Foreign object detection method for high-power wireless charging system

Abstract

The present application relates to the field of wireless charging technology, and provides a foreign object detection method for a high-power wireless charging system. The method includes: setting the activation time node to time zero, and establishing a basic temperature data set; calling back charging data, establishing a first constraint after compensation, establishing a second constraint based on the basic temperature data, and configuring a charging power identification channel using the two constraints; starting monitoring from time zero, and establishing a monitoring data set; identifying power data anomalies through the identification channel, and establishing a first cumulative anomaly; analyzing temperature changes and basic temperatures, and establishing a second cumulative anomaly; and reporting a foreign object result after fusing the two cumulative anomalies. The present application solves the technical problem of untimely and inaccurate foreign object detection during the charging process of a high-power wireless charging system, and achieves the technical effect of accurately detecting the presence of foreign objects by fusing abnormal analysis of power data and temperature rise data, thereby ensuring the safety and stability of the operation of the wireless charging system.

Description

Technical Field

[0001] The present application relates to the field of wireless charging technology, and in particular to a foreign object detection method for a high-power wireless charging system. Background Art

[0002] Wireless charging technology, an emerging energy transmission method, has been widely adopted in recent years in consumer electronics, automotive charging, and industrial applications. High-power wireless charging systems, in particular, enable convenient and efficient charging at high power densities. However, existing high-power wireless charging systems face numerous challenges in practical application, among which foreign object detection is a significant yet unresolved technical challenge. Because wireless charging uses electromagnetic induction or magnetic resonance to transmit energy, the presence of metallic foreign objects during charging can reduce energy transmission efficiency and even pose safety risks such as device overheating and fire. Traditional foreign object detection methods typically rely on single-source temperature sensing or power monitoring technologies. These methods have the following shortcomings: Single-source temperature detection is sensitive to ambient temperature fluctuations, prone to false positives or missed detections; Furthermore, detection methods based on power fluctuations lack accuracy when detecting small foreign objects. Furthermore, existing technologies struggle to achieve real-time detection and efficient response in complex application scenarios, compromising system stability and safety. Summary of the Invention

[0003] This application provides a foreign object detection method for a high-power wireless charging system, aiming to solve the technical problem of untimely and inaccurate foreign object detection in a high-power wireless charging system during the charging process.

[0004] In view of the above problems, the present application provides a foreign object detection method for a high-power wireless charging system.

[0005] The present application provides a foreign object detection method for a high-power wireless charging system, the method comprising: after the wireless charging system is activated, establishing the activation time node as time zero, and establishing a basic temperature dataset at time zero, the basic temperature dataset being constructed by activating a thermal imaging sensor to perform temperature data acquisition, the basic temperature dataset including an ambient temperature dataset and a wireless charging device temperature dataset; calling backtracking charging data of the wireless charging system, compensating the backtracking charging data according to a backtracking interval of the backtracking charging data, establishing a first constraint, establishing a second constraint based on the basic temperature dataset, and configuring a charging power identification channel using the first and second constraints; starting charging monitoring of the wireless charging system at time zero to establish a monitoring dataset, the monitoring dataset including a power dataset and a temperature change dataset; using the charging power identification channel to identify anomalies in the power dataset in the monitoring dataset and establish a first cumulative anomaly; performing temperature rise analysis on the temperature change dataset and the basic temperature dataset in the monitoring data to establish a second cumulative anomaly, fusing the first cumulative anomaly and the second cumulative anomaly into a time series anomaly, and reporting a foreign object detection result.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The above-mentioned foreign object detection method for a high-power wireless charging system first sets the activation moment as time zero after the system is activated, and constructs a basic temperature data set containing the ambient temperature and the device temperature through the thermal imaging sensor; then, the historical charging data of the wireless charging system is called, and it is compensated by the backtracking interval to establish a first constraint. At the same time, a second constraint is established based on the basic temperature data set, and the two are used to jointly configure a charging power identification channel; charging monitoring is started at time zero, and a monitoring data set including power data and temperature change data is generated. The power data is identified as abnormal through the identification channel to generate a first cumulative abnormality; then, the temperature change data and the basic temperature data are analyzed to generate a second cumulative abnormality; finally, by integrating the time series analysis of the two types of abnormalities, the foreign object detection result is comprehensively obtained and an alarm is issued, thereby ensuring the technical effect of the safety and stability of the operation of the wireless charging system.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0010] Figure 1 FIG. 1 is a flow chart of a foreign object detection method for a high-power wireless charging system according to an embodiment.

