Wireless charging power-off protection method for detecting metal foreign matter based on signal change

Abstract

The invention provides a wireless charging power-off protection method for detecting metal foreign matters based on signal change, which comprises the following steps: monitoring magnetic field change through a Hall effect sensor, detecting temperature abnormity in combination with a temperature sensor, carrying out real-time data processing by utilizing an embedded MCU, and automatically entering a calibration mode before charging each time; the method comprises the following steps: acquiring stable magnetic field distribution as a reference value, tracking magnetic field change by using a self-adaptive filter, continuously monitoring magnetic field intensity change in a charging process, comparing the magnetic field intensity change with the reference value, judging whether the magnetic field intensity change exceeds a normal range or not by setting a threshold value, immediately cutting off a power supply and giving an alarm to a user if foreign matters are detected, and after the user confirms that the foreign matters are removed, stopping charging. According to the method, the foreign matters can be accurately detected in different environments through dynamic threshold adjustment and feature optimization, so that the safety of wireless charging and the user experience are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless charging, and in particular to a wireless charging power-off protection method based on detecting metal foreign objects based on signal changes. Background Art

[0002] Wireless charging technology has developed rapidly in recent years, especially with the popularization of the Qi standard, more and more devices support wireless charging. The basic principle of wireless charging is to use electromagnetic induction to transmit energy, and to achieve contactless energy transfer through the interaction between the alternating magnetic field generated by the transmitting coil and the receiving coil. However, wireless charging technology also faces some challenges, one of which is the presence of metal foreign matter in the charging area, which may cause overheating or equipment damage.

[0003] The presence of foreign matter is determined by detecting the loop resistance formed between the receiving coil and the transmitting coil, but this method is not sensitive enough for detecting thin metal sheets or other forms of foreign matter; a temperature sensor is used to detect temperature changes in the charging area. When the temperature rises abnormally, it is determined that there may be foreign matter, but this method is ineffective for non-thermal conductive foreign matter; a magnetic field sensor is used to detect changes in the magnetic field. This method is more effective for detecting metal foreign matter, but due to the influence of environmental factors, it may lead to misjudgment. Summary of the invention

[0004] The purpose of the present invention is to provide a wireless charging power-off protection method for detecting metal foreign objects based on signal changes.

[0005] The problem to be solved by the present invention is: to solve the potential safety hazard caused by metal foreign objects entering the charging area during wireless charging, by integrating high-sensitivity Hall effect sensors and temperature sensors, and using embedded MCU to process data, automatically calibrating the magnetic field reference value, and monitoring the magnetic field changes in real time. When abnormal changes in the magnetic field are detected, the power supply will be cut off immediately to prevent overheating and equipment damage, and the user will be prompted to remove the foreign objects through the user interface. Through dynamic threshold setting and adaptive filter technology, the accuracy and reliability of foreign object detection are improved, ensuring the safety of wireless charging and user experience.

[0006] A wireless charging power-off protection method based on signal change detection of metal foreign objects, the technical solution adopted is as follows: S1: Select Hall effect sensors to be distributed around the wireless charging device, install temperature sensors at different locations of the wireless charging device, and use an embedded MCU to process sensor data; S2: Before each wireless charging, it automatically enters the calibration mode to record the magnetic field distribution when there are no foreign objects. When the user moves the device during charging, it identifies the new magnetic field distribution, updates the baseline value, and uses an adaptive filter to track the magnetic field changes. S3: Set the sampling frequency according to the demand, process the sensor data, and compare and analyze it with the benchmark value. Use digital signal processing technology to analyze the magnetic field data and determine whether the magnetic field change exceeds the normal range by setting the threshold. S4: Extract the trend of magnetic field intensity over time and the frequency component characteristics, optimize the feature set based on experimental data, set the threshold according to the test data, dynamically adjust the threshold according to the actual use of different environments, and collect a large amount of data under normal and abnormal conditions for training the model; S5: Cut off the power supply immediately after detecting a foreign object. At the same time, take measures to prevent the sudden power outage from causing damage to the equipment, and display a concise message to inform the user that a foreign object exists. S6: The user confirms that the foreign object has been removed by pressing the button, sets a reasonable waiting time to avoid user misoperation, and enters the calibration mode again before restarting charging; S7: Record the magnetic field change data during each charging process, including normal and abnormal conditions, regularly check the detection accuracy and response speed performance indicators, and continuously optimize the algorithm based on the evaluation results and user feedback.

