Foreign matter detection wireless charging method and device, equipment and storage medium

Abstract

The invention relates to a foreign matter detection wireless charging method, apparatus and device, and a storage medium. The method comprises the following steps: monitoring charging data of a target charging panel to a to-be-charged device based on a second detection signal; if the charging data is abnormal, identifying whether a metal foreign matter exists in the target charging area based on the second detection signal and the first detection signal; based on the target charging area image, identifying whether a non-metal foreign matter exists in the target charging area; if the target charging area has the metal foreign matter and / or the non-metal foreign matter, performing charging danger degree analysis on the target charging area based on a second detection signal corresponding to the metal foreign matter and the first detection signal and / or the target charging area image to obtain a danger coefficient; and obtaining a safety response strategy based on the danger coefficient, thereby solving the technical problem of how to quickly and accurately detect various types of foreign matters on the premise of not influencing the charging efficiency.

Description

Technical Field

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

[0002] With the development of science and technology and the changes in people's lifestyles, wireless charging technology has rapidly become popular due to its convenience and efficiency, and is widely used in many fields such as smartphones, smart wearable devices and even electric vehicles. However, the safety hazards in the wireless charging process have gradually become a problem that cannot be ignored, especially when metal or other foreign objects are accidentally placed on the charging plate, which may cause serious consequences such as overheating, short circuit and even fire. Although the existing wireless charging standards and equipment have taken some basic safety measures, there are still many deficiencies in foreign object detection, especially in the complex and changeable actual use environment, the diversity of foreign objects and the uncertainty of their positions pose a huge challenge to accurate detection. In the prior art, most wireless charging systems rely on temperature sensors to monitor overheating during charging. Although this method can prevent accidents to a certain extent, its reaction speed is slow and it often cannot respond in time in the early stage caused by foreign objects, thus missing the best time to deal with it. In addition, temperature sensors can only reflect local temperature changes, and it is difficult to effectively perceive the potential risks caused by non-metallic foreign objects. Therefore, how to quickly and accurately detect various types of foreign objects without affecting the charging efficiency, and evaluate the corresponding danger level according to the nature and location of the foreign objects, has become a technical problem that needs to be solved urgently.

[0003] To address the above challenges, this study proposed a wireless charging method for foreign object detection that combines electromagnetic field detection and image recognition technology. This method can not only use the changing characteristics of the electromagnetic field to accurately determine the presence of metal foreign objects, but also effectively identify non-metallic foreign objects through image recognition technology, thereby achieving comprehensive monitoring of the charging area. At the same time, through the comprehensive analysis of the detection signal and image information, the potential risks in the charging process can be evaluated more scientifically and reasonably, providing a reliable basis for formulating corresponding safety response strategies. This innovative solution is expected to significantly improve the safety of wireless charging systems and promote the further development and application of wireless charging technology. Summary of the invention

[0004] The main purpose of the present invention is to provide a foreign object detection wireless charging method, device, equipment and storage medium, which solves the technical problem of how to quickly and accurately detect various types of foreign objects without affecting the charging efficiency, and evaluate the corresponding danger level according to the nature and location of the foreign objects.

[0005] To achieve the above object, the present invention provides a foreign object detection wireless charging method, comprising the following steps: Transmitting a first detection signal to a target charging area in a target charging plate and receiving a second detection signal through a preset electromagnetic field detector; Monitoring charging data of the target charging plate to the device to be charged based on the second detection signal by using a preset charging environment algorithm; If the charging data is abnormal, identifying whether there is a metal foreign object in the target charging area based on the second detection signal and the first detection signal by using a preset resonance frequency shift detection technology; Acquire a target charging area image corresponding to the target charging area, and identify whether there is a non-metallic foreign object in the target charging area based on the target charging area image by a preset image recognition algorithm; If there are metal foreign objects and / or non-metal foreign objects in the target charging area, a charging risk level analysis is performed on the target charging area based on the second detection signal corresponding to the metal foreign objects and the first detection signal and / or the target charging area image to obtain a risk coefficient; A safety response strategy is obtained based on the risk factor.

[0006] The present invention also provides a foreign object detection wireless charging device, comprising: A receiving module, used to transmit a first detection signal to a target charging area in a target charging plate and receive a second detection signal through a preset electromagnetic field detector; A receiving module, used to transmit a first detection signal to a target charging area in a target charging plate and receive a second detection signal through a preset electromagnetic field detector; A monitoring module, configured to monitor charging data of the target charging plate to the device to be charged based on the second detection signal by using a preset charging environment algorithm; a first identification module, configured to identify whether there is a metal foreign object in the target charging area based on the second detection signal by using a preset resonance frequency shift detection technology if there is an abnormality in the charging data; A second recognition module is used to obtain a target charging area image corresponding to the target charging area, and identify whether there is a non-metallic foreign object in the target charging area based on the target charging area image through a preset image recognition algorithm; an analysis module, configured to analyze the charging risk level of the target charging area based on the second detection signal corresponding to the metal foreign matter and the first detection signal and / or the target charging area image to obtain a risk coefficient if there are metal foreign matter and / or non-metal foreign matter in the target charging area; A strategy module is used to obtain a safety response strategy based on the risk factor.

[0007] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0008] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0009] The foreign object detection wireless charging method provided by the present invention comprises the following steps: transmitting a first detection signal and receiving a second detection signal to a target charging area in a target charging plate through a preset electromagnetic field detector; monitoring charging data of the target charging plate to the device to be charged based on the second detection signal through a preset charging environment algorithm; if the charging data is abnormal, identifying whether there is a metal foreign object in the target charging area based on the second detection signal and the first detection signal through a preset resonant frequency shift detection technology; acquiring a target charging area image corresponding to the target charging area, and identifying whether there is a non-metallic foreign object in the target charging area based on the target charging area image through a preset image recognition algorithm; if the target If there are metal foreign objects and / or non-metallic foreign objects in the target charging area, the charging hazard level of the target charging area is analyzed based on the second detection signal corresponding to the metal foreign object and the first detection signal and / or the target charging area image to obtain a hazard coefficient; a safety response strategy is obtained based on the hazard coefficient; through the above-mentioned technical means, the technical problem of how to quickly and accurately detect various types of foreign objects without affecting the charging efficiency, and evaluate the corresponding hazard level according to the nature and location of the foreign objects is solved, and the charging status is monitored in real time and safety measures are automatically executed, which reduces the need for manual intervention by users, which not only protects the property safety of users, but also provides a more secure and convenient charging experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 is a schematic diagram of the steps of a wireless charging method for foreign object detection in one embodiment of the present invention; Figure 2 is a structural block diagram of a foreign object detection wireless charging device in one embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0011] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0012] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0013] like Figure 1 As shown, Figure 1 It is a schematic diagram of the steps of a foreign object detection wireless charging method in one embodiment of the present invention; In one embodiment of the present invention, a foreign object detection wireless charging method is provided, which is characterized by comprising the following steps: Step S1, using a preset electromagnetic field detector, transmitting a first detection signal to a target charging area in a target charging plate and receiving a second detection signal.