[0011] Figure 2 FIG. 1 is a flow chart of establishing a first constraint for a foreign object detection method for a high-power wireless charging system in one embodiment. DETAILED DESCRIPTION

[0012] The embodiments of the present application provide a foreign object detection method for a high-power wireless charging system to solve the technical problem of untimely and inaccurate foreign object detection during the charging process of the high-power wireless charging system.

[0013] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0014] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0015] Examples, such as Figure 1 As shown, the present application provides a foreign object detection method for a high-power wireless charging system, the method comprising:

[0016] After the wireless charging system is activated, the activation time node is created as time zero, and a basic temperature dataset is established at the time zero. The basic temperature dataset is constructed by activating the thermal imaging sensor to perform temperature data acquisition. The basic temperature dataset includes an ambient temperature dataset and a wireless charging device temperature dataset.

[0017] In the embodiment of the present application, after the wireless charging system is activated, the current activation time is marked as time zero, which serves as the benchmark for subsequent data collection and analysis. At time zero, the thermal imaging sensor is activated to start the temperature data collection function, recording and constructing a basic temperature dataset in real time. This dataset mainly consists of two parts: the ambient temperature dataset, which records the temperature information of the surrounding environment to reflect the influence of external factors, and the temperature dataset of the wireless charging device, which focuses on the temperature status of the charging device itself, reflecting its operating status and internal heating. This process ensures that the basic temperature data during the charging process is highly accurate and reliable, laying the foundation for subsequent detection and analysis.

[0018] The method further comprises: calling backtracking charging data of the wireless charging system, compensating the backtracking charging data according to a backtracking interval of the backtracking charging data, establishing a first constraint, establishing a second constraint based on the basic temperature data set, and configuring a charging power identification channel using the first constraint and the second constraint.

[0019] In one embodiment, historical charging data of a wireless charging device is retrieved and compensated and adjusted by setting a backtracking interval to eliminate deviations or incompleteness in the data, ultimately generating a first constraint. Simultaneously, the charging power corresponding to the current temperature state is analyzed based on a previously established basic temperature data set and combined with an experimental charging database, thereby generating a second constraint related to the current temperature. Finally, by combining these two constraints, a charging power identification channel is configured that comprehensively considers changes in power and temperature, providing accurate basic support for subsequent anomaly detection.

[0020] Further, if Figure 2 As shown, the present application provides the calling of the retrospective charging data of the wireless charging system, and after compensating the retrospective charging data according to the retrospective interval of the retrospective charging data, establishes a first constraint, including:

[0021] A backtracking window is established, and with time zero as the starting point, historical wireless charging records based on the backtracking window are backtracked to establish a historical data set with a time series identifier; after data screening of the historical data set, a backtracking interval of a selected data set is obtained, the selected data set is weighted by the backtracking interval, and standard condition charging power fitting is performed based on the selected data set to establish a standard condition charging power fitting result; and the first constraint is established using the standard condition charging power fitting result.

