Wireless charging foreign matter detection method and device, server, medium and program product

Abstract

The invention provides a wireless charging foreign matter detection method and device, a server, a medium and a program product, and the method comprises the steps: receiving the current output data of a wireless charging module, the current output data comprising a current output current and / or a current output voltage; the current output data is compared with historical output data, whether the foreign matter exists or not is judged, and the historical output data comprise data which are historically output by the wireless charging module and are the same as the current output data in type when the foreign matter does not exist. According to the invention, the cost is saved.

Description

Technical Field

[0001] The present invention relates to the technical field of detection, and in particular to a wireless charging foreign object detection method, device, server, medium and program product. Background Art

[0002] Foreign object detection (FOD) is an important part of wireless charging technology. It is mainly used to detect whether there is a metal foreign object between the transmitter (TX) and the receiver (RX) during the charging process, and to take charging interruption measures when a foreign object is detected. Metal foreign objects will generate an induced electromotive force in an alternating magnetic field, forming an induced current, consuming the energy output by the transmitter, and may generate a lot of heat, affecting the efficiency of wireless charging and even causing accidents. Foreign object detection mainly includes metal object detection (MOD) and biological object detection (LOD), which should be implemented during the charging process.

[0003] Chinese patent CN118074356A proposes a "metal foreign body detection device, method and wireless charging system". In the detection of metal foreign bodies in wireless charging, a separate detection coil group is used to detect whether there is a metal foreign body. This structure requires a separate hardware structure design for the detection coil group, which increases the cost. Summary of the invention

[0004] In view of this, an object of the embodiments of the present application is to provide a wireless charging foreign object detection method, which can improve the problem of high cost of the prior art.

[0005] In order to achieve the above technical objectives, the technical solutions adopted in this application are as follows:

[0006] In a first aspect, an embodiment of the present application provides a wireless charging foreign object detection method, the method comprising:

[0007] Receiving current output data of the wireless charging module, where the current output data includes current output current and / or current output voltage;

[0008] The current output data is compared with the historical output data to determine whether there is a foreign object, wherein the historical output data includes historical output data of the wireless charging module of the same type as the current output data when there is no foreign object.

[0009] Further, comparing the current output data with the historical output data to determine whether there is a foreign object includes:

[0010] Calculate the current phase difference according to the current output data;

[0011] If the current phase difference is greater than a phase difference threshold, then a foreign object exists, and the phase difference threshold is calculated based on the historical output data.

[0012] Further, the historical output data includes historical output current and / or historical output voltage;

[0013] The comparing the current output data with the historical output data to determine whether there is a foreign object comprises: comparing the waveform of the historical output current with the waveform of the current output current to obtain a first change degree representing the change degree of the waveform of the current output current relative to the waveform of the historical output current;

[0014] and / or

[0015] Comparing the waveform of the historical output voltage with the waveform of the current output voltage, obtaining a second degree of change representing the degree of change of the waveform of the current output voltage relative to the voltage of the historical output current;

[0016] Whether or not a foreign object is present is determined based on the first change degree and / or the second change degree.

[0017] Further, judging whether there is foreign matter based on the first degree of change and / or the second degree of change includes: when the first degree of change exceeds a first degree threshold, and / or when the second degree of change exceeds a second degree threshold, it is considered that there is foreign matter.

[0018] Furthermore, the method further comprises:

[0019] Collecting the current output data when no foreign matter exists at a preset frequency to form collected data;

[0020] The collected data is input as historical output data into a data pile of a detection model. The detection model is configured to determine whether there is a foreign object based on the historical output data. The detection model is a deep learning model.

[0021] Furthermore, after the current output data is collected at a preset frequency and input into the detection model, the method further includes: sorting all historical output data based on a heap sorting strategy.

[0022] Furthermore, after sorting the collected data and the historical output data based on the heap sorting strategy, the method further includes:

[0023] If the amount of historical output data in the data pile exceeds a limit, the duplicate historical output data is deleted.

[0024] Furthermore, the method also includes: when there is a foreign object, the wireless charging module stops charging and issues a reminder.

[0025] In the second aspect, this example proposes a wireless charging module foreign body detection device, comprising:

[0026] A receiving module, configured to receive current output data of the wireless charging module, wherein the current output data includes a current output current and / or a current output voltage;

[0027] The comparison module is configured to compare the current output data with the historical output data to determine whether there is a foreign object, wherein the historical output data includes data of the same type as the current output data that is historically output by the wireless charging module when there is no foreign object.

[0028] In a third aspect, this embodiment proposes a server, which includes a processor and a memory coupled to each other, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the server executes the above method.

[0029] In a fourth aspect, this embodiment proposes a computer-readable storage medium, in which a computer program is stored. When the computer program runs on a computer, the computer executes the above method.

