invention;

[0061] Figure 2 is a block diagram of the structure of the wireless charging power adjustment device in an embodiment of the present invention;

[0062] Figure 3 is a schematic block diagram of the structure of a computer device in an embodiment of the present invention.

[0063] The realization, functional features, and advantages of the object of the present invention will be further described in conjunction with the embodiments with reference to the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0064] In order to make the object, technical solution, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to 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 used to limit the present invention.

[0065] As Figure 1 shown, Figure 1Schematic diagram of the steps of a wireless charging power adjustment method according to an embodiment of the present invention;

[0066] An embodiment of the present invention provides a wireless charging power adjustment method, including the following steps:

[0067] Step S1, 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 battery level and the current charging speed.

[0068] Specifically, continuously collecting the charging status information of the receiving device to obtain the charging status data of the receiving device is the basis of the entire wireless charging power adjustment 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 battery level 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 adjustment. For example, in an application scenario of wireless charging of a smartphone, when the phone is placed on the wireless charging board to start charging, the charging board (i.e., the transmitting end) will query the phone (i.e., the receiving end) about its current battery percentage and charging rate at regular time intervals (such as every few seconds or dozens of seconds) through a wireless communication protocol. If the phone is in the fast charging stage, the charging board may receive relatively high charging speed data; conversely, if the phone's battery is nearly full, the charging speed will significantly decrease. Based on these real-time updated charging status data, the charging board can make more accurate power adjustment decisions to ensure that both the efficient charging purpose can be achieved throughout the charging process and the safety of the device can be guaranteed, avoiding overcharging or undercharging.

[0069] Step S2, collect temperature data of the receiving device through a preset temperature sensor to obtain the collected temperature data, and perform a timing risk assessment on the receiving device based on the collected temperature data to obtain a timing risk assessment value.

[0070] Specifically, temperature data of the receiving-end device is collected through a preset temperature sensor to obtain the collected temperature data, and a timing risk assessment is performed on the receiving-end device based on the collected temperature data to obtain a timing risk assessment value, which is a key step to ensure the safety of the device during wireless charging. In actual operation, the temperature sensor will be pre-installed at key parts of the receiving-end device, such as near the battery or around the charging coil, which are usually the places where the device is most likely to generate heat during charging. After the receiving-end device starts charging, the temperature sensor will continuously monitor the temperature changes in these areas and transmit the collected temperature data to the transmitting-end device in real time through a wireless communication protocol. After receiving these temperature data, the transmitting-end device will immediately start the built-in risk assessment algorithm, which will calculate a timing risk assessment value according to the historical temperature data and the current temperature change trend. This assessment value reflects the likelihood of the device overheating under the current charging state. For example, in the application scenario of wireless charging of a smartphone, if the temperature of the phone suddenly rises at a certain moment during charging and exceeds the preset safety threshold, then the transmitting-end device will calculate a relatively high timing risk assessment value. Based on this assessment value, the transmitting-end device can quickly take measures, such as reducing the charging power or pausing the charging, to prevent the device from being damaged due to overheating. In this way, not only can the safety of the charging process be ensured, but also the service life of the device can be extended and the user experience can be improved.

[0071] Step S3, input the timing risk assessment value into a preset division interval 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.

[0072] Specifically, multiple division intervals are preset inside the transmitting device, and each interval 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 transmitting device receives the timing risk assessment value, it will immediately map this value into the corresponding division interval to determine the most suitable charging strategy in the current situation. For example, in the application scenario of smartphone wireless charging, assume that three division intervals are set inside the transmitting device: 0 - 50 represents the low-risk area, 51 - 80 represents the medium-risk area, and 81 - 100 represents the high-risk area. If the timing risk assessment value falls within the low-risk area, the transmitting device will select the normal gear-switching charging strategy, which means it can continue to provide an appropriate charging power according to the current charging state. If the assessment value falls within the medium-risk area, the transmitting device may choose the low-gear short-time charging strategy, that is, temporarily reduce the charging power to observe the temperature change of the device and ensure that the device will not be damaged due to long-term high temperature. If the assessment value reaches the high-risk area, the transmitting device will immediately execute the stop charging strategy, completely interrupt the charging process, and resume charging until the device temperature drops to the safe range. In this way, the transmitting device can dynamically adjust the charging strategy according to real-time temperature data and risk assessment results, not only ensuring the safety of charging, but also maximizing the charging efficiency and meeting the user's needs for fast and stable charging.

[0073] Step S4, when the charging strategy is the normal gear-switching charging strategy, select the corresponding charging power gear inside the transmitting device based on the charging state data.