[0022] Preferably, a backtracking window is established at time zero to limit the scope of the backtracking historical charging records. In order to ensure that the selected data can be close to the actual operating status of the current charging system, the length of the window is usually set to 10 or less to avoid the window being too long resulting in excessive dispersion of data, or the window being too short resulting in complete abnormality of the selected features, which affects the accuracy of the results; then, the historical records of the wireless charging system are backtracked within the backtracking window, and a time sequence identifier is attached to each historical record, thereby forming an ordered historical data set, including but not limited to power, voltage, current, temperature, and timestamp; then, the charging records within the backtracking window are extracted from the historical data set, and these records are screened. The main goal of the selection is to eliminate abnormal data or data with noise interference to ensure that the remaining data is representative and reliable. The screening is carried out by setting a threshold range, that is, comparing the data of each historical record in the historical data set with the corresponding normal working range (such as the normal working range of power), so as to eliminate the data that deviates from the normal working range; after the screening is completed, the backtracking interval of each data will be determined based on the time series identifier in the selected data set, and then based on the determined backtracking interval, weighted processing will be applied to the selected data. The weighting rule usually adopts a decreasing weighting method, that is, the records closer to the current time are given higher weights, and the records farther away are given lower weights. For example, the exponential weighting formula can be used ,in, is the weight of the i-th data after weighting, which is used to indicate the importance of the data in charging power fitting. The weight decay coefficient determines the speed at which the weight decreases. The larger the value, the faster the weight decreases, and the smaller the value, the slower the weight decreases. This parameter is adjusted according to actual needs to balance the impact of recent data and long-term data. is the backtracking interval, that is, the time difference between the i-th data and time zero. The larger the time difference, the smaller the impact on the current system state, so the weight is lower. Then, the weighted selected data set is used as input to perform the fitting calculation of the standard charging power. The fitting process can adopt a variety of methods, such as linear regression, polynomial fitting or machine learning model. The goal of fitting is to generate a standard charging power curve that is consistent with the actual charging state based on the power change trend of the weighted selected data set. It is used to reflect the power characteristics of the charging system under normal circumstances when there is no foreign object interference. Specifically, a polynomial fitting function is constructed, and the specific form is ,in, is the power value, t is the time point of the charging system operation, a and b are the polynomial coefficients, c is the intercept, which is a constant term. In the fitting process, the weighted selected data set is used as input, which includes multiple time points. And the corresponding actual power value To ensure the fitting function accurately reflects the changing trend of charging power, the least squares method is used to calculate the polynomial coefficients a, b, and c. The goal of the least squares method is to minimize the sum of squared errors between the fitted function and the actual power values. The selected data set is then substituted into the polynomial function for fitting. The parameters in the function are adjusted so that the fitted polynomial curve closely matches the data distribution as much as possible, thereby obtaining the specific coefficients of the fitted polynomial. To verify the fitting effect, the mean square error (MSE) of the fitting function is calculated. If the error is small and the fitted curve accurately fits the actual data, the fitting function is valid. In this case, the fitted polynomial function is stored as the standard charging power fitting result, which serves as a reference for the system. Otherwise, the order of the polynomial is adjusted or a linear fit is used. After the fitting is complete, the corresponding curve of the fitting result is used to establish the first constraint. This constraint describes the dynamic range of charging power and becomes an important parameter for subsequent charging power identification. This process ensures efficient use of historical data while providing an accurate charging power benchmark, providing reliable support for foreign object detection.

[0023] Furthermore, the present application provides the method of establishing a second constraint based on the basic temperature data set, and configuring a charging power identification channel using the first constraint and the second constraint, including:

[0024] An experimental charging database of the wireless charging system is called, the basic temperature dataset is used as a matching feature, data matching of the experimental charging database is performed, and a second constraint is established based on the charging power fitting result; a dynamic threshold is constructed based on the first constraint, and a static baseline threshold is constructed based on the second constraint; the dynamic threshold and the static baseline threshold are fused into a dual threshold layer, and the dual threshold layer is used to complete the charging power identification channel configuration.

[0025] Optionally, call the experimental charging database of the wireless charging system. This experimental charging database is a historical database that stores a large amount of experimental charging data, including charging characteristic information (including power, timestamp, etc.) under different device states (such as device aging, environmental conditions, etc.). Use the previously established basic temperature data set as a matching feature to match it with the data in the experimental charging database to screen out the experimental data that best matches the current temperature state of the charging device. The matching process is based on Euclidean distance, including calculating the similarity distance between the device temperature, ambient temperature and experimental data to ensure that the matching data can accurately reflect the operating characteristics of the current device. After the matching is completed, use the matched experimental data through the same fitting process as mentioned above to obtain the charging power fitting result, and use this to construct the second constraint. This second constraint is used to describe the charging power range of the charging system under static benchmark conditions, which can reflect the aging of the device and the ambient temperature. At the same time, the first constraint is generated by fitting the backtracking data and can dynamically reflect the changes in the real-time charging power. Therefore, a dynamic threshold is constructed based on the first constraint to describe the short-term dynamic fluctuation range of the charging power. Subsequently, the dynamic threshold generated by the first constraint is fused with the static baseline threshold generated by the second constraint to form a dual threshold layer. The dynamic threshold reflects the short-term changes of the charging system in real time, while the static baseline threshold serves as a long-term reference. The fusion of the two can comprehensively evaluate the normal range of the charging power. Finally, by connecting the dual threshold layer with the configured input layer and output layer, a charging power identification channel is configured to achieve accurate identification of charging power anomalies. This configuration can not only quickly respond to real-time fluctuations, but also take into account long-term effects such as equipment aging, thereby improving the stability and safety of the wireless charging system in complex environments.

[0026] Charging monitoring of the wireless charging system is started at the time zero, and a monitoring data set is established. The monitoring data set includes a power data set and a temperature change data set.