[0014] Specifically, when implementing the above-mentioned step of "transmitting a first detection signal and receiving a second detection signal to a target charging area in a target charging plate through a preset electromagnetic field detector", one or more specially designed electromagnetic field detectors need to be installed in the wireless charging device first. The function of these detectors is to work continuously before the start of wireless charging and during the charging process. They can send an electromagnetic wave called a "first detection signal" to a specific charging area in the target charging plate. This electromagnetic wave is a signal of a specific frequency used to detect whether an object attempted to be placed on the charging plate will interfere with the charging process. When these electromagnetic waves encounter an object, they will be reflected back to form a "second detection signal" and received by the same or another group of detectors. By comparing the difference between the first detection signal and the second detection signal, it is possible to analyze whether there is a foreign object in the target charging area and the nature of the foreign object. For example, in a typical wireless charging scenario, suppose a user places a smartphone on a wireless charging plate to charge it. At this time, the electromagnetic field detector is started according to a preset program and transmits a first detection signal to the target charging area below the phone. If there are other objects in the area besides the mobile phone, such as a coin or a plastic card, these objects will change the distribution of the electromagnetic field, causing the second detection signal to be different from the normal signal (that is, when only the mobile phone is present). After receiving these changed signals, the detector analyzes these differences through the built-in algorithm to determine whether there is a foreign object, and further determine whether the foreign object is metal or non-metal, thereby providing key data support for the subsequent foreign object detection steps.

[0015] Step S2: monitor the charging data of the target charging plate to the device to be charged based on the second detection signal through a preset charging environment algorithm.

[0016] Specifically, when implementing the step of "monitoring the charging data of the target charging plate to the device to be charged based on the second detection signal through a preset charging environment algorithm", it is first necessary to understand that the "charging environment algorithm" here refers to a set of pre-written computer programs that are intended to analyze the second detection signal received by the electromagnetic field detector to evaluate various parameters in the charging process. When the electromagnetic field detector transmits the first detection signal to the target charging area in the target charging plate and receives the second detection signal, these signals will be transmitted to the control unit. The control unit runs a preset charging environment algorithm, which can parse the information in the second detection signal, such as signal strength, phase change, etc., to monitor the actual charging process between the charging plate and the device to be charged. For example, in the wireless charging scenario mentioned above, when the user places the smartphone on the charging plate to charge, the charging environment algorithm starts working. Assuming that there are no other foreign objects around the charging plate at this time, the algorithm will normally monitor the charging current, voltage and other parameters according to the received second detection signal to ensure that the charging process proceeds smoothly. However, if foreign objects (such as metal objects such as keys and coins) accidentally fall into the charging area during the charging process, these foreign objects will change the distribution of the electromagnetic field, causing the second detection signal to change. By analyzing these changes, the charging environment algorithm can quickly detect abnormalities in charging data, such as sudden increases or decreases in current, voltage fluctuations, and other phenomena. Once these abnormalities are detected, the algorithm will trigger the subsequent foreign object detection process, such as using resonant frequency shift detection technology to further confirm whether there are metal foreign objects, so that timely measures can be taken to prevent potential safety risks. In addition, the charging environment algorithm can also record and analyze long-term charging data to help optimize charging efficiency, reduce energy loss, and improve the performance of the overall charging system.

[0017] Step S3: If the charging data is abnormal, a preset resonance frequency shift detection technology is used to identify whether there is a metal foreign object in the target charging area based on the second detection signal and the first detection signal.

[0018] Specifically, in the above-mentioned step of "if the charging data is abnormal, the preset resonant frequency shift detection technology is used to identify whether there is a metal foreign body in the target charging area based on the second detection signal and the first detection signal", it should be first clarified that when the charging environment algorithm detects that the charging data is abnormal, such as a sudden change in current or voltage, this may be due to the presence of a foreign body in the charging area. In order to further confirm the specific nature of the foreign body, especially whether it is a metal foreign body, the system will start the preset resonant frequency shift detection technology. The core principle of this technology is to use the influence of metal objects on the electromagnetic field, which is specifically manifested as the metal foreign body causing the resonant frequency of the charging circuit to shift. When the electromagnetic field detector transmits the first detection signal to the target charging area in the target charging plate and receives the second detection signal back, if there is a metal foreign body in the charging area, the metal foreign body will reflect and absorb the electromagnetic wave, thereby causing the resonant frequency of the charging circuit to change. The preset resonant frequency shift detection technology identifies this change by comparing the frequency difference between the first detection signal and the second detection signal. If the detected frequency shift exceeds the normal range, it can be determined that there is a metal foreign body in the charging area. For example, in a wireless charging scenario, the user places the smartphone on the charging plate to start charging. At this time, the charging environment algorithm runs normally and monitors various data during the charging process. Assume that during this process, the user accidentally drops a coin between the mobile phone and the charging plate. As a metal foreign body, this coin will change the electromagnetic field distribution between the charging plate and the mobile phone. The second detection signal received by the detector will have a significant frequency shift compared to the signal when there is no foreign body. By analyzing this change, the charging environment algorithm confirms the presence of metal foreign bodies and triggers subsequent safety response mechanisms, such as suspending charging and issuing alarms, to prevent possible overheating or short circuit phenomena and ensure charging safety. In this way, the resonant frequency shift detection technology can not only accurately identify metal foreign bodies, but also be combined with other detection methods to form a complete foreign body detection system, effectively improving the safety and reliability of the wireless charging system. In addition, the resonant frequency shift detection technology is used because if only image technology is used, some non-metals will be coated with metal colors, which will cause the system to misjudge that non-metals are metals, and some metals will be coated with non-metal colors, which will cause the system to misjudge that metals are non-metals. In order to comprehensively detect, the resonant frequency shift detection technology and image recognition technology are combined.