[0007] Furthermore, in S1, Hall effect sensors are selected to be distributed around the wireless charging device, temperature sensors are installed at different positions of the wireless charging device, and the sensor data is processed using an embedded MCU, including: Select highly sensitive sensors with strong anti-electromagnetic interference capabilities, and evenly arrange the Hall effect sensors around the entire wireless charging device to cover all potential foreign object placement areas; Install a temperature sensor at a location where heat accumulates near the transmitting coil, and set a safety threshold according to the operating temperature range of the wireless charging device. If the threshold is exceeded, an alarm is triggered and power is cut off for protection; Select high-performance, low-power embedded MCU to support real-time data processing and integrate the data of Hall effect sensors and temperature sensors for transmission.

[0008] Furthermore, the S2 automatically enters a calibration mode, identifies a new magnetic field distribution, updates a reference value, and uses an adaptive filter to track magnetic field changes, including: S21: When the user turns on the wireless charging device, it automatically enters the calibration mode and sets the calibration time to 20 seconds. Stable magnetic field distribution data is obtained during the calibration time. S22: Collect magnetic field strength data at each point through Hall effect sensors distributed around the charging device, synchronously record data from the temperature sensor, and perform temperature compensation on the magnetic field strength; S23: De-noising the collected data to remove high-frequency noise and transient interference, and storing the processed data in the embedded MCU as part of the reference value; S24: fusing the data from multiple sensors to form a comprehensive magnetic field distribution map, and taking an average of the data from multiple measurements; S25: During the charging process, the change of magnetic field strength is continuously monitored and a threshold is set. Recalibration is triggered only when the magnetic field change deviates significantly from the baseline value. When the magnetic field change is detected to exceed the preset threshold, it automatically enters the temporary calibration mode, identifies the new magnetic field distribution, and designs an LMS adaptive filter to track the change of the magnetic field.

[0009] Furthermore, the sensor data is processed in S3, and compared with the reference value for analysis, and a threshold is set to determine whether the magnetic field change exceeds the normal range, including: S31: dynamically updating the reference value according to the result of the adaptive filter, comparing the characteristic value obtained by real-time analysis with the reference value, and determining that a foreign object exists if the characteristic value exceeds the set threshold range; S32: Collect a large amount of magnetic field data under different conditions, including the presence or absence of foreign objects and different environments, as training samples, screen and combine them according to the importance of features to form an optimal feature set.

[0010] Furthermore, the threshold is set according to the test data in S4, and the threshold is dynamically adjusted according to the actual usage of different environments, including: S41: Use the sliding window averaging method to smooth the data, record the trend of magnetic field intensity over time, use fast Fourier transform to analyze the time series data of magnetic field intensity to extract the frequency component, and calculate the gradient of magnetic field intensity. , , ,in is the window size, is the smoothed data, is the temporal variation trend of magnetic field intensity, is the rate of change of magnetic field strength; S42: Setting dynamic thresholds , use the most recent The threshold is calculated based on the data of time steps. ,in Recently The average value of time steps, , Recently The standard deviation of the time steps, , is the coefficient; S43: , when the absolute difference between the smoothed signal and the expected value is greater than the threshold , it is considered that an abnormality is detected at the judgment time t, otherwise, no abnormality is detected at the judgment time t.