[0027] In one embodiment, starting at time zero, real-time monitoring of the wireless charging process is initiated, and data generated during the charging process is continuously collected to construct a monitoring dataset. This monitoring dataset consists of two parts: a power dataset, which records the real-time power changes of the charging system, including key parameters such as charging power output, voltage, and current. This data can reflect the energy transmission efficiency and charging stability of the charging system; and a temperature change dataset, which records the temperature changes of the charging device and its surrounding environment, including the temporal trends of the device surface temperature and ambient temperature. This temperature data can reflect the thermal characteristics of the charging process, particularly whether the device is experiencing abnormal temperature rise. By synchronously collecting and recording power data and temperature change data, a comprehensive understanding of the power transmission and thermal characteristics of the charging process can be achieved, providing comprehensive and reliable basic data support for subsequent anomaly detection and analysis.

[0028] The charging power identification channel is used to identify abnormalities in the power data set in the monitoring data set, and a first accumulated abnormality is established.

[0029] In one embodiment, a previously configured charging power identification channel is used to perform anomaly identification and analysis on the power data set recorded in the monitoring data set. Specifically, the identification channel compares the real-time monitored power data with a dual threshold layer. If the power data exceeds the dynamic threshold range, it indicates a short-term abnormal power fluctuation. If it exceeds the static baseline threshold, it may indicate a long-term abnormality such as device aging or foreign object interference. Subsequently, the power data set is analyzed and recorded item by item. Whenever a power data point in the power data set is outside the dynamic threshold or the static baseline threshold range, the abnormality count (default is 0) is incremented by 1. In this way, through continuous accumulation, the total number of abnormal occurrences can be obtained. In each accumulation, the abnormal power data point and the corresponding timestamp are recorded to obtain a preliminary first cumulative abnormality. The preliminary first cumulative abnormality is then identified with the total number of counts to establish a final first cumulative abnormality. The first cumulative abnormality, as a power abnormality indicator, can accurately reflect the power abnormality during the charging process and provide key support for subsequent comprehensive analysis with temperature abnormalities, thereby ensuring real-time capture and response to power abnormalities and improving the accuracy of foreign object detection.

[0030] Perform temperature rise analysis on the temperature change data set and the basic temperature data set in the monitoring data, establish a second cumulative anomaly, fuse the first cumulative anomaly and the second cumulative anomaly time series anomaly, and report a foreign object detection result.

[0031] In one embodiment, during the monitoring of the charging process, a temperature rise analysis is performed on the temperature change data in the monitoring dataset and the base temperature dataset. The purpose of the temperature rise analysis is to detect any abnormal temperature rise of the device during the charging process, particularly by comparing it with the base temperature data to determine whether any temperature changes exceed the normal range. During this process, the real-time recorded temperature change data is compared with the base temperature dataset, and the number of times the temperature data falls outside the base temperature data range is counted. Each abnormal temperature data and timestamp are recorded in a second cumulative anomaly (also with a total count). This second cumulative anomaly focuses on identifying temperature deviations, reflecting possible thermal management issues or device anomalies during the charging process. Based on this, the first cumulative anomaly (power anomaly) and the second cumulative anomaly (temperature anomaly) are time-series fused. Through the linked analysis of power and temperature data, potential foreign object interference can be more comprehensively identified, thereby determining whether any external foreign object is affecting the charging system. Finally, the fused anomaly results are used to generate a foreign object detection report, which issues a timely warning of potential safety risks or device failures, thereby ensuring the safety and stability of the charging process.

[0032] Furthermore, the present application provides a method for reporting a foreign body detection result after fusing the first cumulative abnormality and the second cumulative abnormality in time sequence, including:

[0033] After performing abnormal timing alignment on the first cumulative abnormality and the second cumulative abnormality, a timing abnormality state is established, and the timing abnormality state includes a power timing abnormality state and a temperature timing abnormality state; a linkage relationship authentication of the timing abnormality state is performed, and a linkage relationship authentication result is established, and the linkage relationship authentication result includes temperature-power linkage and power-temperature linkage; a timing superposition coefficient is established through the linkage relationship authentication result; the timing superposition coefficient is used to perform timing superposition, fusion and accumulation of the first cumulative abnormality and the second cumulative abnormality, and a foreign body detection result is reported.