[0019] Step S4, obtaining the target charging area image corresponding to the target charging area, and identifying whether there is a non-metallic foreign object in the target charging area based on the target charging area image by a preset image recognition algorithm. Specifically, when implementing the step of "obtaining the target charging area image corresponding to the target charging area, and identifying whether there is a non-metallic foreign object in the target charging area based on the target charging area image by a preset image recognition algorithm", it is first necessary to integrate one or more cameras in the wireless charging system, which are responsible for capturing the image of the target charging area. When the system detects the possible presence of foreign objects through the previous steps, the camera will be activated to take a high-definition image of the target charging area, namely the "target charging area image". These images will then be transmitted to the central processing unit of the system, where the preset image recognition algorithm will analyze these images. The image recognition algorithm is usually based on a deep learning model, and after a lot of training, it can accurately identify different types of objects, including non-metallic foreign objects. When the image recognition algorithm receives the image of the target charging area, it will extract feature information in the image, such as shape, color, texture, etc., through a series of complex calculations and analyses, and then compare it with known non-metallic foreign object samples. If the algorithm detects that there is an object in the image that matches the features of a non-metallic foreign object, it can be determined that there is a non-metallic foreign object in the target charging area. For example, in the wireless charging scenario mentioned above, suppose that the user places the smartphone on the charging pad to start charging, and at this time a plastic card accidentally slips between the phone and the charging pad. The image captured by the camera will show the presence of the plastic card. The image recognition algorithm identifies that this is a non-metallic foreign object by analyzing the feature information in the image, thereby confirming that there is a potential risk in the charging area. Once the presence of a non-metallic foreign object is confirmed, the system will take corresponding measures according to the preset safety response strategy, such as suspending charging, issuing an alarm, etc., to prevent overheating or damage to the device caused by foreign objects. In this way, combined with image recognition technology, not only can metal foreign objects be accurately detected, but non-metallic foreign objects can also be effectively identified, further improving the safety and reliability of the wireless charging system. In this way, even in a complex and changeable actual use environment, the system can promptly detect and handle various foreign objects, ensuring that users will not face safety risks while enjoying the convenience of wireless charging.

[0020] Step S5: If there are metal foreign matter and / or non-metal foreign matter in the target charging area, a charging risk level analysis is performed on the target charging area based on the second detection signal corresponding to the metal foreign matter and the first detection signal and / or the target charging area image to obtain a risk coefficient.

[0021] Specifically, when implementing the step of "if there are metal foreign objects and / or non-metal foreign objects in the target charging area, then based on the second detection signal corresponding to the metal foreign objects and the first detection signal and / or the target charging area image, the charging risk level of the target charging area is analyzed to obtain the risk factor", it is first necessary to understand that when the system confirms the presence of metal foreign objects and / or non-metal foreign objects in the target charging area through the detection of the previous steps, the next task is to evaluate the charging risk level caused by these foreign objects. This evaluation process is based on multiple data sources, including the second detection signal and the first detection signal corresponding to the metal foreign objects, and the target charging area image. Specifically, when the system detects the presence of foreign objects, it will collect relevant electromagnetic field detection data, namely the first detection signal and the second detection signal. These signals contain information about the impact of foreign objects on the electromagnetic field, such as changes in signal strength, phase shift, etc. At the same time, the system will also use the target charging area image captured by the camera to analyze the visual features of the foreign objects such as shape, size, and position through an image recognition algorithm. These data will be input into a preset risk level analysis algorithm, which can comprehensively consider electromagnetic field changes and image features to evaluate the potential threat of foreign objects to the charging process. For example, in the wireless charging scenario mentioned above, suppose the user places the smartphone on the charging pad to start charging, and at this time a coin (metallic foreign body) and a plastic card (non-metallic foreign body) accidentally slip between the phone and the charging pad. The system detects the change of the second detection signal through the electromagnetic field detector and confirms the presence of metal foreign bodies; at the same time, the image captured by the camera is analyzed by the image recognition algorithm to confirm the presence of non-metallic foreign bodies. Next, the system will input these data into the hazard analysis algorithm. The algorithm will comprehensively evaluate the impact of these foreign bodies on the charging process based on the degree of electromagnetic field change caused by the metal foreign bodies and the size and location of the non-metallic foreign bodies. For example, if the coin is near the charging coil, it may cause serious electromagnetic interference and overheating risks, and the hazard factor is high; while the plastic card will not cause electromagnetic interference, but if it covers the heat dissipation holes, it may also cause the device to overheat, and the hazard factor is low. Finally, the system will derive a specific hazard factor, which reflects the degree of impact of foreign bodies in the charging area on charging safety. Based on this hazard factor, the system can formulate corresponding safety response strategies, such as pausing charging, reducing charging power, or issuing an alarm, to ensure the safety of the charging process. In this way, the system can not only accurately detect various foreign objects, but also scientifically evaluate the risks brought by these foreign objects, so as to take effective measures to ensure the safety of users and the normal operation of equipment.

[0022] Step S6, obtaining a safety response strategy based on the risk factor.

[0023] Specifically, when implementing the step of "obtaining a safety response strategy based on the risk factor", the system will formulate corresponding safety response measures based on the risk factor calculated previously. Specifically, when the system obtains a specific risk factor by comprehensively analyzing the second detection signal and the first detection signal corresponding to the metal foreign body and the image of the target charging area, different safety measures will be taken according to the different values ​​of this coefficient. For example, in the wireless charging scenario mentioned above, it is assumed that the system detects that there is a coin and a plastic card in the target charging area. Through the risk level analysis, the risk factor obtained is high. In this case, the system will immediately suspend charging to prevent overheating or short circuit caused by metal foreign bodies, and prompt the user to check the charging area through the display or sound, and restart charging after removing the foreign body. On the contrary, if the risk factor is low, such as only the plastic card exists, the system may choose to reduce the charging power, continue charging but closely monitor the temperature change at the same time to ensure safety. In this way, the system can adopt the most appropriate response strategy according to different types of foreign bodies and their impact on the charging process to ensure the safety and reliability of the charging process.

[0024] In a specific embodiment, the transmitting of a first detection signal to a target charging area in a target charging plate and receiving a second detection signal by a preset electromagnetic field detector include: Before charging begins, a multi-band signal is modulated by a preset electromagnetic field detector to obtain a multi-band modulated signal; Performing beamforming on the multi-band modulated signal to obtain a directional detection signal; wherein the directional detection signal is a directional detection signal with directivity; Power-adjusting the directional detection signal to obtain a power-adjusted detection signal; wherein the power-adjusted detection signal is a power-adjusted detection signal adapted to the size of the target charging area; Time-slicing the power adjustment detection signal to obtain first detection signals at multiple time intervals, and transmitting the first detection signal according to the time intervals; In different time intervals, the return signal of the first detection signal is received and reconstructed for the target charging area to obtain a second detection signal.