[0074] Specifically, when the transmitting device determines that the normal switching gear charging strategy should be adopted currently based on the timing risk assessment value, it will further refer to the charging status data of the receiving device, including the current battery level and the current charging speed, to determine the most suitable charging power gear. This is because different charging statuses correspond to different charging requirements. A reasonable selection of the power gear can maximize the charging efficiency and avoid overcharging or undercharging. For example, in the application scenario of wireless charging for smartphones, assume that the transmitting device supports multiple charging power gears, such as 5W, 10W, 15W, etc. When the phone starts charging, the battery level is low and the charging speed is slow. At this time, the transmitting device may choose a high power gear of 15W to quickly increase the phone's battery level. As the charging process progresses, the phone's battery level gradually increases and the charging speed also changes. When the phone's battery level reaches about 70%, the charging speed may slow down. At this time, the transmitting device may switch to a medium power gear of 10W to balance the charging speed and battery health. Finally, when the phone's battery level is close to 100%, to prevent overcharging and protect the battery, the transmitting device will adjust to a low power gear of 5W again to ensure a smooth end to the charging process. In this way, the transmitting device can dynamically select the most suitable charging power gear according to the real-time charging status of the receiving device, not only improving the charging efficiency but also ensuring the safety and stability of the charging process. This intelligent charging management method greatly enhances the user experience and makes wireless charging more intelligent and reliable.

[0075] Step S5: Based on the charging power gear, adjust the output power of the transmitting device to obtain an adaptive output power, and perform safe charging on the receiving device based on the adaptive output power.

[0076] Specifically, when the transmitting device selects an appropriate charging power level based on the charging status data and timing risk assessment value of the receiving device, it precisely adjusts its output power according to the selected power level to generate an adaptive output power. This process involves the power control module inside the transmitting device, which adjusts the parameters of its output circuit according to the selected power level to ensure that the output electrical energy meets the current charging requirements. For example, in the application scenario of wireless charging of a smartphone, assume that the transmitting device has selected a medium power level of 10W based on the charging status and temperature data of the phone. Next, the power control module of the transmitting device will adjust its output circuit and precisely set the output power to 10W. In this way, the transmitting device can stably supply power to the phone at a power of 10W, ensuring that the charging process is both efficient and safe. If during the charging process, the temperature of the phone suddenly rises and the timing risk assessment value enters the medium risk area, the transmitting device may re-select 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 generation and ensure that the phone will not be damaged due to overheating. In this way, the transmitting device can dynamically adjust the output power according to the real-time charging status and temperature data of the receiving device to generate an adaptive output power, thereby ensuring the safety of the charging process while guaranteeing the charging efficiency. 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.

[0077] 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:

[0078] Performing real-time power sampling on the battery of the receiving device through a preset power monitoring module to obtain the current power data;

[0079] Calculating the charging speed of the receiving device based on the current power data to obtain the current charging speed.

[0080] Specifically, first, the battery of the receiving device is sampled for real-time power 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 battery percentage and store this data in the memory of the device. For example, in the application scenario of wireless charging of a smartphone, when the phone is placed on the wireless charging pad to start charging, the power monitoring module inside the phone will continuously read the current power of the battery and transmit this data to the charging pad (i.e., the transmitting device) in real time through a wireless communication protocol. In this way, the transmitting device can always understand the power change of the phone and provide accurate data support for subsequent power adjustment. 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), then calculate the power difference between these two samplings, and divide it by the time interval to obtain the average charging speed during this period. For example, assume that during a charging process, the power monitoring module of the phone measures the power as 20% at the 10th second and 22% at the 20th second. Then, within these 10 seconds, the charging speed of the phone is (22% - 20%) / 10 seconds = 0.2% per second. In this way, the transmitting 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 jointly constitute the continuous collection process of the charging status information. Through real-time power sampling and charging speed calculation, the transmitting device can comprehensively master 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 a smartphone, when the phone starts charging, the power monitoring module will frequently read the power of the battery and calculate a relatively high charging speed. The transmitting device will select a relatively high charging power gear based on this data to quickly increase the power of the phone. As the charging process progresses and the power of the phone gradually increases, the charging speed will gradually slow down. At this time, the transmitting device will adjust the charging power in a timely manner according to the latest charging speed data to ensure that the charging process is both efficient and safe.