[0034] Preferably, after obtaining the first cumulative anomaly (power anomaly) and the second cumulative anomaly (temperature anomaly), the first cumulative anomaly and the second cumulative anomaly are time-aligned. The process of time-alignment is to synchronize the power data and the temperature data according to the timestamp to ensure that the two are compared on the same time scale. If there is only one data (power data or temperature data) at a certain timestamp, the data at that time point will be extracted from the corresponding data set for filling. Through this alignment method, the changing trends of power and temperature can be evaluated at the same time, so as to more accurately identify the possible abnormal relationship between the two. Based on the time-aligned data, a time-series abnormality state is established, which is divided into a power time-series abnormality state and a temperature time-series abnormality state. The power timing abnormal state reflects the abnormal fluctuation of power over time, while the temperature timing abnormal state describes the abnormal increase or decrease in temperature during the temperature change process. These two states can help the system identify the abnormal linkage relationship between power and temperature. Subsequently, the linkage relationship certification of the timing abnormal state is performed. In this step, the linkage relationship between power and temperature is judged according to the preset physical laws. In the temperature-power linkage certification, it is analyzed whether the temperature change reasonably affects the power change. The increase in temperature usually leads to a decrease in power, especially when the equipment temperature is high, the efficiency may be reduced. By calculating the change values ​​ΔT(t) and ΔP(t) of temperature and power, it is checked whether they conform to the normal expected relationship. If the power change fails to change with the temperature change, or the change amplitude exceeds the corresponding linkage threshold (determined by expert decision), it is marked as abnormal. Similarly, in the power-temperature linkage certification, it is analyzed whether the power change brings about reasonable temperature changes. In most cases, when the power increases, the temperature of the equipment should also increase. By calculating the power change ΔP(t) and the temperature change ΔT(t), it is verified whether there is a normal linkage relationship between power and temperature. If the power change fails to drive the temperature change, or the temperature change amplitude corresponds to the linkage threshold (determined by expert decision), it is also considered abnormal. By analyzing the linkage relationship between temperature and power, a linkage relationship certification result is generated, which includes temperature. - The certification results of power linkage and power-temperature linkage are used to establish a time series superposition coefficient. Specifically, the data in the first cumulative anomaly and the second cumulative anomaly are normalized to eliminate dimensionality issues and ensure that data in different units can be reasonably compared. Normalization is usually performed using a max-min method. Through normalization, the data in the first cumulative anomaly and the second cumulative anomaly are converted to the same range (usually 0 to 1), avoiding calculation problems caused by different dimensions. The corresponding data of the normalized first cumulative anomaly and the second cumulative anomaly are then located based on the data defined as anomalies in the linkage relationship certification results, and the power change value or temperature change value at that location is calculated;Afterwards, the calculated power change value and temperature change value are time-series aligned, and then the power change value and temperature change value at the same timestamp are ratio-calculated, and the absolute value of the calculation result is taken to obtain the temperature-power linkage intensity at the timestamp, and then the temperature change value and power change value at the same timestamp are ratio-calculated, and the absolute value of the calculation result is taken to obtain the power-temperature linkage intensity at the timestamp, and then the temperature-power linkage intensity and the power-temperature linkage intensity at the same timestamp are added together to obtain the linkage coefficient at the same timestamp; then, all the linkage coefficients are accumulated to obtain the time series superposition coefficient, which can quantify the overall situation of the linkage between power and temperature anomalies. , providing a basis for subsequent anomaly detection; finally, the time series superposition coefficient is used to perform time series superposition and fusion on the first and second cumulative anomalies. This process adds the total number of anomalies marked by the first cumulative anomaly and the total number of anomalies marked by the second cumulative anomaly, and then multiplies the sum by the time series superposition coefficient to obtain the final abnormal accumulation value. The fused abnormal accumulation value reflects the comprehensive abnormality of the charging process. By comparing this abnormal accumulation value with the abnormal accumulation value threshold, the foreign object detection result is finally reported (i.e., the abnormal accumulation value is greater than or equal to the abnormal accumulation value threshold), indicating possible foreign object interference or other abnormal conditions, thereby ensuring the safe and stable operation of the charging system.

[0035] Furthermore, the present application provides the reported foreign body detection results, including:

[0036] A corresponding warning level is reported according to the foreign object detection result, and the user's warning feedback is read; a steady-state space is reconstructed based on the warning feedback and the monitoring data set; and the steady-state space is used to perform continuous foreign object detection for the wireless charging system.