[0025] Specifically, first, before charging starts, the system will perform multi-band signal modulation through a preset electromagnetic field detector to obtain a multi-band modulated signal. This means that the detector will generate signals at multiple different frequencies, so that the target charging area can be covered more comprehensively, ensuring that foreign objects of different materials and positions can be effectively detected. The use of multi-band signals can increase the richness of the signal and improve the accuracy of subsequent analysis. Next, the multi-band modulated signal is beamformed to obtain a directional detection signal. Beamforming technology adjusts the phase and amplitude of the signal to concentrate the signal in a specific direction, thereby obtaining a directional detection signal with directionality. This directional detection signal can focus more accurately on the target charging area, reduce the interference of background noise, and improve the quality of the signal and the sensitivity of detection. For example, in a wireless charging scenario, assuming that the user places a smartphone on the charging pad, the beamforming technology can ensure that the detection signal is mainly concentrated in the charging area below the mobile phone, rather than the surrounding environment, thereby improving the pertinence of the detection. Subsequently, the directional detection signal is power-adjusted to obtain a power-adjusted detection signal. The purpose of power adjustment is to adapt the strength of the detection signal to the size and characteristics of the target charging area. If the charging area is large, the power of the signal can be appropriately increased to ensure that the signal can cover the entire area; if the charging area is small, the power of the signal can be reduced to reduce energy consumption and avoid unnecessary interference. For example, in the aforementioned wireless charging scenario, if the charging plate is a large desktop charging plate, the system may choose a higher power to ensure that the entire charging area can be effectively detected; if it is a small portable charging plate, the system will choose a lower power to save energy. Next, the power adjustment detection signal is time-sliced ​​to obtain a first detection signal of multiple time intervals, and the first detection signal is transmitted according to the time interval. The time slicing technology divides the continuous detection signal into multiple time intervals, and transmits a detection signal once in each interval. This can improve the time resolution of the signal, so that the system can monitor the changes in the charging area more finely. For example, the system can be set to transmit 10 detection signals per second, and the time interval between each transmission is 100 milliseconds, so that the status of the charging area can be monitored in real time and any abnormalities can be discovered in time. Finally, the return signal of the first detection signal is received and reconstructed for the target charging area at different time intervals to obtain a second detection signal. When the first detection signal is transmitted to the target charging area, if there is a foreign object, the signal will be reflected or scattered to form a return signal. The detector receives these return signals at different time intervals and reconstructs them through signal processing technology to obtain a second detection signal. By analyzing the second detection signal, it can be determined whether there is a foreign object in the charging area and its specific location.For example, in a wireless charging scenario, if the system detects a significant change in the strength or phase of the return signal within a certain time interval, it can preliminarily determine that there is a foreign object in the charging area, thereby triggering subsequent foreign object detection and safety response mechanisms.

[0026] In a specific embodiment, the monitoring of charging data of the target charging plate to the device to be charged based on the second detection signal by using a preset charging environment algorithm includes: Extracting spectrum features of the second detection signal based on a preset signal spectrum analyzer to obtain spectrum detection signal features; By using a preset feature change detector, continuous feature change recognition is performed on the features of the spectrum detection signal to obtain a feature recognition result; If the feature recognition result exceeds the preset feature change threshold range, the feature change data is evaluated through the preset charging environment algorithm to obtain the preliminary abnormality degree; Based on the preliminary abnormality level, the charging data of the target charging plate to the device to be charged is monitored; wherein the charging data includes the charging voltage and the charging current.

[0027] Specifically, first, the system needs to extract the spectrum features of the second detection signal based on a preset signal spectrum analyzer to obtain the spectrum detection signal features. The signal spectrum analyzer is a tool that can convert time domain signals into frequency domain signals. By performing spectrum analysis on the second detection signal, the features of the signal at different frequencies, such as signal strength, frequency distribution, etc., can be extracted. These spectrum features can reflect the changes in the electromagnetic environment in the charging area and provide basic data for subsequent foreign object detection. For example, in a wireless charging scenario, assuming that the user puts the smartphone on the charging plate to start charging, after the system receives the second detection signal through the electromagnetic field detector, the signal spectrum analyzer will process these signals and extract the spectrum features. These spectrum features may include information such as the intensity distribution and phase change of the signal at different frequencies, which can reflect whether the electromagnetic environment in the charging area is normal. Next, the spectrum detection signal features are continuously identified through a preset feature change detector to obtain a feature recognition result. The feature change detector is an algorithm that can monitor the changes in signal features in real time. By continuously analyzing the features of the spectrum detection signal, slight changes in the signal features can be identified. These changes may be caused by foreign objects in the charging area or by other external factors. The feature change detector identifies any abnormal changes by comparing the current spectrum characteristics with historical data, and generates a feature recognition result. For example, in the above wireless charging scenario, suppose a coin accidentally falls between the mobile phone and the charging plate during the charging process. The coin will change the electromagnetic field distribution in the charging area, causing the spectrum characteristics of the second detection signal to change. The feature change detector monitors these changes in real time and generates a feature recognition result, indicating that the electromagnetic environment in the charging area is abnormal. If the feature recognition result exceeds the preset feature change threshold range, the feature change data is evaluated by the preset charging environment algorithm to obtain a preliminary abnormality degree. The preset feature change threshold is a reference value set by the system based on the spectrum characteristics under normal charging conditions. When the feature recognition result exceeds this threshold, the system will consider that there is an abnormality in the charging area. The charging environment algorithm will further analyze the feature change data and evaluate the degree of abnormality. The evaluation of the preliminary abnormality degree can be based on multiple indicators, such as the amplitude of the change in signal strength, the deviation of the frequency distribution, etc. These indicators can reflect the degree of influence of foreign matter on the charging process. For example, in the aforementioned wireless charging scenario, assuming that the feature change detector detects that the change in the spectrum feature exceeds the preset threshold, the charging environment algorithm will further analyze these changes and evaluate the degree of abnormality. If the change in signal strength is large and the deviation in frequency distribution is obvious, the system will judge that the degree of abnormality is high and there may be metal foreign matter; if the change is small and the deviation in frequency distribution is not large, the system will judge that the degree of abnormality is low and there may be non-metal foreign matter or slight environmental interference.Based on the preliminary abnormality, the charging data of the target charging plate to the device to be charged is monitored, wherein the charging data includes the charging voltage and the charging current. The charging environment algorithm monitors the charging voltage and the charging current provided by the charging plate to the device to be charged in real time according to the preliminary abnormality. If the preliminary abnormality is high, the system will monitor the charging data more frequently to ensure the safety of the charging process. The changes in the charging voltage and the charging current can reflect the stability of the charging process. If the charging voltage or the charging current fluctuates abnormally, the system will immediately take corresponding safety measures, such as suspending charging, reducing the charging power or issuing an alarm. For example, in the above wireless charging scenario, assuming that the system evaluates that the preliminary abnormality is high, the charging voltage and the charging current will be monitored in real time. If a sudden increase in the charging current or a large fluctuation in the charging voltage is detected, the system will determine that there is a serious safety risk, immediately suspend charging, and prompt the user to check the charging area through a display or sound, and restart charging after removing foreign objects. On the contrary, if the preliminary abnormality is low, the system may choose to reduce the charging power and continue charging, but at the same time closely monitor temperature changes to ensure safety.