[0030] In a fifth aspect, this embodiment proposes a computer program product, including a computer program, which implements the above method when executed by a processor.

[0031] The invention adopting the above technical solution has the following advantages:

[0032] In the technical solution provided in the present application, there is no need to detect the power factor size, nor is there any need to adjust the power factor value in real time to determine whether there is a metal foreign body. The determination of whether there is a foreign body is achieved only by comparing the current output data with the historical output data, thereby reducing costs.

[0033] In the technical solution provided in the present application, the collected data is input into the data stack of the detection model, and the data in the data stack can be dynamically updated. At the same time, the detection model is a deep learning model, and the accuracy of the detection model can be continuously improved during the charging process of the mobile phone, thereby continuously improving the accuracy of detecting whether there are foreign objects in wireless charging. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present application may be further described by the non-limiting embodiments given in the accompanying drawings. It should be understood that the following drawings only illustrate certain embodiments of the present application and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings may be obtained based on these drawings without creative effort.

[0035] Figure 1 A flowchart of a method provided in an embodiment of the present application.

[0036] Figure 2This is a structural diagram of the primary transmitting coil provided in an embodiment of the present application.

[0037] Figure 3 The voltage and current waveform diagram provided in the embodiment of the present application.

[0038] Figure 4 A flow chart for determining whether a foreign object is present is provided in an embodiment of the present application.

[0039] Figure 5 A flow chart for determining whether a foreign object is present is provided in an embodiment of the present application.

[0040] Figure 6 A flow chart for determining whether a foreign object is present is provided in an embodiment of the present application.

[0041] Figure 7 This is a diagram of the device structure provided in an embodiment of the present application.

[0042] Icons: 1-receiving module, 2-comparison module, 3-collection module, 4-alarm module. DETAILED DESCRIPTION

[0043] The present application will be described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that in the drawings or descriptions, similar or identical parts use the same figure numbers, and the implementation methods not shown or described in the drawings are forms known to ordinary technicians in the relevant technical field. In the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0044] The foreign body detection method for the wireless charging module during the charging process can be based on the resonant circuit model. When a metal foreign body is present, it will absorb the energy of the resonant cavity, causing the system quality factor (Q value) to decrease. By detecting the change in the Q value, it can be determined whether there is a foreign body. Another detection method is to measure the power loss (PLoss). If there is a metal foreign body in the transmission magnetic field, additional losses will be generated. By calculating the difference between the input power and the output power, it can be determined whether there is a metal foreign body. The existing technology proposes a method that combines hyperspectral imaging and machine learning to improve the detection capability of metal foreign bodies. This method can not only detect tiny metal foreign bodies, but also has the potential to detect wrapped metal foreign bodies.

[0045] Through these methods, wireless charging devices can effectively detect and respond to metal foreign objects during the charging process, thereby ensuring user safety and device reliability.

[0046] In the design of wireless charging system, the electromagnetic characteristics analysis of metal foreign body detection coil is the key. It is necessary to consider the structural form, structural parameters and combination method of the coil to improve the detection sensitivity and accuracy.

[0047] The embodiment of the present application provides a server that may include a processing module and a storage module. The storage module stores a computer program, and when the computer program is executed by the processing module, the server can execute the corresponding steps in the following wireless charging foreign object detection method.

[0048] Example 1

[0049] Please refer to Figure 1 This embodiment provides a wireless charging foreign object detection method. The wireless charging foreign object detection method may include the following steps:

[0050] Step 110, receiving current output data of the wireless charging module in a current time period, where the current output data includes a current output current and / or a current output voltage;

[0051] Step 120, compare the current output data with the historical output data to determine whether there is a foreign object, the historical output data includes the same type of data as the current output data that the wireless charging module historically output when there is no foreign object. In this embodiment, the historical output current and the historical output voltage.

[0052] The structure of the wireless charging module in this example is based on the topology of the primary transmitting coil. According to the coil state of the existing wireless charging module, which is mainly three coils connected in parallel, the three coils can be controlled separately so that each coil has a separately connected topology.

[0053] like Figure 2 As shown in the figure, this is the primary topology of one of the coils. In this topology, the switch circuit is connected to a transformer to protect the primary circuit. This is used to ensure that when the primary and secondary coil circuits are short-circuited, the switch circuit and power supply are protected, so that even if a short circuit occurs, the primary switch circuit and the fuse will not be burned out. The transformer is connected to a circuit that is a deformation of the traditional LCC topology to ensure that the instantaneous voltage near the primary topology switch tube and the coil is not too large, causing the switch to be burned through. Switch S1 is controlled by BDC to ensure that the chip can control the opening and closing of the coil at any time. The circuits of the other two coils and Figure 2 The same is true, or the transformer connected to the switching tube is connected to LP2 and LP3.