[0037] Optionally, after foreign object detection, the status of the charging system is evaluated according to the foreign object detection result to determine the corresponding warning level. The warning level is usually divided into multiple levels, for example, level one risk warning, level two risk warning, level three risk warning, etc., with increasing severity. Each level has a corresponding abnormal accumulation interval, which is set based on safety requirements. After the warning level is determined, the warning level will be sent to the user. After waiting for a predetermined time, the user's warning feedback is read. This step is to understand the user's reaction to the current warning, for example, whether the user accepts the current warning information and whether relevant measures have been taken. The user's feedback may include confirmation of the current detection result. Subsequently, based on the user's warning feedback, the corresponding strategy will be selected to construct the steady-state space. The steady-state space refers to Under normal conditions, the stable range of all system parameters (such as power, temperature, etc.) is as follows: for example, if the early warning feedback is that there is no foreign object, the monitoring data set will be analyzed and the steady-state space will be reconstructed. If the early warning feedback is that there is a foreign object, a reconstructed data set will be established and the steady-state space will be reconstructed based on this reconstructed data set; afterwards, based on the reconstructed steady-state space, continuous foreign object detection will be carried out. During this process, the real-time monitoring data will be continuously analyzed to determine whether there are new abnormalities. By comparing the new monitoring data with the reconstructed steady-state space, the operating status of the current charging system can be dynamically evaluated. If the system's power, temperature and other parameters exceed the range of the reconstructed steady-state space, a warning will be re-issued to indicate that there may be foreign objects or equipment failures, ensuring the safety and stability of the charging process.

[0038] Furthermore, the present application provides the method of reconstructing the steady-state space based on the early warning feedback and the monitoring data set, including:

[0039] Determine whether the warning feedback is feedback that no foreign object exists; if the warning feedback is feedback that no foreign object exists, perform steady-state analysis of the monitoring data set to establish a steady-state baseline; perform fluctuation aggregation on the monitoring data set, and construct a fluctuation factor using the fluctuation aggregation result; and reconstruct a steady-state space using the steady-state baseline and the fluctuation factor.

[0040] Optionally, when judging the early warning feedback, based on the content of the received early warning feedback, determine whether the feedback indicates that the device has not found any foreign objects. If the user's feedback information confirms that there are no foreign objects, a steady-state analysis will be performed on the monitoring data set. The purpose of the steady-state analysis is to evaluate the normal operating status of the charging system under the current conditions. By analyzing the system's power, temperature and other parameters, a steady-state baseline is established to define the normal operating range of the charging system. The steady-state baseline represents the stable range that the power, temperature and other data should maintain when there is no external interference or abnormal situation. The steady-state baseline of power is obtained by calculating the mean of the power data set in the monitoring data set, and the same is true for the steady-state baseline of temperature. Subsequently, the monitoring data set is subjected to fluctuation aggregation. The purpose of fluctuation aggregation is to identify the change pattern of power and temperature within a certain time range, thereby obtaining fluctuation factors, including power fluctuation factor and temperature fluctuation factor. Specifically, the monitoring data set is divided according to the preset aggregation window (determined according to the accuracy requirements, such as 1 minute, 3 minutes, etc.) to obtain multiple aggregated data sets. For each aggregated data set, the standard deviation of the aggregated data set is calculated, and these standard deviations are added to the fluctuation aggregation results. The fluctuation aggregation results are then averaged to obtain the fluctuation factor. This fluctuation factor represents the intensity or frequency of parameter fluctuations and is used for subsequent steady-state space reconstruction. Afterwards, the steady-state space of the charging system is reconstructed using the steady-state baseline and the fluctuation factor. The process of reconstructing the steady-state space involves combining the steady-state baseline and the fluctuation factor to define a stable working area that includes the fluctuation range (adding or subtracting 3 fluctuation factors to the steady-state baseline). Through the reconstructed steady-state space, its normal working range can be dynamically adjusted, and a more accurate standard can be provided for subsequent continuous monitoring and anomaly detection.

[0041] Furthermore, the present application provides the method of determining whether the warning feedback is feedback of no foreign object, including:

[0042] If the warning feedback is a feedback of the presence of a foreign object, after the user removes the foreign object and reconstruction time zero, data collection of the wireless charging system is performed to establish a reconstructed data set; after data segmentation of the reconstructed data set, the steady-state space is reconstructed using the data segmentation result, and the data segmentation is thermal state recovery data segmentation.