[0028] In a specific embodiment, the identifying whether there is a metal foreign object in the target charging area based on the second detection signal and the first detection signal by using a preset resonant frequency shift detection technology includes: Measuring the resonant frequency of the target charging area based on the second detection signal to obtain a second resonant frequency value; Calculating a first resonant frequency value of the first detection signal; The first resonant frequency value is obtained using the following formula; ; Said is the first resonant frequency value, is the first inductance value of the target charging board, is the first capacitance value of the target charging plate, is the second capacitance value of the device to be charged, is the mutual inductance value, which indicates the coupling strength between the target charging board and the device to be charged; By using a preset resonance frequency offset detection technology, a resonance frequency offset value is compared and calculated based on the second resonance frequency value and the first resonance frequency value to obtain a resonance frequency offset value; The resonance frequency offset value is compared and calculated with a preset resonance frequency offset threshold value to obtain a comparison calculation result; wherein the comparison calculation result is used to determine whether there is a metal foreign object in the target charging area.

[0029] Specifically, first, the resonant frequency of the target charging area is measured based on the second detection signal to obtain the second resonant frequency value. The second detection signal here refers to a specific signal sent by the system during the wireless charging process, which is used to detect the resonant frequency in the charging area. The key to this step is to ensure the accuracy and reliability of the measurement, because any error may affect subsequent judgments. For example, in a typical wireless charging scenario, the user places the mobile phone on the wireless charging pad. At this time, the charging pad will send out a second detection signal, and the resonant frequency of the current charging area, that is, the second resonant frequency value, is measured through this signal. Next, the first resonant frequency value of the first detection signal is calculated. The first detection signal refers to the signal sent by the system under ideal conditions without interference from metal foreign matter. In order to calculate the first resonant frequency value, the following formula can be used: Said is the first resonant frequency value, is the first inductance value of the target charging board, is the first capacitance value of the target charging plate, is the second capacitance value of the device to be charged, is the mutual inductance value, which indicates the coupling strength between the target charging plate and the device to be charged; this formula calculates the resonant frequency under ideal conditions by considering the inductance, capacitance and mutual inductance values ​​in the system. For example, in the above wireless charging scenario, the inductance and capacitance values ​​between the charging plate and the mobile phone are known, and the first resonant frequency value can be calculated by these parameters. Then, the resonant frequency offset is compared and calculated based on the second resonant frequency value and the first resonant frequency value by using the preset resonant frequency offset detection technology to obtain the resonant frequency offset value. The core of this step is to compare the difference between the actual measured second resonant frequency value and the first resonant frequency value under ideal conditions. The specific calculation method can be a simple difference calculation or a more complex algorithm, such as least squares or Kalman filtering, to improve accuracy. For example, in the above wireless charging scenario, the system compares the actual measured second resonant frequency value with the calculated first resonant frequency value to calculate the resonant frequency offset value. Finally, the resonant frequency offset value is compared and calculated with the preset resonant frequency offset threshold to obtain the comparison calculation result. The purpose of this step is to determine whether the resonant frequency offset value exceeds the preset threshold, so as to determine whether there is a metal foreign body in the target charging area. If the resonant frequency offset value exceeds the preset threshold, the system will determine that there is a metal foreign object in the charging area and take corresponding measures, such as stopping charging or issuing a warning. For example, in the above wireless charging scenario, if the user places a coin on the charging pad, the coin will change the resonant frequency of the charging area, causing the resonant frequency offset value to exceed the preset threshold. The system will immediately stop charging and prompt the user to remove the foreign object. Through the above steps, the method can effectively detect metal foreign objects in the wireless charging area to ensure the safety and reliability of the charging process. This detection mechanism not only improves the user experience, but also reduces the potential safety hazards caused by metal foreign objects. For example, at wireless charging stations in public places, this detection technology can prevent users from mistakenly placing metal objects such as keys and coins on the charging pad, avoiding problems such as overheating and short circuits that may occur during the charging process.

[0030] In a specific embodiment, obtaining a target charging area image corresponding to the target charging area, and identifying whether there is a non-metallic foreign object in the target charging area based on the target charging area image by a preset image recognition algorithm, includes: The target charging area is imaged by a preset camera to obtain an image of the target charging area; Input the target charging area image into a preset image recognition algorithm; wherein the image recognition algorithm includes a feature extraction module, an attention mechanism module, a feature fusion module and a classification recognition module; Inputting the target charging area image into a feature extraction module to perform multi-scale feature extraction to obtain a multi-scale feature image; Inputting the multi-scale feature image into an attention mechanism module for attention enhancement to obtain an attention enhanced feature map; Inputting the attention enhancement feature map into a feature fusion module for feature fusion to obtain a fused feature vector; The fused feature vector is input into a classification and recognition module for classification and recognition to obtain a classification and recognition result; wherein the classification and recognition result is used to determine whether there is a non-metallic foreign object in the target charging area.