[0011] The beneficial effects of the present invention are: when a metal foreign body is detected, it can quickly respond and cut off the power supply to prevent overheating or fire accidents caused by the metal foreign body, and combine magnetic field strength detection with temperature monitoring to provide a multi-level safety protection mechanism to ensure the safety of users and equipment; It can automatically identify new magnetic field distribution and update the reference value during charging, so as to adapt to environmental changes, maintain stability, and dynamically adjust the threshold value, so as to better adapt to different usage environments and conditions, and reduce the possibility of false alarms and missed alarms; By analyzing a large number of training samples, the most effective feature set is screened out, which improves the accuracy and reliability of detection. The changes in magnetic field intensity are monitored in real time and compared with the baseline value to detect abnormal situations in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 The present invention is a flow chart of a wireless charging power-off protection method based on detecting metal foreign objects through signal changes. DETAILED DESCRIPTION

[0013] The present invention is further clearly and completely described below, but the protection scope of the present invention is not limited thereto.

[0014] A wireless charging power-off protection method based on signal change detection of metal foreign objects, the technical solution adopted is as follows: S1: Select Hall effect sensors to be distributed around the wireless charging device, install temperature sensors at different locations of the wireless charging device, and use an embedded MCU to process sensor data; S2: Before each wireless charging, it automatically enters the calibration mode to record the magnetic field distribution when there are no foreign objects. When the user moves the device during charging, it identifies the new magnetic field distribution, updates the baseline value, and uses an adaptive filter to track the magnetic field changes. S3: Set the sampling frequency according to the demand, process the sensor data, and compare and analyze it with the benchmark value. Use digital signal processing technology to analyze the magnetic field data and determine whether the magnetic field change exceeds the normal range by setting the threshold. S4: Extract the trend of magnetic field intensity over time and the frequency component characteristics, optimize the feature set based on experimental data, set the threshold according to the test data, dynamically adjust the threshold according to the actual use of different environments, and collect a large amount of data under normal and abnormal conditions for training the model; S5: Cut off the power supply immediately after detecting a foreign object. At the same time, take measures to prevent the sudden power outage from causing damage to the equipment, and display a concise message to inform the user that a foreign object exists. S6: The user confirms that the foreign object has been removed by pressing the button, sets a reasonable waiting time to avoid user misoperation, and enters the calibration mode again before restarting charging; S7: Record the magnetic field change data during each charging process, including normal and abnormal conditions, regularly check the detection accuracy and response speed performance indicators, and continuously optimize the algorithm based on the evaluation results and user feedback.

[0015] Furthermore, in S1, Hall effect sensors are selected to be distributed around the wireless charging device, temperature sensors are installed at different positions of the wireless charging device, and the sensor data is processed using an embedded MCU, including: Select highly sensitive sensors with strong anti-electromagnetic interference capabilities, and evenly arrange the Hall effect sensors around the entire wireless charging device to cover all potential foreign object placement areas; Install a temperature sensor at a location where heat accumulates near the transmitting coil, and set a safety threshold according to the operating temperature range of the wireless charging device. If the threshold is exceeded, an alarm is triggered and power is cut off for protection; Select high-performance, low-power embedded MCU to support real-time data processing and integrate the data of Hall effect sensors and temperature sensors for transmission.

[0016] Furthermore, the S2 automatically enters a calibration mode, identifies a new magnetic field distribution, updates a reference value, and uses an adaptive filter to track magnetic field changes, including: S21: When the user turns on the wireless charging device, it automatically enters the calibration mode and sets the calibration time to 20 seconds. Stable magnetic field distribution data is obtained during the calibration time. S22: Collect magnetic field strength data at each point through Hall effect sensors distributed around the charging device, synchronously record data from the temperature sensor, and perform temperature compensation on the magnetic field strength; S23: De-noising the collected data to remove high-frequency noise and transient interference, and storing the processed data in the embedded MCU as part of the reference value; S24: fusing the data from multiple sensors to form a comprehensive magnetic field distribution map, and taking an average of the data from multiple measurements; S25: During the charging process, the change of magnetic field strength is continuously monitored and a threshold is set. Recalibration is triggered only when the magnetic field change deviates significantly from the baseline value. When the magnetic field change is detected to exceed the preset threshold, it automatically enters the temporary calibration mode, identifies the new magnetic field distribution, and designs an LMS adaptive filter to track the change of the magnetic field.