[0081] In a specific embodiment, the collected temperature data is a surface temperature distribution matrix, and the temperature sensor is a multi-point thermocouple array sensor. The temperature data of the receiving device is collected through a preset temperature sensor to obtain the collected temperature data, and based on the collected temperature data, a timing risk assessment of the receiving device is performed to obtain a timing risk assessment value, including:

[0082] The surface of the receiving-end device is scanned for temperature 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 in the Y-axis direction of the receiving-end device, 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;

[0083] Based on the surface temperature distribution matrix, the receiving-end device is reconstructed for a thermal map to obtain a three-dimensional thermal distribution model;

[0084] Noise is eliminated from the three-dimensional thermal distribution model to obtain an optimized thermal distribution model;

[0085] Based on the optimized thermal distribution model, hot spots are identified for the receiving-end device to obtain a set of hot spot area coordinates;

[0086] The temperature change trend is obtained by predicting the temperature of the set of hot spot area coordinates through a preset long short-term memory network;

[0087] Based on the temperature change trend, a time-series thermal runaway risk assessment is performed on the receiving-end device to obtain a time-series risk assessment value.

[0088] Specifically, the collected temperature data is a surface temperature distribution matrix, and the temperature sensor is a multi-point thermocouple array sensor. The temperature data of the receiving-end device is collected through the preset temperature sensor to obtain the collected temperature data, and the timing risk assessment of the receiving-end device is carried out 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, thermal map reconstruction, noise elimination, hot spot identification, temperature prediction, and timing thermal runaway risk assessment. First, the surface of the receiving-end device is scanned for temperature through the multi-point thermocouple array sensor to obtain the surface temperature distribution matrix. The multi-point thermocouple array sensor is a high-precision temperature measurement device. It can arrange multiple temperature sensing points on the surface of the receiving-end device, and each sensing point will collect the temperature data of that point in real time. These temperature data are organized in a matrix form, called the surface temperature distribution matrix. In this matrix, the rows represent the coordinates in the Y-axis direction of the receiving-end device, the columns represent the coordinates in the X-axis direction, and each element in the matrix represents the temperature value of that point. For example, in the application scenario of wireless charging of a smartphone, when the phone is placed on the wireless charging board to start charging, the multi-point thermocouple array sensor will cover the entire back of the phone, and each sensing point will collect the temperature data of that point in real time and organize these data into a two-dimensional matrix. Assuming the size of the matrix is 10x10, then 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, based on the surface temperature distribution matrix, the thermal map of the receiving-end device is reconstructed 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 on the device surface. This process can be achieved through an interpolation algorithm, that is, interpolating between the 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 device surface but also provide a basis for subsequent analysis. Then, noise elimination is performed on the three-dimensional thermal distribution model 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 noise cancellation processing, the obtained optimized thermal distribution model is more accurate and can more realistically reflect the temperature distribution on the surface of the device. Then, based on the optimized thermal distribution model, hot spot identification is performed on the receiving-end device to obtain a set of hot spot area coordinates. Hot spot identification refers to finding areas with abnormally high temperatures in the optimized thermal distribution model, and these areas 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, then this area is considered a hot spot. For example, assuming that in the optimized thermal distribution model, areas with temperatures exceeding 40°C are considered hot spots, then by traversing each point in the model, all points with temperatures exceeding 40°C can be found, and the coordinates of these points are recorded to form a set of hot spot area coordinates. The coordinate information of these hot spot areas will be used for subsequent temperature prediction and risk assessment. Subsequently, the temperature change trend is obtained by predicting the temperature of the set of hot spot area coordinates through a preset long short-term memory network. The long short-term memory network (LSTM) is a special type of recurrent neural network, especially suitable for processing time series data. In this step, the LSTM network will predict the temperature change trend of the hot spot area in the next period of time based on the historical temperature data and the set of hot spot area coordinates. For example, assuming that we have collected the temperature data of the hot spot area in the past 10 minutes, the LSTM network will predict the temperature change of the hot spot area in the next 10 minutes based on this data. In this way, potential overheating risks can be discovered in advance, providing a basis for subsequent risk assessment. Finally, based on the temperature change trend, a time-series thermal runaway risk assessment is performed on the receiving-end device to obtain a time-series risk assessment value. The time-series thermal runaway risk assessment refers to evaluating the risk of the device experiencing thermal runaway in the next period of time according to the predicted temperature change trend. Specifically, a series of risk assessment indicators, such as the temperature rise rate, the maximum temperature value, etc., can be set to calculate the time-series risk assessment value. For example, if the prediction result shows that the temperature of a certain hot spot area will rise rapidly in the next 10 minutes and may exceed the safety temperature upper limit of the device, then the time-series risk assessment value of this area will be set to high risk. According to these risk assessment values, the transmitting-end 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 change of the receiving-end device in real time, discover potential overheating risks in time, and take corresponding measures to ensure the safety and reliability of the charging process.