[0031] Specifically, first, the system needs to capture the target charging area through a preset camera to obtain the target charging area image. This process involves integrating one or more cameras in the wireless charging device, which can capture the image of the target charging area in real time before or during charging. Specifically, when the user places the smartphone on the charging pad to start charging, the system will activate the preset camera to capture the target charging area on the charging pad. The camera will capture high-definition images of the charging area, which will serve as the basic data for subsequent image recognition. For example, in a wireless charging scenario, assuming that the user places the smartphone on the charging pad, the camera will capture the charging area image under the phone, including the surface of the charging pad, the bottom of the phone, and the space between the two. Next, the system inputs the target charging area image into the preset image recognition algorithm. The image recognition algorithm here is a complex deep learning model, including a feature extraction module, an attention mechanism module, a feature fusion module, and a classification recognition module. These modules work together to complete the analysis and recognition of the target charging area image. First, the target charging area image is input into the feature extraction module for multi-scale feature extraction to obtain a multi-scale feature image. The feature extraction module uses convolutional neural networks (CNN) and other technologies to extract features of different scales from the image. Multi-scale feature extraction can capture local and global information in the image and improve the accuracy and robustness of recognition. For example, in the aforementioned wireless charging scenario, the feature extraction module extracts the texture of the charging plate surface, the contour of the bottom of the mobile phone, and other possible foreign body features from the captured image. Then, the multi-scale feature image is input into the attention mechanism module for attention enhancement to obtain an attention-enhanced feature map. The attention mechanism module enhances the features of key areas by learning the importance of different areas in the image. This can highlight the important information in the image and reduce the interference of background noise. For example, in the aforementioned wireless charging scenario, if there are non-metallic foreign objects such as plastic cards in the image, the attention mechanism module will enhance the features of these foreign object areas to make them easier to identify. Next, the attention-enhanced feature map is input into the feature fusion module for feature fusion to obtain a fused feature vector. The feature fusion module integrates features of different scales and different areas to generate a comprehensive feature vector. This feature vector contains all the key information in the image and provides input for subsequent classification and recognition. For example, in the aforementioned wireless charging scenario, the feature fusion module combines multi-scale features and attention enhancement features to generate a fused feature vector containing information about the charging plate, mobile phone, and foreign objects. Finally, the fused feature vector is input into the classification and recognition module for classification and recognition to obtain the classification and recognition result. The classification and recognition module uses a deep learning model (such as support vector machine, random forest, etc.) to classify the fused feature vector and determine whether there is a non-metallic foreign object in the target charging area.The classification and recognition results can be output as binary labels, such as "non-metallic foreign objects exist" or "non-metallic foreign objects do not exist". For example, in the aforementioned wireless charging scenario, the classification and recognition module will determine whether there are non-metallic foreign objects such as plastic cards in the charging area based on the fused feature vector. If the judgment result is "non-metallic foreign objects exist", the system will take corresponding safety measures, such as suspending charging, reducing charging power or issuing an alarm to prevent potential safety risks. Through the above steps, the system can efficiently and accurately identify non-metallic foreign objects in the target charging area to ensure the safety and reliability of the wireless charging process. This approach not only improves the intelligence level of the system, but also provides users with a more secure charging experience. By combining camera shooting and deep learning image recognition technology, the system can monitor and handle various potential safety hazards in real time during the charging process to ensure the safety and normal operation of the user's device. For example, in a typical wireless charging scenario, the user puts the smartphone on the charging plate to start charging. The system successfully detects the plastic card in the charging area through the image captured by the camera, after multi-scale feature extraction, attention enhancement, feature fusion and classification recognition, and takes safety measures in time to ensure the safety of the charging process.

[0032] In a specific embodiment, if there are metal foreign objects and / or non-metal foreign objects in the target charging area, a charging risk level analysis is performed on the target charging area based on the second detection signal corresponding to the metal foreign objects and the first detection signal and / or the target charging area image to obtain a risk factor, including: If the comparison calculation result is that the offset value exceeds the preset threshold value, it is determined that there is a metal foreign object in the target charging area; Performing feature matching mapping on the resonant frequency offset values ​​corresponding to the second detection signal and the first detection signal through the metal feature parameters in the preset metal foreign body feature library to obtain the properties of the metal foreign body; wherein the properties of the metal foreign body include the type and size of the metal foreign body; Based on the properties of the metal foreign matter, the electromagnetic environment of the target charging area is monitored to obtain electromagnetic field change data; Performing charging hazard analysis on the electromagnetic field change data to obtain a first charging hazard result; If the classification and identification result is that there is a non-metallic foreign object in the target charging area, a charging hazard level analysis is performed on the target charging area based on the target charging area image to obtain a second charging hazard level result; Performing vector conversion on the first charging danger level result to obtain a first conversion vector; Performing vector transformation on the second charging danger level result to obtain a second transformation vector; Constructing a matrix by using the first transformation vector and the second transformation vector from left to right or from top to bottom to obtain a constructed matrix; When there is a metal foreign body, the first conversion vector is input into a preset foreign body hazard algorithm to calculate the hazard coefficient to obtain the hazard coefficient of the metal foreign body; When non-metallic foreign matter exists, the second conversion vector is input into a preset foreign matter hazard algorithm to calculate the hazard coefficient, thereby obtaining the hazard coefficient of the non-metallic foreign matter; When metallic foreign matter and non-metallic foreign matter exist, the constructed matrix is ​​input into a preset foreign matter hazard algorithm to calculate the hazard coefficient to obtain a comprehensive hazard coefficient, and the hazard coefficient of the metallic foreign matter, the hazard coefficient of the non-metallic foreign matter and the comprehensive hazard coefficient are used as the hazard coefficient.