[0054] In this embodiment, collecting the current and voltage of the wireless charging module usually involves multiple steps and components to ensure the accuracy and reliability of the data. The following are some of the main methods and steps:

[0055] 1. Current collection

[0056] Using current transformers:

[0057] A current transformer is a commonly used current acquisition device that can measure the current in a circuit without contact. In a wireless charging system, a current transformer can be connected in series in the current path of the wireless charging module to measure the current value in real time.

[0058] High-precision current sensor:

[0059] The current in the wireless charging module can be accurately measured using high-precision current sensors such as Hall effect sensors or fluxgate sensors. These sensors generally have the characteristics of low noise, high linearity and wide measurement range.

[0060] Signal processing and conversion:

[0061] Convert the analog signal collected by the current sensor into a digital signal for subsequent processing and recording. This can be achieved through an ADC (analog to digital converter). The accuracy and sampling rate of the ADC will affect the accuracy of the current data.

[0062] 2. Voltage Collection

[0063] Direct measurement method:

[0064] Use a voltmeter or multimeter to directly measure the output voltage of the wireless charging module. This method is simple and direct, but it may be affected by the measurement tool and measurement environment.

[0065] Voltage divider circuit:

[0066] A voltage divider circuit is set at the output end of the wireless charging module to reduce the output voltage to a range acceptable to the ADC. Then, the ADC is used to measure the divided voltage and the original voltage value is obtained by calculation.

[0067] High-precision voltage sensor:

[0068] Use a high-precision voltage sensor to directly measure the output voltage of the wireless charging module. These sensors are usually characterized by high accuracy, low noise and fast response.

[0069] 3. Comprehensive acquisition system

[0070] Integrated acquisition module:

[0071] Use modules with integrated current and voltage acquisition functions, such as some dedicated wireless charging monitoring modules. These modules usually integrate high-precision current and voltage sensors, ADC, and data processing functions, and can be easily integrated into wireless charging systems.

[0072] Data acquisition and processing system:

[0073] Build a data acquisition and processing system, including data acquisition hardware (such as ADC module, microcontroller, etc.) and data processing software (such as MATLAB, LabVIEW, etc.). Realize real-time acquisition, processing and display of current and voltage data through programming.

[0074] In S120 , the principle of detecting metallic foreign matter is first described.

[0075] like Figure 3 As shown: According to the state shown in the figure, when metal foreign objects such as iron and nickel are placed in the coupling mechanism, the parameters of the coupling mechanism will change, resulting in a decrease in its mutual inductance, while affecting the charging efficiency and voltage and current parameters, resulting in reduced efficiency, inconsistent voltage and current phases, increased heat generation of the switch tube, and affecting the life of the switch tube.

[0076] According to the characteristics of the LCC-S topology, this is a constant voltage output topology. When there is current input, the voltage and current are in phase under resonance, and the efficiency is the highest and the heat generation is the lowest.

[0077] According to the formula: V IN / V OUT =(LFA1-1 / (4π2F2(CFP1+CFP2))) / M;

[0078] According to the above formula, it can be deduced that if the input voltage remains unchanged and the system frequency remains unchanged, when the M value decreases, VOUT decreases, resulting in a decrease in output power, and at the same time, the real-time waveform phases of the voltage and current are inconsistent, causing the voltage phase to exceed a certain position of the current phase, and the current waveform is distorted, so that the value of the same position of the voltage waveform corresponding to the 0-point switching value of the current waveform is not 0. Placing metal foreign matter on the wireless charging module will cause the magnetic field strength around the wireless charging module to change, increasing the value of the mutual inductance M. Due to the addition of metal foreign matter, the magnetic field generates eddy currents on the metal and generates heat, which will lead to energy waste and reduce charging efficiency.

[0079] Therefore, if the wireless charging module is in the process of charging, it will cause abnormal changes in current, voltage and phase difference.

[0080] In S120, a series of normal voltage waveforms, current waveforms, and phase difference data of the wireless charging module when there is no metal foreign matter need to be collected as calibration data, and the calibration data is input into the detection model as historical output data. Then, based on the calibration data, the system establishes an initial model, and the range of the calibrated voltage waveform data when there is no foreign matter, the range of the current waveform, and the corresponding threshold setting are input into the initial model to form a detection model. The detection model is used to compare the historical output data after receiving the current output data in S110, and determine whether there is a foreign matter based on the comparison result. If there is a foreign matter, the wireless charging module is controlled to stop charging through the PLC or other control modules, and a reminder is issued.