[0043] Optionally, if the early warning feedback is a feedback of the presence of foreign objects, after the user has cleared the foreign objects, the time zero point will be reconstructed. The time zero point is the starting moment of the charging process, which usually represents the time point when the charging system resumes normal operation. By resetting the time point after the foreign objects are cleared to the time zero point and re-collecting data from this time point, the influence of the previous foreign object interference on the data is eliminated, thereby establishing a new reconstructed data set. This reconstructed data set contains monitoring data such as power and temperature starting from the new time zero point, laying the foundation for subsequent analysis and steady-state space reconstruction; subsequently, in order to further analyze the data, the reconstructed data set will be segmented. The purpose of data segmentation is to divide the continuous charging data into different subsets for analysis of different For the data features of the same stage, the segmentation method is hot state recovery data segmentation, that is, according to the design standard of the device, a temperature threshold is set. When the temperature of the device reaches this temperature threshold, it is considered that the device has entered the hot state from the cold state. At this time, the reconstructed data set will be segmented according to the timestamp of reaching this temperature threshold, and the cold state reconstructed data set and the hot state reconstructed data set will be obtained. Afterwards, the steady-state baseline and fluctuation factor of the cold state reconstructed data set and the hot state reconstructed data set are determined respectively in the same way as mentioned above, and the cold state steady-state space and the hot state steady-state space are reconstructed, and together they form the final steady-state space, which helps eliminate the interference caused by foreign matter and ensures the safety and stability of the subsequent charging process.

[0044] Furthermore, the present application provides the reporting of foreign body detection results, further comprising:

[0045] A safety response window is configured according to the safety level of the foreign object detection result; if no foreign object processing response exists in the safety response window, a shutdown instruction is generated; according to the shutdown instruction, the wireless charging system is placed in a safety protection state, and a safety protection state identification is executed.

[0046] Optionally, after receiving the foreign object detection result, a safety response window is configured according to the safety level of the detection result (i.e., the aforementioned warning level). The purpose of this safety response window is to limit the time within which the charging system needs to respond. The duration and range of the safety response window may vary according to different safety levels. For example, if the safety level is level three risk warning, the response window time is shorter to allow for a quick response. If the safety level is level one risk warning, the response time may be longer. If no effective foreign object handling response is detected within the safety response window, that is, no action is taken to deal with possible foreign object interference, the device will be deemed to have a potential risk and a shutdown command will be automatically generated based on this situation. The shutdown command means that the device will be shut down without handling the abnormality or In the event of foreign objects, the charging process will be stopped immediately to prevent equipment damage or safety hazards caused by untimely processing; once a shutdown command is generated, the wireless charging system will be switched to a safety protection state according to the command. In the safety protection state, all high-risk operations of the charging system will be suspended or restricted to prevent the charging system from continuing to run and causing possible equipment damage or further safety problems. At the same time, a safety protection state mark will be executed to confirm that the charging system is currently in protection mode. This mark can be displayed on the user interface or recorded in the log to facilitate subsequent inspections and operators' understanding of the current status. If necessary, an alarm or notification can be used to remind the user that the system has entered a protection state to ensure that further manual intervention measures are taken in time.

[0047] In summary, the embodiments of the present application have at least the following technical effects:

[0048] In an embodiment of the present application, after the wireless charging system is activated, the activation time node is created as time zero, and a basic temperature dataset is established at time zero. The basic temperature dataset is constructed by activating a thermal imaging sensor to perform temperature data acquisition. The basic temperature dataset includes an ambient temperature dataset and a wireless charging device temperature dataset. Subsequently, the retrospective charging data of the wireless charging system is called, and after compensating the retrospective charging data according to the retrospective interval of the retrospective charging data, a first constraint is established. A second constraint is established based on the basic temperature dataset, and a charging power identification channel is configured using the first and second constraints. Furthermore, charging monitoring of the wireless charging system is performed starting at time zero to establish a monitoring dataset. The monitoring dataset includes a power dataset and a temperature change dataset. Then, the charging power identification channel is used to identify anomalies in the power dataset in the monitoring dataset and establish a first cumulative anomaly. Finally, temperature rise analysis is performed on the temperature change dataset and the basic temperature dataset in the monitoring data to establish a second cumulative anomaly. After the time series anomaly of the first cumulative anomaly and the second cumulative anomaly are fused, a foreign object detection result is reported. These technical effects jointly solve the technical problem of untimely and inaccurate foreign object detection in high-power wireless charging systems during the charging process, and achieve the technical effect of accurately detecting the presence of foreign objects by integrating abnormal analysis of power data and temperature rise data, thereby ensuring the safety and stability of the wireless charging system operation.

