Wireless charger and charging method of wireless charger

Abstract

The invention provides a wireless charger and a charging method of the wireless charger. Belongs to the technical field of wireless power transmission. The method comprises the following steps: performing multi-coil array layout design on a wireless charger, generating multi-coil array layout data, deploying an electromagnetic field imaging module and an equipment fingerprint identification module, and constructing an intelligent charging framework with electromagnetic field sensing and equipment identification capabilities; performing electromagnetic field space scanning on the charging area based on the intelligent charging architecture to generate electromagnetic field distribution data; performing feature extraction on equipment entering the charging area through an equipment fingerprint identification module to obtain equipment fingerprint feature data; determining any position information of the multiple devices in the charging area by combining the electromagnetic field distribution data and the device fingerprint feature data; through fusion of a multi-coil array, electromagnetic field imaging and an equipment fingerprint identification technology, the degree of freedom of multi-equipment charging is improved, the equipment can be efficiently charged at any position of a charging area, and the constraint of single-point alignment is eliminated.

Description

Technical Field

[0001] This invention proposes a wireless charger and a charging method for the wireless charger, belonging to the field of wireless power transmission technology. Background Technology

[0002] In today's era of rapid technological advancement, wireless charging technology has gradually become a research hotspot in the field of electronic device charging due to its convenience. However, current mainstream wireless charging technologies, such as the WPC Qi standard, have revealed many key shortcomings in practical applications, making it difficult to meet users' demands for both high convenience and high practicality.

[0003] On the one hand, its single-point alignment is extremely dependent, and the receiving coil and transmitting coil must be strictly aligned. Once the offset exceeds 5mm, the charging efficiency will drop by more than 30%, which greatly limits the user's freedom of use. On the other hand, most transmitters only support charging a single device. To achieve charging of multiple devices, multiple independent coil areas need to be set up, which does not allow devices to be placed freely on a continuous surface, greatly reducing their practicality.

[0004] Furthermore, the fixed operating frequency of existing technologies cannot adapt to changes in receiver load, fluctuations in coil coupling coefficient, or temperature drift, resulting in severe detuning losses. In terms of foreign object detection, relying solely on power-voltage anomalies makes it difficult to distinguish legitimate equipment from metallic foreign objects, easily leading to false shutdowns or missed detections.

[0005] Therefore, it is urgent to develop a new wireless charging method with multi-target non-intrusive recognition, full-area energy focusing, frequency adaptation and intelligent foreign object protection capabilities, in order to break through the existing technical bottlenecks and bring users a more efficient, convenient and safe wireless charging experience. Summary of the Invention

[0006] This invention provides a wireless charger and a charging method for the wireless charger, to solve the problems mentioned in the background section above:

[0007] This invention proposes a charging method for a wireless charger, the method comprising:

[0008] S1. Design a multi-coil array layout for the wireless charger, generate multi-coil array layout data, and deploy an electromagnetic field imaging module and a device fingerprint recognition module to build an intelligent charging architecture with electromagnetic field sensing and device recognition capabilities.

[0009] S2. Based on the intelligent charging architecture, perform electromagnetic field spatial scanning on the charging area to generate electromagnetic field distribution data; extract features of devices entering the charging area through the device fingerprint recognition module to obtain device fingerprint feature data; combine the electromagnetic field distribution data and the device fingerprint feature data to determine the location information of multiple devices in the charging area.

[0010] S3. Perform energy focusing calculations based on the arbitrary location information of multiple devices to generate energy focusing parameters for each device; dynamically adjust the power distribution of each coil in the multi-coil array according to the energy focusing parameters to obtain charging efficiency data for multiple devices; monitor the corresponding data in real time, and adaptively adjust the working frequency according to the monitoring results to generate adaptive frequency data;

[0011] S4. Based on electromagnetic field distribution data, device fingerprint feature data, and adaptive frequency data, perform three-level foreign object protection processing; identify metallic foreign objects and generate foreign object detection result data; if a foreign object is detected, immediately adjust the energy transmission strategy to avoid safety hazards.

[0012] S5. Based on the charging efficiency data of multiple devices and the foreign object detection results, a comprehensive evaluation is conducted to generate comprehensive evaluation data on charging safety and energy efficiency; based on the comprehensive evaluation data on charging safety and energy efficiency, a charging optimization instruction is generated to dynamically optimize the charging process of the wireless charger.