[0081] like Figure 4 As shown, in at least one embodiment, the comparison method of historical output data and current output data is specifically as follows:

[0082] S121: Calculate the current phase difference according to the current output data;

[0083] S122: Determine the magnitude of the current phase difference and a phase difference threshold, where the phase difference threshold is calculated based on historical output data;

[0084] S123: If the current phase difference is greater than the phase difference threshold, there is a foreign object;

[0085] S124: Otherwise, there is no foreign matter.

[0086] In at least one embodiment, Figure 5 As shown, the comparison method between historical output data and current output data is as follows:

[0087] S121: Compare the waveform of the historical output current with the waveform of the current output current to obtain a first change degree representing the change degree of the waveform of the current output current relative to the waveform of the historical output current;

[0088] S122: Compare the first change degree with a first degree threshold;

[0089] S123: If the first change degree exceeds the first degree threshold, foreign matter exists;

[0090] S124: If the first change degree does not exceed the first degree threshold, there is no foreign matter.

[0091] In this embodiment, the degree of change between the current current waveform and the historical current waveform can be determined by the following method, which can be analyzed and compared through the following parameters:

[0092] 1. Total Harmonic Current Distortion (THD)

[0093] Definition: THD is an important parameter that describes the degree of current waveform distortion in the power grid. It is defined as the ratio of the total harmonic current effective value to the fundamental current effective value, usually expressed as a percentage. THD measures the degree of deviation of the current waveform from a pure sine waveform.

[0094] Calculation formula: THDi = (√ (i22 + i3 2 +...+in2)) / i1×100%, where in represents the effective value of the nth harmonic current, and i1 represents the effective value of the fundamental current.

[0095] Application: By comparing the THD values ​​at different time points, the change in current waveform distortion can be evaluated. The higher the THD value, the more severe the current waveform distortion is and the greater the difference from the pure sine waveform.

[0096] 2. Current ripple coefficient

[0097] Definition: The current ripple factor is used to measure the power quality of a device under different load conditions. It reflects the ripple degree of the current curve, that is, the degree of fluctuation of the current waveform.

[0098] Calculation formula: Current ripple factor B = (I1-I2) / I, where I1 is the maximum peak value in the current waveform, I2 is the minimum valley value, and I is the average current.

[0099] Application: Although the current ripple factor is mainly used to measure the ripple degree of the current waveform rather than directly compare the historical waveform, the change in the stability of the current waveform can be indirectly reflected by monitoring and recording the current ripple factor at different time points.

[0100] 3. Waveform similarity or correlation

[0101] Concept: In order to more directly reflect the degree of change between the current waveform and the historical current waveform, the similarity or correlation between the two can be calculated. This usually involves comparison and mathematical analysis of waveform data.

[0102] Method: You can use correlation coefficient, Euclidean distance, dynamic time warping (DTW) and other methods to calculate the similarity or distance between two waveforms. The closer the correlation coefficient is to 1, the more similar the two waveforms are; the smaller the Euclidean distance is, the closer the two waveforms are; DTW can handle the expansion and displacement of waveforms on the time axis.

[0103] 4. Spectrum Analysis

[0104] Method: By performing spectrum analysis on the current waveform, the spectrum distribution of the current waveform can be obtained, including the amplitude and phase information of each harmonic.

[0105] Application: By comparing the spectrum analysis results at different time points, the changes in the harmonic components in the current waveform can be observed, thereby reflecting the degree of change in the current waveform.

[0106] The first degree of change can be calculated through the above four methods. When the first degree of change is greater than the first degree threshold, it means that the current output current waveform is greatly distorted from the historical current waveform, and there is a high possibility of metal foreign matter. Therefore, it is judged that there is metal foreign matter. This is because wireless charging mainly relies on the principle of electromagnetic induction, that is, the transmitter (usually the charging base) generates an alternating magnetic field, and the receiver (such as the wireless charging coil on the back of the mobile phone) senses this magnetic field and generates current through electromagnetic induction, thereby charging the device. When there is a metal foreign matter, eddy current effect and electromagnetic field interference are easily generated, which will cause the current waveform in the wireless charging module to change.

[0107] Moreover, due to the presence of metal foreign matter, the wireless charging module needs to adjust its working state to cope with this interference, which may cause fluctuations or distortions in the current waveform. In addition, wireless charging devices usually have a certain self-protection mechanism. When abnormal changes in the current waveform are detected, the device may automatically adjust the output power or take other measures to protect itself from damage. This system response will also be reflected in the change of the current waveform. Therefore, when the current current waveform varies greatly from the historical current waveform, it is likely caused by the presence of metal foreign matter in the wireless charging module. The eddy current effect and electromagnetic field interference of metal foreign matter will directly affect the electromagnetic field distribution and current waveform of the wireless charging device, thereby causing abnormal changes in the current waveform. Therefore, during the wireless charging process, if the current waveform is found to have changed significantly, it should be checked in time whether there are metal foreign matter in the charging area, and corresponding measures should be taken to ensure the safety and efficiency of wireless charging.