[0017] Furthermore, the sensor data is processed in S3, and compared with the reference value for analysis, and a threshold is set to determine whether the magnetic field change exceeds the normal range, including: S31: dynamically updating the reference value according to the result of the adaptive filter, comparing the characteristic value obtained by real-time analysis with the reference value, and determining that a foreign object exists if the characteristic value exceeds the set threshold range; S32: Collect a large amount of magnetic field data under different conditions, including the presence or absence of foreign objects and different environments, as training samples, screen and combine them according to the importance of features to form an optimal feature set.

[0018] Furthermore, the threshold is set according to the test data in S4, and the threshold is dynamically adjusted according to the actual usage of different environments, including: S41: Use the sliding window averaging method to smooth the data, record the trend of magnetic field intensity over time, use fast Fourier transform to analyze the time series data of magnetic field intensity to extract the frequency component, and calculate the gradient of magnetic field intensity. , , ,in is the window size, is the smoothed data, is the temporal variation trend of magnetic field intensity, is the rate of change of magnetic field strength; S42: Setting dynamic thresholds , use the most recent The threshold is calculated based on the data of time steps. ,in Recently The average value of time steps, , Recently The standard deviation of the time steps, , is the coefficient; S43: , when the absolute difference between the smoothed signal and the expected value is greater than the threshold , it is considered that an abnormality is detected at the judgment time t, otherwise, no abnormality is detected at the judgment time t.

[0019] The present invention provides a wireless charging power-off protection method based on signal change detection of metal foreign objects. The method monitors magnetic field changes through a Hall effect sensor, detects temperature anomalies in combination with a temperature sensor, and uses an embedded MCU to perform real-time data processing. Before each charging, the method automatically enters a calibration mode to obtain a stable magnetic field distribution as a reference value, and uses an adaptive filter to track magnetic field changes. During the charging process, the magnetic field intensity change is continuously monitored and compared with the reference value. A threshold is set to determine whether it exceeds a normal range. If a foreign object is detected, the power supply is immediately cut off and a warning is issued to the user. After the user confirms that the foreign object has been removed, the method recalibrates and resumes charging. Dynamic threshold adjustment and feature optimization ensure that foreign objects can be accurately detected in different environments, thereby improving the safety of wireless charging and user experience.

Claims

1. A wireless charging power-off protection method for detecting metal foreign objects based on signal changes, characterized in that: include: S1: Select Hall effect sensors to be distributed around the wireless charging device, install temperature sensors at different locations of the wireless charging device, and use an embedded MCU to process sensor data; S2: Before each wireless charging, it automatically enters the calibration mode to record the magnetic field distribution when there are no foreign objects. When the user moves the device during charging, it identifies the new magnetic field distribution, updates the baseline value, and uses an adaptive filter to track the magnetic field changes. S3: Set the sampling frequency according to the demand, process the sensor data, and compare and analyze it with the benchmark value. Use digital signal processing technology to analyze the magnetic field data and determine whether the magnetic field change exceeds the normal range by setting the threshold. S4: Extract the trend of magnetic field intensity over time and the frequency component characteristics, optimize the feature set based on experimental data, set the threshold according to the test data, dynamically adjust the threshold according to the actual use of different environments, and collect a large amount of data under normal and abnormal conditions for training the model; S5: Cut off the power supply immediately after detecting a foreign object. At the same time, take measures to prevent the sudden power outage from causing damage to the equipment, and display a concise message to inform the user that a foreign object exists. S6: The user confirms that the foreign object has been removed by pressing the button, sets a reasonable waiting time to avoid user misoperation, and enters the calibration mode again before restarting charging; S7: Record the magnetic field change data during each charging process, including normal and abnormal conditions, regularly check the detection accuracy and response speed performance indicators, and continuously optimize the algorithm based on the evaluation results and user feedback.