[0089] 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. Inputting the time-series risk assessment value into a preset division interval in the transmitting-end device to obtain the charging strategy corresponding to the division interval includes:

[0090] Perform a non - linear transformation on the time - series risk assessment value using a preset hyperbolic tangent transformation function to obtain a time - series transformation risk index;

[0091] Construct a time - series risk curve based on the time - series transformation risk index, and map the time - series risk curve 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;

[0092] Monitor the point coordinates in the time - series risk curve in the coordinate system through a preset Kalman filtering algorithm to obtain the ordinate value corresponding to the point coordinates;

[0093] When the ordinate value is within the first interval of the divided intervals, obtain a stop - charging strategy corresponding to the first interval;

[0094] When the ordinate value is within the second interval of the divided intervals, obtain a low - gear short - time charging strategy corresponding to the second interval;

[0095] When the ordinate value is within the third interval of the divided intervals, obtain a normal - gear - switching charging strategy corresponding to the third interval.

[0096] Specifically, the charging strategy includes a stop charging strategy, a low-gear short-time charging strategy, and a normal gear-switching charging strategy. Inputting the timing risk assessment value into a preset division interval in the transmitting device to obtain the charging strategy corresponding to the division interval is a process to ensure 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: non-linear transformation, constructing a timing risk curve, Kalman filter monitoring, and division interval strategy selection. First, use a preset hyperbolic tangent transformation function to perform non-linear transformation on the timing risk assessment value to obtain a timing transformation risk index. The hyperbolic tangent transformation function is a commonly used non-linear transformation function that can map the input timing risk assessment value to a specific range, usually between -1 and 1. This non-linear transformation helps to highlight high-risk values while compressing low-risk values, making the risk assessment more sensitive. For example, in the application scenario of wireless charging for smartphones, assume that the timing risk assessment value at a certain moment is 0.8. Through the hyperbolic tangent transformation function, it can be converted into a timing transformation risk index. Assume the converted value is 0.95. This value not only retains the information of the original risk assessment value but also enhances the distinguishability of high-risk values through non-linear transformation. Next, construct a timing risk curve based on the timing transformation risk index and map the timing risk curve into a preset coordinate system to obtain a timing risk curve with a coordinate system. The timing risk curve is a risk index curve that changes with time and can intuitively display the risk change of the device during the charging process. In the preset coordinate system, the vertical axis represents the timing transformation risk index, and the horizontal axis represents time. For example, assume that within the first 10 minutes after the start of charging, the changes in the timing transformation risk index are as follows: 0.2 at the 1st minute, 0.3 at the 2nd minute, 0.4 at the 3rd minute, and so on until 0.8 at the 10th minute. These data can be plotted into a timing risk curve, showing that the risk index gradually rises over time. Then, monitor the point coordinates in the timing risk curve in the coordinate system through a preset Kalman filter algorithm to obtain the vertical coordinate 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 real risk value in the timing risk curve. Through the Kalman filter algorithm, each point on the timing risk curve can be monitored in real time to obtain its corresponding vertical coordinate value. For example, assume that at the 5th minute, the point coordinates on the timing risk curve are (5, 0.5). Through the Kalman filter algorithm, the real vertical coordinate value of this point can be obtained, assume it is 0.52. This value more accurately reflects the real risk status of the device at the 5th minute. Finally, select the corresponding charging strategy according to the position of the vertical coordinate value in the division interval.Specifically, when the ordinate value is within the first interval of the divided intervals, the stop charging strategy corresponding to the first interval is obtained; when the ordinate value is within the second interval of the divided intervals, 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 intervals, the normal gear-switching charging strategy corresponding to the third interval is obtained. Assume that there are three preset divided intervals inside the transmitting device: 0 - 0.3 represents the low-risk area, 0.3 - 0.7 represents the medium-risk area, and 0.7 - 1.0 represents the high-risk area. For example, in the application scenario of wireless charging for smartphones, assume that at the 5th minute, the time-series transformation risk index obtained through the Kalman filter algorithm is 0.52, and this value falls within the medium-risk area (0.3 - 0.7). Therefore, the transmitting device will select the 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 and ensure that the device will not be damaged due to long-term high temperature. If at the 10th minute, the time-series transformation risk index rises to 0.85, falling within the high-risk area (0.7 - 1.0), the transmitting device will immediately execute the stop charging strategy to completely interrupt the charging process until the device temperature drops to the safe range before resuming charging. At the initial stage of charging, assume that the time-series transformation risk index is 0.2, falling within the low-risk area (0 - 0.3), and the transmitting device will select the normal gear-switching charging strategy to continue charging at the current charging power. Through the above steps, the wireless charging system can dynamically adjust the charging strategy according to the real-time temperature data and risk assessment results of the receiving device to ensure that the charging process is both efficient and safe.