[0108] In at least one embodiment, Figure 6 As shown, the comparison method between historical output data and current output data is as follows:

[0109] S121: Compare the waveform of the historical output voltage with the waveform of the current output voltage to obtain a second change degree representing the change degree of the waveform of the current output voltage relative to the waveform of the historical output voltage;

[0110] S122: Comparing the second change degree with the second degree threshold;

[0111] S123: If the second change degree exceeds the second degree threshold, foreign matter exists;

[0112] S124: If the second change degree does not exceed the second degree threshold, there is no foreign matter.

[0113] The parameters that reflect the degree of change between the current voltage waveform and the historical voltage waveform are mainly the following:

[0114] 1. Voltage Harmonic Distortion Rate

[0115] Definition: Voltage waveform distortion rate, also known as voltage sine wave distortion rate or voltage harmonic distortion rate, is an indicator to measure the degree to which the voltage waveform deviates from the sine wave. In the power system, due to the existence of a large number of power supply and power consumption equipment with nonlinear impedance characteristics, the actual voltage waveform may deviate from the ideal sine waveform.

[0116] Calculation method: Usually the voltage waveform is decomposed into the fundamental wave and each harmonic through Fourier analysis, and then the ratio of the effective value of each harmonic to the effective value of the fundamental wave is calculated. Finally, the voltage waveform distortion rate is obtained by synthesizing them in a certain way (such as the root mean square method).

[0117] Application: The greater the voltage waveform distortion rate, the greater the difference between the voltage waveform and the sine wave, that is, the greater the degree of change between the current voltage waveform and the historical (or ideal) voltage waveform.

[0118] 2. Voltage fluctuation and flicker

[0119] Voltage fluctuation: refers to the phenomenon that the voltage changes sharply and deviates from the rated value within a certain period of time. When the rate of voltage change is greater than 1% per second, it can be regarded as a sharp voltage change.

[0120] Voltage flicker: The phenomenon that the luminous flux of an electric light source fluctuates sharply due to periodic rapid changes in voltage, causing visual discomfort to the human eye.

[0121] Application: The magnitude of voltage fluctuation and flicker can reflect the instability of the voltage waveform in a short period of time, thereby indirectly reflecting the degree of change between the current voltage waveform and the historical voltage waveform.

[0122] 3. Negative sequence voltage coefficient

[0123] Definition: The negative-sequence voltage coefficient is used to indicate the degree of three-phase voltage imbalance and is usually expressed as a percentage of the effective value of the negative-sequence voltage of the three-phase fundamental wave to the effective value of the rated voltage.

[0124] Application: In a three-phase power system, if the three-phase voltage is unbalanced (i.e., the negative sequence voltage coefficient is large), it means that there are significant differences in the voltage waveforms between the three phases, which can also be used as an indicator to reflect the degree of change of the current voltage waveform from the historical (or ideally balanced) voltage waveform.

[0125] 4. Waveform comparison parameters

[0126] Direct comparison: In some cases, the degree of change can be assessed by directly comparing the waveform diagram or waveform data of the current voltage waveform with the historical voltage waveform. This includes comparison of factors such as the peak value, valley value, average value, effective value, and waveform shape of the waveform.

[0127] Quantitative indicators: In order to more accurately quantify the degree of waveform change, some quantitative indicators can be introduced, such as waveform similarity, waveform difference, etc. These indicators usually need to be obtained through complex algorithms and calculations.

[0128] Voltage waveform distortion rate, voltage fluctuation and flicker, negative sequence voltage coefficient and waveform comparison parameters can reflect the second change degree to a certain extent. In practical applications, appropriate parameters should be selected for evaluation according to specific circumstances.

[0129] The second degree of change is calculated by the voltage waveform distortion rate, voltage fluctuation and flicker, negative sequence voltage coefficient and waveform comparison parameters. When the second degree of change is greater than the second degree threshold, it means that the current output voltage waveform is greatly distorted from the historical voltage waveform, and there is a high possibility of metal foreign matter. Therefore, it is judged that there is metal foreign matter. This is because when the current voltage waveform is greatly changed from the historical voltage waveform, the reasons why there may be metal foreign matter in the wireless charging module can be mainly explained from the following aspects:

[0130] 1. Principle of electromagnetic induction

[0131] The basic principle of wireless charging modules, especially electromagnetic induction wireless charging, is that the changing current flowing through the coil generates a changing magnetic field, and the adjacent paired coils couple to generate a corresponding voltage for charging. When there is a metal foreign body in the wireless charging area, the metal object will become a new conductor and participate in the coupling process of the electromagnetic field. This additional participation will change the original electromagnetic field distribution, thereby affecting the shape and characteristics of the voltage waveform.