[0013] The present invention provides a wireless charger comprising:

[0014] One or more processors;

[0015] Memory, used to store one or more programs;

[0016] When the one or more programs are executed by the one or more processors, the one or more processors implement the charging method described in any one of the above descriptions.

[0017] The beneficial effects of this invention are as follows: By integrating multi-coil arrays with electromagnetic field imaging and device fingerprint recognition technologies, the freedom of charging multiple devices is improved, allowing devices to charge efficiently at any location within the charging area, freeing them from the constraints of single-point alignment; dynamic power distribution and online frequency tracking technologies reduce detuning losses and enhance energy transmission efficiency, making the charging process more energy-efficient; a three-level foreign object protection mechanism reduces false stops and missed detections, avoiding safety hazards caused by metal foreign objects and ensuring charging safety; it enables simultaneous, seamless charging of multiple devices, improving ease of use, and accurately identifies foreign objects to prevent danger, bringing users a new, efficient, safe, and convenient wireless charging experience. Attached Figure Description

[0018] Figure 1 This is a diagram illustrating the steps of the method described in this invention;

[0019] Figure 2 This is a schematic diagram of the device positioning and position calculation described in this invention;

[0020] Figure 3 This is a schematic diagram of the comprehensive evaluation and dynamic optimization closed loop described in this invention. Detailed Implementation

[0021] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0022] One embodiment of the present invention, such as Figure 1 As shown, a charging method for a wireless charger includes:

[0023] S1. Design a multi-coil array layout for the wireless charger and generate multi-coil array layout data; deploy an electromagnetic field imaging module and a device fingerprint recognition module based on the multi-coil array layout data to build an intelligent charging architecture with electromagnetic field sensing and device recognition capabilities.

[0024] S2. Based on the intelligent charging architecture, perform electromagnetic field spatial scanning on the charging area to generate electromagnetic field distribution data; extract features of devices entering the charging area through the device fingerprint recognition module to obtain device fingerprint feature data; combine the electromagnetic field distribution data and the device fingerprint feature data to determine the location information of multiple devices in the charging area.

[0025] S3. Perform energy focusing calculations based on the arbitrary location information of multiple devices to generate energy focusing parameters for each device; dynamically adjust the power distribution of each coil in the multi-coil array according to the energy focusing parameters to achieve efficient energy transmission of multiple devices at any location and obtain charging efficiency data of multiple devices; at the same time, start the online frequency tracking module to monitor the load change of the receiving end, the fluctuation of the coil coupling coefficient and the temperature drift in real time, and adaptively adjust the working frequency according to the monitoring results to generate adaptive frequency data to reduce detuning loss;

[0026] S4. Based on electromagnetic field distribution data, equipment fingerprint feature data, and adaptive frequency data, a three-level foreign object protection process is implemented. Level 1 protection analyzes changes in electromagnetic field distribution to initially determine the presence of foreign objects. Level 2 protection combines equipment fingerprint feature data to distinguish legitimate equipment from suspected foreign objects. Level 3 protection utilizes power and voltage characteristics under adaptive frequency data to accurately identify metallic foreign objects and generate foreign object detection result data. If a foreign object is detected, the energy transmission strategy is immediately adjusted to avoid safety hazards.

[0027] S5. Based on the charging efficiency data of multiple devices and the foreign object detection results, a comprehensive evaluation is performed to generate comprehensive evaluation data on charging safety and energy efficiency; based on the comprehensive evaluation data on charging safety and energy efficiency, a charging optimization instruction is generated to dynamically optimize the charging process of the wireless charger; at the same time, charging status feedback information is generated based on the comprehensive evaluation data to provide users with an intuitive display of the charging status, thus completing the intelligent charging process of the wireless charger.

[0028] The working principle and effects of the above technical solution are as follows: Through multi-coil array layout design and intelligent charging architecture construction, combined with electromagnetic field scanning and device fingerprint recognition, precise positioning of multiple devices can be achieved, which can significantly improve the charging efficiency of multiple devices at any location and avoid charging interruptions or inefficiencies caused by improper device placement; the coil power distribution is dynamically adjusted based on positioning information, coupled with online frequency adaptive adjustment, which effectively reduces energy loss caused by frequency detuning and reduces energy waste; the three-level foreign object protection is progressive, which can accurately identify various foreign objects, enhance the safety of the charging process, and avoid safety hazards such as overheating and short circuits caused by foreign objects; dynamic optimization based on comprehensive evaluation data can not only continuously improve charging energy efficiency, but also adjust the strategy in real time according to the safety status; at the same time, the charging status feedback allows users to intuitively grasp the charging progress and safety status, avoiding user confusion caused by information asymmetry and significantly improving the user experience.