[0097] In a specific embodiment, 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 the normal gear-switching charging strategy, based on the charging status data, selecting the corresponding charging power gear inside the transmitting device includes:

[0098] When the charging strategy is the normal gear-switching charging strategy, through a preset charging strategy model, based on the charging status data, performing real-time charging demand analysis on the receiving device to obtain a charging demand index;

[0099] Calculating the size relationship between the charging demand index and the preset charging demand index range, and selecting the corresponding charging power gear inside the transmitting device according to the size relationship. Among them, selecting the corresponding charging power gear inside the transmitting device according to the size relationship includes:

[0100] When the size relationship is that the charging demand index is higher than the preset charging demand index range, then adopt the charging power gear corresponding to the high-gear charging strategy;

[0101] 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 medium gear charging strategy is adopted;

[0102] When the size relationship is that the charging demand index is lower than the preset charging demand index range, the charging power level corresponding to the low gear charging strategy is adopted.

[0103] Specifically, the normal switching gear charging strategy includes a high gear charging strategy, a medium gear charging strategy, and a low gear charging strategy. When the charging strategy is the normal switching gear 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 needs 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 the normal switching gear charging strategy, through a preset charging strategy model, the real-time charging demand of the receiving device is analyzed based on the charging status data 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, that is, the charging demand index, according to the charging status data such as the current battery level and charging speed of the receiving device. The higher this 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 a smartphone, assuming the current battery level of the phone is 30% and the charging speed is 0.5% per second, through the calculation of the charging strategy model, a charging demand index can be obtained, assuming it is 0.7. This index reflects the degree of charging demand of the phone in the current charging state. Next, calculate the size relationship between the charging demand index and the preset charging demand index range, and select the corresponding charging power gear in the transmitting device 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, assume the preset charging demand index range is from 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 gear 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, assume the charging demand index is 0.8, exceeding the preset upper limit value of 0.7. At this time, the transmitting device will select the high gear charging strategy, such as a charging power gear of 15W, to quickly increase the battery level of the device. When the size relationship is that the charging demand index is within the preset charging demand index range, the charging power gear corresponding to the medium gear 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, assume that the charging demand index is 0.5, which falls within the preset range of 0.3 to 0.7. At this time, the transmitting device will select a medium charging power level strategy, such as a charging power level of 10W, 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 level corresponding to the low charging power level strategy is adopted. This means that if the charging demand index is lower than the preset lower limit value, it indicates that the current charging demand of the device is relatively low, and the charging power can be appropriately reduced. For example, assume that the charging demand index is 0.2, which is lower than the preset lower limit value of 0.3. At this time, the transmitting device will select a low charging power level strategy, such as a charging power level of 5W, 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 state of the receiving device, ensuring 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 enhances the user experience, making wireless charging more intelligent, efficient, and safe. For example, in the application scenario of wireless charging for smartphones, assume that when the mobile phone starts charging, the current battery level is 20% and the charging speed is 0.6% per second. The charging demand index calculated through 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 transmitting device will select a high charging power level strategy, such as a charging power level of 15W, to quickly increase the battery level of the mobile phone. As the charging process progresses, the battery level of the mobile phone gradually increases. Assume that after 10 minutes of charging, the current battery level is 70% and the charging speed drops to 0.3% per second. The charging demand index calculated through 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 transmitting device will select a medium charging power level strategy, such as a charging power level of 10W, to balance the charging speed and battery health. Finally, when the battery level of the mobile phone is close to 100%, assume that the current battery level is 95% and the charging speed drops to 0.1% per second. The charging demand index calculated through 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 transmitting device will select a low charging power level strategy, such as a charging power level of 5W, 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 state of the receiving device, ensuring that the charging process is both efficient and safe.