[0132] 2. Electromagnetic effect of metal foreign matter

[0133] Metallic foreign matter will produce a variety of electromagnetic effects in the electromagnetic field of wireless charging, including eddy current effect, hysteresis effect, etc. These effects will cause the metal foreign matter to heat up and may change the impedance, inductance and other parameters of the coil in the wireless charging system. The changes in these parameters will further affect the shape and size of the voltage waveform, making the current voltage waveform significantly different from the historical voltage waveform when there is no foreign matter.

[0134] 3. Foreign object detection mechanism of wireless charging module

[0135] In order to ensure the safety and efficiency of wireless charging, modern wireless charging modules are usually equipped with a foreign object detection function. This function determines whether there is a metal foreign object in the wireless charging area by monitoring the changes in parameters such as voltage, current, and power loss. When a significant change in the voltage waveform is detected compared to the historical waveform, the system may believe that this is caused by the presence of a metal foreign object and trigger the foreign object detection mechanism accordingly.

[0136] 4. Security and stability considerations

[0137] Metallic foreign matter will not only change the voltage waveform during wireless charging, but may also threaten the safety and stability of the wireless charging system. For example, metal objects absorb energy and generate heat. Long-term absorption of energy may cause the surface temperature to continue to rise, leading to safety accidents such as fire. In addition, the presence of metallic foreign matter may also affect the transmission efficiency and stability of wireless charging, reducing the user experience.

[0138] To sum up, when the current voltage waveform varies greatly from the historical voltage waveform, the reason why there may be metal foreign matter in the wireless charging module is mainly due to the interference of the metal foreign matter on the electromagnetic field and the foreign matter detection mechanism of the wireless charging module itself.

[0139] In at least one embodiment, the comparison method of historical output data and current output data is specifically as follows:

[0140] S121: comparing the waveform of the historical output current with the waveform of the current output current to obtain a first change degree representing the change degree of the waveform of the current output current relative to the waveform of the historical output current, and comparing the waveform of the historical output voltage with the waveform of the current output voltage to obtain a second change degree representing the change degree of the waveform of the current output voltage relative to the waveform of the historical output voltage;

[0141] S122: comparing the first change degree with a first degree threshold, and comparing the second change degree with a second degree threshold;

[0142] S123: If the first change degree exceeds the first degree threshold, and the second change degree exceeds the second degree threshold, then there is a foreign object;

[0143] S124: If the first change degree does not exceed the first degree threshold or the second change degree does not exceed the second degree threshold, there is no foreign matter.

[0144] This embodiment performs initial calibration learning through an algorithm based on the charging and heating principles of wireless charging, and then compares the subsequent output voltage and current related information with the learned calibration value. If it is within the calibration range, it is considered that there is no metal foreign matter, otherwise, it is considered that there is metal foreign matter.

[0145] In this embodiment, when collecting the current output data of the current time period in S110, the current output data when no foreign matter exists is collected at a specific frequency to form collected data; the collected data is input into the data pile of the detection model as historical output data.

[0146] In this embodiment, each time the car is powered on and off, it is regarded as a cycle. In each cycle, the current output data collected is continuously input into the detection model with a specific spectrum, and the collected data is input into the detection model as historical output data. The historical output data in this embodiment includes the historical output voltage, the historical output current, and the phase relationship between the historical output voltage and the historical output current. Each set of data corresponds to each other in the stack sort. At the same time, in each cycle, every certain time (for example, 3 minutes), in this embodiment, the certain time is set as a preset frequency, and the system retains a detection array and updates it to the data stack. When the data inside the data stack exceeds a certain limit, the system deletes the repeated arrays and only retains one group. At the same time, the data stack is recompiled according to the arithmetic difference data length. Each time it is recompiled, the accuracy is increased by one level, and then it is recompiled again. Until the data reaches the limit of normal operation. At this time, the data begins to be compiled again.

[0147] The detection model in this embodiment is a deep learning model with self-learning capabilities, such as a convolutional neural network model, a residual network, etc.

[0148] In this embodiment, the stack sort is used to implement the classification of input information, and is mainly used to classify voltage, current, and phase signals.

[0149] In this embodiment, after heap sorting, the data groups are detected and saved and arranged in order of size. When a data group is collected, this data group is compared with the saved data group. If the collected data is within the range of the saved data group, there is no metal foreign matter. Otherwise, it is considered that there is metal foreign matter.