[0033] Specifically, first, the system needs to determine whether there is a metal foreign body in the target charging area. This process is based on the resonant frequency offset detection technology of the previous module. Specifically, if the comparison calculation result is that the offset value exceeds the preset threshold, it is determined that there is a metal foreign body in the target charging area. This means that the system has confirmed that the electromagnetic environment in the charging area has changed significantly through the calculation of the resonant frequency offset value, which may be due to the interference of the metal foreign body. For example, in a wireless charging scenario, suppose that the user puts the smartphone on the charging plate to start charging. After the system receives the second detection signal through the electromagnetic field detector, it performs a resonant frequency offset detection. If the calculated resonant frequency offset value exceeds the preset threshold, the system will determine that there is a metal foreign body in the charging area. Next, the system performs feature matching mapping on the resonant frequency offset values ​​corresponding to the second detection signal and the first detection signal through the metal feature parameters in the preset metal foreign body feature library to obtain the properties of the metal foreign body. The metal foreign body feature library is a database containing characteristic parameters such as the type and size of various metal foreign bodies. The system can identify the specific type and size of the metal foreign body by comparing the detected resonant frequency offset value with the parameters in the feature library. For example, the system may identify that the metal foreign object in the charging area is a coin with a diameter of about 2 cm. Based on the properties of the metal foreign object, the system monitors the electromagnetic environment of the target charging area and obtains electromagnetic field change data. This process involves real-time monitoring of electromagnetic field changes in the charging area and recording relevant data. These data can reflect the degree of influence of metal foreign objects on the charging process. For example, the system monitors parameters such as electromagnetic field strength and frequency distribution in the charging area and records the changes in these parameters. The charging hazard level analysis is performed on the electromagnetic field change data to obtain a first charging hazard level result. The charging hazard level analysis algorithm evaluates the potential threat of metal foreign objects to the charging process based on the electromagnetic field change data. For example, if the electromagnetic field strength increases significantly and the frequency distribution is abnormal, the system will determine that the charging hazard level is high and there may be a risk of overheating or short circuit. If the classification and recognition result is that there are non-metallic foreign objects in the target charging area, the charging hazard level analysis of the target charging area is performed based on the image of the target charging area to obtain a second charging hazard level result. This process involves the classification and recognition results of the image recognition module. If the system determines that there are non-metallic foreign objects in the charging area, it will further analyze the impact of these foreign objects on the charging process. For example, the system will identify that the non-metallic foreign object in the charging area is a plastic card with a size of approximately 5 cm x 8 cm. The first charging hazard level result is vector-converted to obtain a first conversion vector. This process converts the charging hazard level result into a vector form to facilitate subsequent mathematical operations. For example, the first charging hazard level result is a numerical value, such as 0.8, indicating a high degree of hazard, and the system will convert it into a vector form, such as [0.8].The second charging danger level result is vector-converted to obtain a second conversion vector. This process is similar to the above, and the second charging danger level result is converted into a vector form. For example, the second charging danger level result is a numerical value, such as 0.3, indicating a low danger level, and the system will convert it into a vector form, such as [0.3]. The first conversion vector and the second conversion vector are constructed from left to right or from top to bottom to obtain a constructed matrix. This process combines two vectors into a matrix for comprehensive analysis. For example, if the first conversion vector is [0.8] and the second conversion vector is [0.3], the system will construct a matrix, such as [[0.8], [0.3]]. When there is a metal foreign body, the first conversion vector is input into a preset foreign body danger algorithm to calculate the danger coefficient and obtain the danger coefficient of the metal foreign body. The foreign body danger algorithm is a preset mathematical model used to evaluate the danger level of different types of foreign bodies to the charging process. For example, the system will input the first conversion vector [0.8] into the foreign body danger algorithm to calculate the danger coefficient of the metal foreign body, such as 0.9. When there is a non-metallic foreign body, the second conversion vector is input into the preset foreign body hazard algorithm to calculate the hazard coefficient and obtain the hazard coefficient of the non-metallic foreign body. This process is similar to the above. The system will input the second conversion vector [0.3] into the foreign body hazard algorithm to calculate the hazard coefficient of the non-metallic foreign body, such as 0.4. When there are metallic foreign bodies and non-metallic foreign bodies, the construction matrix is ​​input into the preset foreign body hazard algorithm to calculate the hazard coefficient to obtain a comprehensive hazard coefficient, and the hazard coefficient of the metallic foreign body, the hazard coefficient of the non-metallic foreign body and the comprehensive hazard coefficient are used as the hazard coefficient. This process involves inputting the construction matrix [[0.8], [0.3]] into the foreign body hazard algorithm to comprehensively evaluate the total hazard level of the two foreign bodies to the charging process. For example, the system will calculate the comprehensive hazard coefficient to be 0.7, indicating that the charging area is highly dangerous. Through the above steps, the system can efficiently and accurately evaluate the hazard level in the target charging area and ensure the safety and reliability of the wireless charging process. This method not only improves the intelligence level of the system, but also provides users with a more secure charging experience. For example, in a typical wireless charging scenario, the user places the smartphone on the charging pad to start charging. The system detects metal foreign objects and non-metal foreign objects through electromagnetic field detectors and cameras respectively. Through multi-dimensional data analysis, the system can accurately assess the degree of danger in the charging area and take corresponding safety measures, such as pausing charging, reducing charging power or issuing alarms to ensure the safety of the charging process.

[0034] In a specific embodiment, the safety response strategy includes interrupting charging, reducing charging power, and issuing a warning.

[0035] The foreign object detection wireless charging method in the embodiment of the present invention is described above. The foreign object detection wireless charging device in the embodiment of the present invention is described below. Figure 2 , an embodiment of a foreign object detection wireless charging device in an embodiment of the present invention includes: The receiving module 21 is used to transmit a first detection signal to a target charging area in a target charging plate and receive a second detection signal through a preset electromagnetic field detector; A monitoring module 22, configured to monitor charging data of the target charging plate to the device to be charged based on the second detection signal by using a preset charging environment algorithm; A first identification module 23 is used to identify whether there is a metal foreign object in the target charging area based on the second detection signal by using a preset resonance frequency shift detection technology if the charging data is abnormal; The second recognition module 24 is used to obtain a target charging area image corresponding to the target charging area, and identify whether there is a non-metallic foreign object in the target charging area based on the target charging area image through a preset image recognition algorithm; The analysis module 25 is used to analyze the charging risk level of the target charging area based on the second detection signal corresponding to the metal foreign matter and the first detection signal and / or the target charging area image to obtain a risk coefficient if there is a metal foreign matter and / or a non-metal foreign matter in the target charging area; The strategy module 26 is used to obtain a safety response strategy based on the risk factor.

[0036] In this embodiment, for the specific implementation of each unit in the above device embodiment, please refer to the above method embodiment, which will not be repeated here.

[0037] Reference Figure 3 The present invention also provides a computer device in an embodiment, wherein the internal structure of the computer device can be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0038] Those skilled in the art will understand that Figure 3The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0039] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0040] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0041] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0042] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A foreign object detection wireless charging method, characterized in that: The following steps are involved: Transmitting a first detection signal to a target charging area in a target charging plate and receiving a second detection signal through a preset electromagnetic field detector; Monitoring charging data of the target charging plate to the device to be charged based on the second detection signal by using a preset charging environment algorithm; If the charging data is abnormal, identifying whether there is a metal foreign object in the target charging area based on the second detection signal and the first detection signal by using a preset resonance frequency shift detection technology; Acquire a target charging area image corresponding to the target charging area, and identify whether there is a non-metallic foreign object in the target charging area based on the target charging area image by a preset image recognition algorithm; If there are metal foreign objects and / or non-metal foreign objects in the target charging area, a charging risk level analysis is performed on the target charging area based on the second detection signal corresponding to the metal foreign objects and the first detection signal and / or the target charging area image to obtain a risk coefficient; A safety response strategy is obtained based on the risk factor.