[0029] In one embodiment of the present invention, S1 includes:

[0030] S11. Based on charging scenario requirements (such as desktop, in-vehicle, and simultaneous charging of multiple devices), the number of coils, arrangement spacing, and coil diameter of the wireless charger are iteratively designed in multiple dimensions to generate multiple sets of candidate coil array layout data.

[0031] S12. Perform electromagnetic field simulation and energy transfer efficiency simulation on multiple sets of layout candidate data, select the optimal layout scheme, and generate the final multi-coil array layout data.

[0032] S13. Based on the optimal layout data, deploy a high-resolution electromagnetic field imaging module in the corresponding area of ​​the coil array to achieve real-time capture of the electromagnetic field in the charging space.

[0033] S14. Based on the coil distribution logic of the layout data, deploy multi-node device fingerprint recognition modules at the edge of the charging area to build a device recognition network covering the entire charging range;

[0034] S15. The electromagnetic field imaging module and the device fingerprint recognition module are interconnected with the main control unit through an industrial bus to integrate sensing, recognition and control functions, and generate an intelligent charging architecture with electromagnetic field sensing and device recognition capabilities.

[0035] The working principle and effects of the above technical solution are as follows: Multi-dimensional parameter iterative design based on charging scenario requirements significantly improves the adaptability of coil array layout to different scenarios such as desktops and vehicles, avoiding problems such as incomplete charging coverage and energy waste that occur with general layouts in specific scenarios; Electromagnetic field simulation and efficiency simulation are used to select the optimal solution, improving the reliability of the layout design, reducing rework and adjustment costs caused by unreasonable layouts after actual deployment, and avoiding the hidden danger of inefficient energy transmission caused by blind design; The precise deployment of high-resolution electromagnetic field imaging modules and multi-node device fingerprint recognition networks enhances the real-time performance of electromagnetic field capture in the charging space and the comprehensiveness of device recognition, avoiding blind spots and perception lag; The interconnection and integration of each module with the main control unit realizes the coordinated linkage of sensing, recognition, and control functions, significantly enhancing the intelligence level of the charging architecture and avoiding response disconnect and functional redundancy problems caused by independent operation of each component.

[0036] In one embodiment of the present invention, S11 includes:

[0037] Analyze the spatial constraints, charging power requirements, and device compatibility requirements of different charging scenarios (desktop, vehicle, and simultaneous charging of multiple devices) to generate core requirement index data for each scenario.

[0038] Based on the core requirement indicators of the scenario, the optional parameter ranges for the number of wireless charger coils, the arrangement spacing, and the coil diameter are determined, and a multi-dimensional parameter design interval dataset is generated.

[0039] Based on the parameter design interval dataset, the orthogonal experimental method is used to perform multi-dimensional parameter combination iteration to generate multiple sets of initial coil array layout candidate data;

[0040] Physical feasibility checks (such as space occupation and coil interference checks) are performed on multiple sets of initial candidate data to eliminate invalid schemes and finally generate multiple sets of coil array layout candidate data.

[0041] The working principle and effects of the above technical solution are as follows: By accurately analyzing the core requirements of different charging scenarios and generating indicator data, the coil array layout design can closely fit the actual requirements of desktop, vehicle and other scenarios, avoiding blind design that is out of touch with the scenario and significantly improving the scenario adaptability of the layout solution; Determining the parameter design range based on the requirement indicators can effectively narrow the parameter selection range, reduce the design redundancy caused by invalid parameter combinations, and improve the efficiency of subsequent iterative design; Using the orthogonal experimental method for parameter combination iteration can comprehensively cover the key combinations of multi-dimensional parameters, enhance the diversity and rationality of candidate data, and avoid missing potential optimal layout solutions; Conducting physical feasibility verification in advance and eliminating invalid solutions can reduce the useless work in the subsequent simulation verification stage, reduce design costs, and avoid the situation where the generated solution cannot be implemented due to space occupation conflicts, coil interference and other issues, thus ensuring the feasibility and reliability of the layout design.

[0042] One embodiment of the present invention, such as Figure 2 As shown, S2 includes:

[0043] S21. The main control unit based on the intelligent charging architecture sends a scanning command to the electromagnetic field imaging module to perform a point-by-point scanning of the three-dimensional electromagnetic field space of the charging area and generate a high-density electromagnetic field distribution data matrix.