[0104] In a specific embodiment, a charging coil and a resonant circuit are provided in the transmitting device. The output power of the transmitting device is adjusted based on the charging power level to obtain an adaptive output power, including:

[0105] Based on the charging power level, power mapping conversion is performed on the transmitting device through a preset power mapping algorithm to obtain an initial power parameter;

[0106] Perform impedance matching adjustment on the resonant circuit based on the initial power parameters to obtain matching resonant parameters;

[0107] Dynamically optimize the matching resonant parameters to obtain an optimized operating frequency;

[0108] Perform magnetic field intensity modulation on the transmitting end device based on the optimized operating frequency to obtain a modulated magnetic field distribution;

[0109] Perform homogenization processing on the modulated magnetic field distribution to obtain a uniform magnetic field;

[0110] Perform closed-loop control of the coil current on the charging coil based on the uniform magnetic field to obtain the adaptive output power.

[0111] Specifically, first, through a preset power mapping algorithm, the transmitting device is subjected to power mapping conversion based on the charging power level 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 transmitting 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 a smartphone, assume that the transmitting device selects a charging power level of 10W. Through the power mapping algorithm, 10W can be converted into specific voltage and current values, assumed to be 12V and 0.83A. These initial power parameters will serve as the basis for subsequent adjustment. Next, based on the initial power parameters, impedance matching adjustment is performed on the resonant circuit to obtain matching resonant parameters. The resonant circuit is an important part of the wireless charging system. It adjusts the impedance of the circuit to match the frequencies of the transmitting and receiving ends, thereby improving the energy transfer efficiency. Impedance matching adjustment means adjusting the inductance and capacitance values of the resonant circuit according to the initial power parameters to make it reach the optimal matching state. For example, assume 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 made to match the resonant frequency of the receiving device, thereby ensuring the maximum efficiency of energy transfer. The adjusted parameters are called matching resonant parameters, which will be used for subsequent dynamic optimization. Then, dynamic optimization is performed on the matching resonant parameters to obtain an optimized operating frequency. Dynamic optimization means further adjusting the operating frequency of the resonant circuit according to the matching resonant parameters to ensure that the transmitting and receiving ends always maintain the best resonant state during the actual charging process. Dynamic optimization can be achieved through a feedback control algorithm, which will monitor the operating state of the resonant circuit in real time and adjust the operating frequency as needed. For example, assume the matching resonant parameters are 12V, 0.83A, and a resonant frequency of 100kHz. Through the dynamic optimization algorithm, the resonant frequency can be adjusted in real time to ensure that it always maintains the best resonant state during the charging process, thereby improving the energy transfer efficiency. Next, based on the optimized operating frequency, magnetic field intensity modulation is performed on the transmitting device to obtain a modulated magnetic field distribution. Magnetic field intensity modulation means adjusting the magnetic field intensity of the transmitting device according to the optimized operating frequency to ensure the stability and efficiency of energy transfer. By adjusting the magnetic field intensity, the distribution of the magnetic field can be optimized to make it more concentrated and uniform. For example, assume the optimized operating frequency is 100kHz. Through magnetic field intensity modulation, the magnetic field intensity of the transmitting device can be adjusted to form a stable magnetic field distribution in the charging area, thereby improving the efficiency and stability of energy transfer. Then, homogenization processing is performed on the modulated magnetic field distribution to obtain a uniform magnetic field. Homogenization processing means making the modulated magnetic field distribution more uniform through specific algorithms or physical means to avoid the situation of too strong or too weak local magnetic fields.The formation of a uniform magnetic field can improve the uniformity and efficiency of energy transmission and reduce energy loss during the charging process. For example, assuming that the modulated magnetic field distribution is stronger in some areas and weaker in other areas, through homogenization, the magnetic field intensity in the entire charging area can be made to tend to be consistent, thus ensuring the uniformity and efficiency of energy transmission. Finally, based on the uniform magnetic field, closed-loop control of the coil current of the charging coil is performed to obtain the adaptive output power. Closed-loop control of the coil current means that through a feedback control system, the current of the charging coil is monitored in real time and the current value is adjusted as needed to ensure the stability and accuracy of the output power. Through closed-loop control of the coil current, the current of the charging coil can be precisely adjusted so that the output power matches the selected charging power level, thereby achieving the adaptive output power. For example, assuming that a uniform magnetic field has been formed, through closed-loop control of the coil current, 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 always remains at the level of 10W, thus 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, ensuring 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 for smartphones, assuming that the mobile phone needs to be charged at a power of 10W, the transmitting device first converts 10W into initial power parameters of 12V and 0.83A through a power mapping algorithm. Then, through impedance matching adjustment, the inductance and capacitance values of the resonant circuit are brought to the optimal matching state to obtain the matching resonant parameters. Next, through a dynamic optimization algorithm, the operating frequency of the resonant circuit is adjusted to ensure that it always maintains the optimal resonant state during the charging process. Subsequently, through magnetic field intensity modulation, the magnetic field intensity of the transmitting device is adjusted to form a stable magnetic field distribution. By homogenizing the modulated magnetic field distribution, the magnetic field intensity in the entire charging area is made to tend to be consistent. Finally, through closed-loop control of the coil current, the current of the charging coil is monitored in real time and the current value is adjusted to ensure that the output power always remains at the level of 10W, thus achieving efficient and safe charging. In this way, the wireless charging system can dynamically adjust the output power according to the real-time charging requirements of the receiving device, ensuring 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.