[0049] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0050] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

[0051] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A foreign object detection method for a high-power wireless charging system, characterized in that: The method comprises: After the wireless charging system is activated, the activation time node is created as time zero, and a basic temperature dataset is established at the time zero. The basic temperature dataset is constructed by activating the thermal imaging sensor to perform temperature data acquisition. The basic temperature dataset includes an ambient temperature dataset and a wireless charging device temperature dataset. Retrieving the retrospective charging data of the wireless charging system, compensating the retrospective charging data according to a retrospective interval of the retrospective charging data, and establishing a first constraint, wherein the first constraint is used to describe a dynamic range of charging power; establishing a second constraint based on the basic temperature data set, and configuring a charging power identification channel using the first constraint and the second constraint, wherein the second constraint is used to describe a charging power range of the charging system under static reference conditions; Starting charging monitoring of the wireless charging system at the time zero, establishing a monitoring data set, the monitoring data set including a power data set and a temperature change data set; Using the charging power identification channel to identify abnormalities in the power data set in the monitoring data set, and establish a first accumulated abnormality; Performing temperature rise analysis on the temperature change data set and the basic temperature data set in the monitoring data, establishing a second cumulative anomaly, fusing the first cumulative anomaly and the second cumulative anomaly time series anomaly, and reporting a foreign object detection result; The calling of the retrospective charging data of the wireless charging system and establishing a first constraint after compensating the retrospective charging data according to the retrospective interval of the retrospective charging data include: Establishing a backtracking window, taking the time zero as the starting point, performing backtracking of historical wireless charging records based on the backtracking window, and establishing a historical data set with a time series identifier; After filtering the historical data set, obtaining a backtracking interval of a selected data set, weighting the selected data set using the backtracking interval, performing standard condition charging power fitting based on the selected data set, and establishing a standard condition charging power fitting result; Establishing the first constraint using the standard condition charging power fitting result; The establishing of a second constraint based on the basic temperature data set and configuring a charging power identification channel using the first constraint and the second constraint includes: calling an experimental charging database of the wireless charging system, using the basic temperature data set as a matching feature, performing data matching on the experimental charging database, and establishing a second constraint based on a charging power fitting result; A dynamic threshold is constructed using the first constraint, and a static baseline threshold is constructed using the second constraint. The dynamic threshold and the static baseline threshold are fused into a dual threshold layer, and the charging power identification channel configuration is completed using the dual threshold layer.

2. The foreign object detection method for a high-power wireless charging system according to claim 1, wherein: After fusing the first accumulated abnormality and the second accumulated abnormality in time sequence, reporting the foreign body detection result includes: After performing abnormal timing alignment on the first accumulated abnormality and the second accumulated abnormality, a timing abnormality state is established, where the timing abnormality state includes a power timing abnormality state and a temperature timing abnormality state; Execute linkage relationship authentication of the timing abnormal state and establish linkage relationship authentication results, wherein the linkage relationship authentication results include temperature-power linkage and power-temperature linkage; Establishing a time series superposition coefficient based on the linkage relationship authentication result; The time series superposition coefficient is used to perform time series superposition, fusion and accumulation of the first cumulative abnormality and the second cumulative abnormality, and a foreign body detection result is reported.

3. The foreign object detection method for a high-power wireless charging system according to claim 1, wherein: The reporting of the foreign body detection result includes: Report the corresponding warning level according to the foreign body detection result and read the user's warning feedback; reconstructing a steady-state space based on the early warning feedback and the monitoring data set; The steady-state space is utilized to perform continuous foreign object detection of the wireless charging system.

4. The foreign object detection method for a high-power wireless charging system according to claim 3, wherein: The reconstructing the steady-state space based on the early warning feedback and the monitoring data set includes: Determining whether the warning feedback is a feedback that no foreign object exists; If the warning feedback is that there is no foreign body feedback, performing steady-state analysis of the monitoring data set to establish a steady-state baseline; performing fluctuation aggregation on the monitoring data set, and constructing a fluctuation factor using the fluctuation aggregation result; The steady-state space is reconstructed using the steady-state baseline and the fluctuation factor.

5. The foreign object detection method for a high-power wireless charging system according to claim 4, wherein: The determining whether the warning feedback is feedback that no foreign object exists further includes: If the warning feedback is a foreign object presence feedback, after the user removes the foreign object and after reconstruction time zero, data collection of the wireless charging system is performed to establish a reconstructed data set; After data segmentation is performed on the reconstructed data set, the steady-state space is reconstructed using the data segmentation result, and the data segmentation is a hot state recovery data segmentation.

6. The foreign object detection method for a high-power wireless charging system according to claim 1, wherein: The reporting of the foreign body detection result also includes: Configuring a security response window according to the security level of the foreign object detection result; If there is no foreign object handling response in the safety response window, generating a shutdown instruction; The wireless charging system is placed in a safety protection state according to the shutdown instruction, and a safety protection state identification is performed.

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