2. The foreign object detection wireless charging method according to claim 1, characterized in that: The method of transmitting a first detection signal to a target charging area in a target charging plate and receiving a second detection signal by a preset electromagnetic field detector includes: Before charging begins, a multi-band signal is modulated by a preset electromagnetic field detector to obtain a multi-band modulated signal; Performing beamforming on the multi-band modulated signal to obtain a directional detection signal; wherein the directional detection signal is a directional detection signal with directivity; Power-adjusting the directional detection signal to obtain a power-adjusted detection signal; wherein the power-adjusted detection signal is a power-adjusted detection signal adapted to the size of the target charging area; Time-slicing the power adjustment detection signal to obtain first detection signals at multiple time intervals, and transmitting the first detection signal according to the time intervals; In different time intervals, the return signal of the first detection signal is received and reconstructed for the target charging area to obtain a second detection signal.

3. The foreign object detection wireless charging method according to claim 1, characterized in that: The method of monitoring charging data of the target charging plate to the device to be charged based on the second detection signal by using a preset charging environment algorithm includes: Extracting spectrum features of the second detection signal based on a preset signal spectrum analyzer to obtain spectrum detection signal features; By using a preset feature change detector, continuous feature change recognition is performed on the features of the spectrum detection signal to obtain a feature recognition result; If the feature recognition result exceeds the preset feature change threshold range, the feature change data is evaluated through the preset charging environment algorithm to obtain the preliminary abnormality degree; Based on the preliminary abnormality level, the charging data of the target charging plate to the device to be charged is monitored; wherein the charging data includes the charging voltage and the charging current.

4. The foreign object detection wireless charging method according to claim 1, characterized in that: The method of identifying whether there is a metal foreign object in the target charging area based on the second detection signal and the first detection signal by using a preset resonant frequency shift detection technology includes: Measuring the resonant frequency of the target charging area based on the second detection signal to obtain a second resonant frequency value; Calculating a first resonant frequency value of the first detection signal; The first resonant frequency value is obtained using the following formula; ； Said is the first resonant frequency value, is the first inductance value of the target charging board, is the first capacitance value of the target charging plate, is the second capacitance value of the device to be charged, is the mutual inductance value, which indicates the coupling strength between the target charging board and the device to be charged; By using a preset resonance frequency offset detection technology, a resonance frequency offset value is compared and calculated based on the second resonance frequency value and the first resonance frequency value to obtain a resonance frequency offset value; The resonance frequency offset value is compared and calculated with a preset resonance frequency offset threshold value to obtain a comparison calculation result; wherein the comparison calculation result is used to determine whether there is a metal foreign object in the target charging area.

5. The foreign object detection wireless charging method according to claim 4, characterized in that: Acquiring a target charging area image corresponding to the target charging area, and identifying whether there is a non-metallic foreign object in the target charging area based on the target charging area image by a preset image recognition algorithm, including: The target charging area is imaged by a preset camera to obtain an image of the target charging area; Input the target charging area image into a preset image recognition algorithm; wherein the image recognition algorithm includes a feature extraction module, an attention mechanism module, a feature fusion module and a classification recognition module; Inputting the target charging area image into a feature extraction module to perform multi-scale feature extraction to obtain a multi-scale feature image; Inputting the multi-scale feature image into an attention mechanism module for attention enhancement to obtain an attention enhanced feature map; Inputting the attention enhancement feature map into a feature fusion module for feature fusion to obtain a fused feature vector; The fused feature vector is input into a classification and recognition module for classification and recognition to obtain a classification and recognition result; wherein the classification and recognition result is used to determine whether there is a non-metallic foreign object in the target charging area.

6. The foreign object detection wireless charging method according to claim 5, characterized in that: If there are metal foreign objects and / or non-metal foreign objects in the target charging area, a charging risk level analysis is performed on the target charging area based on the second detection signal corresponding to the metal foreign objects and the first detection signal and / or the target charging area image to obtain a risk factor, including: If the comparison calculation result is that the offset value exceeds the preset threshold value, it is determined that there is a metal foreign object in the target charging area; Performing feature matching mapping on the resonant frequency offset values ​​corresponding to the second detection signal and the first detection signal through the metal feature parameters in the preset metal foreign body feature library to obtain the properties of the metal foreign body; wherein the properties of the metal foreign body include the type and size of the metal foreign body; Based on the properties of the metal foreign matter, the electromagnetic environment of the target charging area is monitored to obtain electromagnetic field change data; Performing charging hazard analysis on the electromagnetic field change data to obtain a first charging hazard result; If the classification and identification result is that there is a non-metallic foreign object in the target charging area, a charging hazard level analysis is performed on the target charging area based on the target charging area image to obtain a second charging hazard level result; Performing vector conversion on the first charging danger level result to obtain a first conversion vector; Performing vector transformation on the second charging danger level result to obtain a second transformation vector; Constructing a matrix by using the first transformation vector and the second transformation vector from left to right or from top to bottom to obtain a constructed matrix; When there is a metal foreign body, the first conversion vector is input into a preset foreign body hazard algorithm to calculate the hazard coefficient to obtain the hazard coefficient of the metal foreign body; When non-metallic foreign matter exists, the second conversion vector is input into a preset foreign matter hazard algorithm to calculate the hazard coefficient, thereby obtaining the hazard coefficient of the non-metallic foreign matter; When metallic foreign matter and non-metallic foreign matter exist, the constructed matrix is ​​input into a preset foreign matter hazard algorithm to calculate the hazard coefficient to obtain a comprehensive hazard coefficient, and the hazard coefficient of the metallic foreign matter, the hazard coefficient of the non-metallic foreign matter and the comprehensive hazard coefficient are used as the hazard coefficient.

7. The foreign object detection wireless charging method according to claim 1, characterized in that: The safety response strategies include interrupting charging, reducing charging power, and issuing warnings.

8. A foreign object detection wireless charging device, characterized in that: include: A receiving module, used to transmit a first detection signal to a target charging area in a target charging plate and receive a second detection signal through a preset electromagnetic field detector; A monitoring module, configured to monitor charging data of the target charging plate to the device to be charged based on the second detection signal by using a preset charging environment algorithm; a first identification module, configured to identify whether there is a metal foreign object in the target charging area based on the second detection signal by using a preset resonance frequency shift detection technology if there is an abnormality in the charging data; A second recognition module is used to obtain a target charging area image corresponding to the target charging area, and identify whether there is a non-metallic foreign object in the target charging area based on the target charging area image through a preset image recognition algorithm; an analysis module, configured to analyze the charging risk level of the target charging area based on the second detection signal corresponding to the metal foreign matter and the first detection signal and / or the target charging area image to obtain a risk coefficient if there are metal foreign matter and / or non-metal foreign matter in the target charging area; A strategy module is used to obtain a safety response strategy based on the risk factor.

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

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

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