[0112] In a specific embodiment, the closed-loop control of the coil current of the charging coil based on the uniform magnetic field to obtain the adaptive output power includes:

[0113] The uniform magnetic field is measured in real time by a preset magnetic sensor to obtain the real-time magnetic field intensity;

[0114] The real-time magnetic field intensity is converted into the expected value of the coil current of the corresponding charging coil through a preset magnetic-electric conversion model;

[0115] Based on the expected value of the coil current, the initial current of the charging coil is adjusted to obtain a preliminarily adjusted current;

[0116] The spectrum characteristics analysis is performed on the preliminarily adjusted current to obtain spectrum characteristic data;

[0117] Based on the spectrum characteristic data, the harmonic components of the preliminarily adjusted current are eliminated to obtain the coil current after harmonic elimination;

[0118] Based on the coil current after harmonic elimination and the real-time magnetic field intensity, the adaptive output power is obtained;

[0119] The following formula is used to calculate the adaptive output power;

[0120]

[0121] where the represents the adaptive output power, represents the coil current after harmonic elimination, varying with time changing represents the real-time magnetic field intensity, varying with time changing represents the complex exponential function, is the angular frequency corresponding to the operating frequency of the transmitting-end device, which is where is the operating frequency, refers to the time of a complete operating cycle, that is .

[0122] Specifically, first, the uniform magnetic field is measured in real time through a preset magnetic sensor to obtain the real-time magnetic field intensity. A magnetic sensor is a device capable of detecting the magnetic field intensity and can monitor the magnetic field intensity generated by the transmitting device in real time. These magnetic sensors are usually distributed around the charging coil to ensure accurate measurement of the magnetic field intensity in the entire charging area. 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 intensity of this uniform magnetic field in real time and transmit this data to the control unit. Suppose at a certain moment, the real-time magnetic field intensity measured by the magnetic sensor is 0.5 Tesla. Next, through a preset magnetic-electric conversion model, the real-time magnetic field intensity is converted into the expected value of the coil current of the corresponding charging coil. The magnetic-electric conversion model is a mathematical model that can calculate the required coil current value based on the magnetic field intensity. Through this model, the real-time measured magnetic field intensity can be converted into the expected value of the coil current of the charging coil, thereby guiding subsequent current regulation. For example, assume the formula of the magnetic-electric conversion model is , where is the expected value of the coil current, is the real-time magnetic field intensity, and k is a constant. If k = 2 A / Tesla and the real-time magnetic field intensity B = 0.5 Tesla, then the expected value of the coil current Amperes. Then, based on the expected value of the coil current, the initial current of the charging coil is adjusted to obtain a preliminarily adjusted current. The initial current refers to the current value of the charging coil before adjustment. Through the adjustment circuit, the initial current can be adjusted to the expected value of the coil current. For example, assume the initial current is 0.8 amperes and the expected value of the coil current is 1 ampere. Through the adjustment circuit, the initial current can be adjusted from 0.8 amperes to 1 ampere to obtain a preliminarily adjusted current. Next, spectral characteristic analysis is performed on the preliminarily adjusted current to obtain spectral characteristic data. Spectral characteristic analysis refers to converting the time-domain signal of the preliminarily adjusted current into a frequency-domain signal through methods such as Fourier transform to analyze its spectral characteristics. This step can help identify the harmonic components in the preliminarily adjusted current and provide a basis for subsequent harmonic elimination. For example, assume the time-domain signal of the preliminarily adjusted current is i(t). Through Fourier transform, its spectral characteristic data I(f) can be obtained, where f represents frequency. Then, based on the spectral characteristic data, harmonic component elimination is performed on the preliminarily adjusted current to obtain the coil current after harmonic elimination. Harmonic component elimination refers to removing the harmonic components in the preliminarily adjusted current through methods such as filters to make the current purer. For example, assume the spectral characteristic data shows that there are obvious 50 Hz harmonic components in the preliminarily adjusted 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, based on the coil current after harmonic elimination and the real-time magnetic field intensity, the adaptive output power is obtained. The adaptive output power refers to the final output power calculated based on the coil current after harmonic elimination and the real-time magnetic field intensity. The specific calculation formula is as follows: The following formula is used to calculate the adaptive output power;

[0123]

[0124] where, the represents the adaptive output power, represents the coil current after harmonic elimination, varying with time changing represents the real-time magnetic field intensity, varying with time changing represents the complex exponential function, is the angular frequency corresponding to the operating frequency of the transmitting-end device, which is where is the operating frequency, refers to a complete operating cycle time, that is .