[0150] In this embodiment, during the comparison and addition of the heap classification of the data group, a basic classification needs to be performed through an algorithm, and the algorithm is as follows:

[0151] defheapify(arr,n,i):

[0152] largest=i

[0153] left=2*i+1

[0154] right=2*i+2

[0155] #Check if the left subtree exists and whether its value is greater than the current node

[0156] ifleft <n and arr[i]<arr[left]:

[0157] largest=left

[0158] #Check if the right subtree exists and whether its value is greater than the largest node

[0159] ifright <n and arr[largest]<arr[right]:

[0160] largest=right

[0161] #If the root node is not the maximum value, swap it with the node with the maximum value

[0162] iflargest! =i:

[0163] arr[i], arr[largest]=arr[largest], arr[i]

[0164] # Recursively heapify subtrees

[0165] heapify(arr,n,largest)

[0166] defheap_sort(arr):

[0167] n=len(arr)

[0168] #Build the maximum heap

[0169] for i in range(n / / 2-1,-1,-1):

[0170] heapify(arr,n,i)

[0171] # Extract the largest element from the heap and re-heapify it

[0172] for i in range(n-1,0,-1):

[0173] arr[i],arr[0]=arr[0],arr[i]#Exchange the current maximum value to the end

[0174] heapify(arr,i,0)

[0175] #Test heap sort

[0176] arr = [12,11,13,5,6,7]

[0177] heap_sort(arr)

[0178] print("Sorted array is:",arr)

[0179] The code first defines a heapify function, which is responsible for heapifying the elements in the array into a maximum heap. Then it defines the heap_sort function, which first builds the maximum heap, then reduces the size of the heap by swapping the current maximum element (the root node of the heap) with the last element, and re-heapifies the remaining elements.

[0180] The time complexity of heap sort is O(n\log n), where n is the length of the array. It is not a stable sorting algorithm because it may change the initial order of the same elements.

[0181] The learned heap array is used as a complete binary tree, and the binary tree is traversed to compare the values ​​of the input array and the array in the binary tree to obtain the conclusion.

[0182] #Heap binary tree node definition

[0183] class TreeNode:

[0184] def__init__(self,value=0,left=None,right=None):

[0185] self.value = value

[0186] self.left=left

[0187] self.right=right

[0188] #Pre-order traversal (root-left-right)

[0189] defpreorder_traversal(root):

[0190] ifroot is None:

[0191] return

[0192] print(root.value)

[0193] preorder_traversal(root.left)

[0194] preorder_traversal(root.right)

[0195] #Heap array binary tree

[0196] root=TreeNode(1,TreeNode(2),TreeNode(3))

[0197] preorder_traversal(root)

[0198] Example 2

[0199] Please refer to Figure 7 The present application also provides a wireless charging module foreign body detection device, based on the wireless charging foreign body detection method described in Example 1. The wireless charging module foreign body detection device includes at least one software function module that can be stored in the storage module or fixed in the operating system (OS) in the form of software or firmware. The processing module is used to execute the executable module stored in the storage module, such as the software function module and computer program included in the wireless charging module foreign body detection device.

[0200] The wireless charging module foreign body detection device includes a receiving module 1 and a comparing module 2, and the functions of each unit can be as follows:

[0201] The receiving module 1 is configured to receive current output data of the wireless charging module in a current time period, where the current output data includes a current output current and a current output voltage;

[0202] The comparison module 2 is configured to compare the current output data with the historical output data to determine whether there is a foreign object, and the historical output data includes the historical output current and the historical output voltage of the wireless charging module when there is no foreign object.

[0203] The comparison module 2 is also configured to determine whether there is a foreign object according to the following method, specifically:

[0204] Calculate the current phase difference according to the current output data;

[0205] If the current phase difference is greater than the phase difference threshold, there is a foreign object. The phase difference threshold is calculated based on the historical output data.

[0206] The comparison module 2 is also configured to determine whether there is a foreign object according to the following method, specifically:

[0207] Comparing the waveform of the historical output current with the waveform of the current output current to obtain a first change degree representing the change degree of the waveform of the current output current relative to the waveform of the historical output current; and / or

[0208] Comparing the waveform of the historical output voltage with the waveform of the current output voltage, obtaining a second change degree representing the change degree of the waveform of the current output voltage relative to the voltage of the historical output current;

[0209] Whether or not a foreign object is present is determined based on the first change degree and / or the second change degree.

[0210] When the first change degree exceeds the first degree threshold, and / or the second change planning difficulty exceeds the second degree threshold, it is considered that foreign matter exists.

[0211] This embodiment also includes a collection module 3, which is configured to collect the current output data when there is no foreign matter at a specific frequency to form collected data; the collected data is input into the data pile of the detection model as historical output data, and the detection model is configured to determine whether there is a foreign matter based on the historical output data.