2. A wireless charging power-off protection method for detecting metal foreign objects based on signal changes as claimed in claim 1, characterized in that: In S1, Hall effect sensors are distributed around the wireless charging device, temperature sensors are installed at different positions of the wireless charging device, and the sensor data is processed using an embedded MCU, including: Select highly sensitive sensors with strong anti-electromagnetic interference capabilities, and evenly arrange the Hall effect sensors around the entire wireless charging device to cover all potential foreign object placement areas; Install a temperature sensor at a location where heat accumulates near the transmitting coil, and set a safety threshold according to the operating temperature range of the wireless charging device. If the threshold is exceeded, an alarm is triggered and power is cut off for protection; Select high-performance, low-power embedded MCU to support real-time data processing and integrate the data of Hall effect sensors and temperature sensors for transmission.

3. A wireless charging power-off protection method for detecting metal foreign objects based on signal changes as claimed in claim 1, characterized in that: The S2 automatically enters the calibration mode, identifies the new magnetic field distribution, updates the reference value, and uses an adaptive filter to track the magnetic field changes, including: S21: When the user turns on the wireless charging device, it automatically enters the calibration mode and sets the calibration time to 20 seconds. Stable magnetic field distribution data is obtained during the calibration time. S22: Collect magnetic field strength data at each point through Hall effect sensors distributed around the charging device, synchronously record data from the temperature sensor, and perform temperature compensation on the magnetic field strength; S23: De-noising the collected data to remove high-frequency noise and transient interference, and storing the processed data in the embedded MCU as part of the reference value; S24: fusing the data from multiple sensors to form a comprehensive magnetic field distribution map, and taking an average of the data from multiple measurements; S25: During the charging process, the change of magnetic field strength is continuously monitored and a threshold is set. Recalibration is triggered only when the magnetic field change deviates significantly from the baseline value. When the magnetic field change is detected to exceed the preset threshold, it automatically enters the temporary calibration mode, identifies the new magnetic field distribution, and designs an LMS adaptive filter to track the change of the magnetic field.

4. A wireless charging power-off protection method for detecting metal foreign objects based on signal changes as claimed in claim 1, characterized in that: The sensor data is processed in S3, and compared with the reference value for analysis, and a threshold is set to determine whether the magnetic field change exceeds the normal range, including: S31: dynamically updating the reference value according to the result of the adaptive filter, comparing the characteristic value obtained by real-time analysis with the reference value, and determining that a foreign object exists if the characteristic value exceeds the set threshold range; S32: Collect a large amount of magnetic field data under different conditions, including the presence or absence of foreign objects and different environments, as training samples, screen and combine them according to the importance of features to form an optimal feature set.

5. A wireless charging power-off protection method for detecting metal foreign objects based on signal changes as claimed in claim 1, characterized in that: In S4, the threshold is set according to the test data, and the threshold is dynamically adjusted according to the actual usage of different environments, including: S41: Use the sliding window averaging method to smooth the data, record the trend of magnetic field intensity over time, use fast Fourier transform to analyze the time series data of magnetic field intensity to extract the frequency component, and calculate the gradient of magnetic field intensity. , , ,in is the window size, is the smoothed data, is the temporal variation trend of magnetic field intensity, is the rate of change of magnetic field strength; S42: Setting dynamic thresholds , use the most recent The threshold is calculated based on the data of time steps. ,in Recently The average value of time steps, , Recently The standard deviation of the time steps, , is the coefficient; S43: , when the absolute difference between the smoothed signal and the expected value is greater than the threshold , it is considered that an abnormality is detected at the judgment time t, otherwise, no abnormality is detected at the judgment time t.

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