[0125] For example, assume the coil current after harmonic elimination, is 1 ampere, the real-time magnetic field intensity B(t) is 0.5 tesla, and the operating frequency f is 100 kHz. Then the angular frequency = 100 × 10 3 rad / s, and the time T for a complete working cycle is == 10 μs. 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 enhances the user experience, making wireless charging more intelligent, efficient, and safe. For example, in the application scenario of wireless charging for smartphones, assume that the phone needs to be charged at a power of 10W. The transmitting device first measures the intensity of the uniform magnetic field in real time through a magnetic sensor, assume it is 0.5 Tesla. Then, through the magnetic-electric conversion model, the real-time magnetic field intensity is converted into the expected value of the coil current, assume it is 1 Ampere. Next, through the adjustment circuit, the initial current is adjusted from 0.8 Ampere to 1 Ampere to obtain the preliminary adjusted current. By analyzing the spectral characteristics of the preliminary adjusted current, it is found that there is a harmonic component of 50Hz. These harmonic components are filtered out through a low-pass filter to obtain the coil current after harmonic elimination. Finally, based on the coil current after harmonic elimination and the real-time magnetic field intensity, the adaptive output power is calculated through the above formula to ensure that the output power always maintains 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 requirements of the receiving device, ensuring 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.

[0126] The method for adjusting the wireless charging power in the embodiments of the present invention has been described above. Next, the wireless charging power adjustment system in the embodiments of the present invention will be described. Please refer to Figure 2 , an embodiment of the wireless charging power adjustment system in the embodiments of the present invention includes:

[0127] A collection module 21 for continuously collecting 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 battery level and the current charging speed;

[0128] An acquisition module 22 for collecting temperature data of the receiving device through a preset temperature sensor to obtain the acquired temperature data, and performing a timing risk assessment on the receiving device based on the acquired temperature data to obtain a timing risk assessment value;

[0129] A partitioning module 23, configured to input the timing risk assessment value into a preset partitioning range in the transmitting device to obtain a charging strategy corresponding to the partitioning range; wherein, the charging strategy includes a charging stop strategy, a low-gear short-time charging strategy, and a normal gear-switching charging strategy;

[0130] A selection module 24, configured to, when the charging strategy is a normal gear-switching charging strategy, select a corresponding charging power gear in the transmitting device based on the charging status data;

[0131] An adjustment module 25, configured to adjust the output power of the transmitting device based on the charging power gear to obtain an adaptive output power, and perform safe charging on the receiving device based on the adaptive output power.

[0132] 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, and details are not described herein again.

[0133] Refer to Figure 3 , in the embodiment of the present invention, a computer device is further provided. The internal structure of the computer device may be as Figure 3 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 of the computer design 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. The computer program, when executed by the processor, implements the above method.

[0134] Those skilled in the art can understand that Figure 3 the structure shown in

[0135] is only a block diagram of a part 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.

[0136] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. 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 methods. Among them, any reference to a memory, storage, database, or other medium provided by the present invention and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0137] It should be noted that in this article, the term "including", "comprising", or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, device, article, or method including a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such a process, device, article, or method. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, device, article, or method including that element.

[0138] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally 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; 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; 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; Performing a timing thermal runaway risk assessment on the receiving-end device based on the temperature change trend to obtain a timing risk assessment value; 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; Performing closed-loop control on the coil current of the charging coil based on the uniform magnetic field to obtain the adaptive output power; 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 transmitter device, which is ,in is the operating frequency, It refers to a complete working cycle time, that is, .

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; Based on the current power level, a charging speed is calculated for the receiving device to obtain a current charging speed.

3. 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.

4. The wireless charging power adjustment method according to claim 3, 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.

5. 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; an adjustment module, configured 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; 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; Performing a timing thermal runaway risk assessment on the receiving-end device based on the temperature change trend to obtain a timing risk assessment value; 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; Performing closed-loop control on the coil current of the charging coil based on the uniform magnetic field to obtain the adaptive output power; 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, .

6. 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 4 are implemented.

7. 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 4 are implemented.

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