[0212] The acquisition module 3 is also configured to delete duplicate historical output data if the amount of historical output data in the data pile exceeds a limit.

[0213] This example also includes an alarm module 4, which is configured to stop the wireless charging module from charging and issue a reminder when a foreign object is present. The reminder is in the form of a voice announcement: a metal foreign object is detected, and charging is stopped.

[0214] In this embodiment, the processing module may be an integrated circuit chip having the ability to process signals. The above-mentioned processing module may be a general-purpose processor. For example, the processor may be a central processing unit (CPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, and may implement or execute the disclosed methods, steps and logic block diagrams in the embodiments of the present application.

[0215] The storage module may be, but is not limited to, a random access memory, a read-only memory, a programmable read-only memory, an erasable programmable read-only memory, an electrically erasable programmable read-only memory, etc. In this embodiment, the storage module may be used to store a program, and the processing module executes the program after receiving an execution instruction.

[0216] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the server described above can refer to the corresponding process of each step in the aforementioned method, and will not be elaborated here.

[0217] The embodiment of the present application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is run on a computer, the computer executes the wireless charging foreign object detection method as described in the above embodiment.

[0218] This embodiment further provides a computer product, including a computer program, which implements the wireless charging foreign object detection method described in Embodiment 1 when executed by a processor.

[0219] Through the description of the above implementation methods, technical personnel in this field can clearly understand that the present application can be implemented by hardware, and can also be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present application.

[0220] In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can also be implemented in other ways. The apparatus and method embodiments described above are merely schematic, for example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to the multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a part of a module, a program segment or a code, and a part of the module, program segment or code contains one or more executable instructions for implementing the specified logical function. It should also be noted that each box in the block diagram and / or the flowchart, and the combination of the boxes in the block diagram and / or the flowchart can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. In addition, each functional module in each embodiment of the present application can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part.

[0221] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A wireless charging foreign object detection method, characterized in that: The method comprises: Receiving current output data of the wireless charging module, where the current output data includes current output current and / or current output voltage; The current output data is compared with the historical output data to determine whether there is a foreign object, wherein the historical output data includes historical output data of the wireless charging module of the same type as the current output data when there is no foreign object.

2. The method according to claim 1, characterized in that The comparing the current output data with the historical output data to determine whether there is a foreign object includes: Calculate the current phase difference according to the current output data; If the current phase difference is greater than a phase difference threshold, then a foreign object exists, and the phase difference threshold is calculated based on the historical output data.

3. The method according to claim 1, characterized in that The historical output data includes historical output current and / or historical output voltage; The comparing the current output data with the historical output data to determine whether there is a foreign object comprises: comparing the waveform of the historical output current with the waveform of the current output current to obtain a first change degree representing the change degree of the waveform of the current output current relative to the waveform of the historical output current; and / or Comparing the waveform of the historical output voltage with the waveform of the current output voltage, obtaining a second degree of change representing the degree of change of the waveform of the current output voltage relative to the voltage of the historical output current; Whether or not a foreign object is present is determined based on the first change degree and / or the second change degree.

4. The method according to claim 3, characterized in that The determining whether there is foreign matter based on the first change degree and / or the second change degree includes: when the first change degree exceeds a first degree threshold, and / or when the second change degree exceeds a second degree threshold, it is considered that there is foreign matter.

5. The method according to claim 1, characterized in that The method further comprises: Collecting the current output data when no foreign matter exists at a preset frequency to form collected data; The collected data is input as historical output data into a data pile of a detection model. The detection model is configured to determine whether there is a foreign object based on the historical output data. The detection model is a deep learning model.

6. The method according to claim 3, characterized in that After collecting the current output data at the preset frequency and inputting it into the detection model, the method further includes: sorting all historical output data based on a heap sorting strategy.

7. The method according to claim 6, characterized in that After sorting the collected data and the historical output data based on the heap sorting strategy, the method further includes: If the amount of historical output data in the data pile exceeds a limit, the duplicate historical output data is deleted.

8. The method according to claim 1, characterized in that The method further includes: when there is a foreign object, the wireless charging module stops charging and issues a reminder.

9. A wireless charging module foreign body detection device, characterized in that: include: A receiving module, configured to receive current output data of the wireless charging module, wherein the current output data includes a current output current and / or a current output voltage; The comparison module is configured to compare the current output data with the historical output data to determine whether there is a foreign object, wherein the historical output data includes data of the same type as the current output data that is historically output by the wireless charging module when there is no foreign object.

10. A server, characterized in that: The server comprises a processor and a memory coupled to each other, wherein a computer program is stored in the memory. When the computer program is executed by the processor, the server executes the method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed on a computer, the computer is enabled to execute the method according to any one of claims 1 to 8.

12. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 8.

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