[0044] S22. Through the radio frequency sensing unit of the device fingerprint recognition module, an identity detection request is initiated for the device entering the charging area, and the core features of the device are extracted. The core features include the wireless communication protocol version, battery type, and receiving coil parameters to obtain device fingerprint feature data.

[0045] S23. Input the electromagnetic field distribution data matrix and the device fingerprint feature data into the location solution model, and determine the three-dimensional coordinate information of a single device or multiple devices in the charging area through the correlation analysis of the electromagnetic field intensity gradient change and the attenuation law of the device feature signal.

[0046] S24. Perform real-time filtering on the three-dimensional coordinate information to eliminate position fluctuations caused by environmental interference and generate accurate arbitrary position information of multiple devices within the charging area.

[0047] The working principle and effects of the above technical solution are as follows: The high-density data matrix generated by point-by-point scanning of the three-dimensional electromagnetic field significantly improves the comprehensiveness and accuracy of electromagnetic field sensing in the charging area, avoiding blind spots in traditional scanning methods and providing reliable data support for subsequent positioning; extracting multi-dimensional core features of the device to generate fingerprint data enhances the uniqueness and accuracy of device identification, avoiding confusion or misjudgment of different devices; calculating the position through correlation analysis of electromagnetic field and fingerprint data improves the accuracy of three-dimensional coordinate positioning for single / multiple devices, avoiding deviations caused by positioning based on a single data dimension. Real-time filtering eliminates environmental interference, reducing fluctuations in position information and preventing subsequent energy distribution imbalances due to inaccurate positioning; the entire process can quickly capture device positions while ensuring positioning accuracy, laying the foundation for efficient charging of multiple devices at any location, and simultaneously avoiding…

[0048] In one embodiment of the present invention, step S23 includes:

[0049] The electromagnetic field distribution data matrix is ​​subjected to noise filtering and data standardization to remove abnormal data points caused by environmental electromagnetic interference, and a purified electromagnetic field distribution data matrix is ​​generated.

[0050] The device fingerprint feature data is subjected to feature enhancement processing to extract key derived features, including signal attenuation coefficient and feature signal strength, to generate an enhanced device fingerprint feature dataset.

[0051] The purified electromagnetic field distribution data matrix and the enhanced device fingerprint feature dataset are synchronously input into the location solution model to initialize the model correlation analysis parameters.

[0052] The correlation coefficient between the change in electromagnetic field intensity gradient and the attenuation law of the equipment's characteristic signals is calculated by modeling, and a location-signal correlation mapping relationship is constructed.

[0053] Based on the correlation mapping relationship, the spatial coordinates of the device are inferred to determine the three-dimensional coordinate information of a single device or multiple devices within the charging area.

[0054] The working principle and effects of the above technical solution are as follows: By performing noise filtering and standardization on the electromagnetic field distribution data matrix, abnormal data points caused by environmental electromagnetic interference are effectively eliminated, significantly improving the purity and reliability of the data and avoiding the distortion of basic positioning data caused by interference signals. The device fingerprint features are enhanced, and key derived features such as signal attenuation coefficients and feature signal strengths are extracted, further enriching the device feature dimensions and enhancing the identification of device signals, allowing the model to more accurately capture the correlation between the device and its spatial location. The purified data and enhanced features are simultaneously input into the model and the correlation parameters are initialized, ensuring the accuracy of the analysis starting point and reducing positioning errors caused by parameter deviations. By calculating correlation coefficients to construct a mapping relationship between location and signal correlation, the coordinate back-calculation becomes more logically supported, improving the accuracy of three-dimensional coordinate calculations and avoiding positioning ambiguity caused by single-dimensional analysis. The entire process ensures data quality and enhances the rigor of the positioning logic, effectively reducing deviations and fluctuations in single / multi-device positioning, laying a solid foundation for subsequent accurate energy allocation, and avoiding inefficient charging or energy waste caused by inaccurate positioning.

[0055] In one embodiment of the present invention, S3 includes:

[0056] S31. Based on the precise arbitrary location information of multiple devices, combined with the remaining battery power and rated charging power requirements of each device, perform energy focusing algorithm calculations to generate core parameters for each device. The core parameters include energy focusing angle and focusing intensity.

[0057] S32. Based on the energy focusing parameters, the main control unit sends power distribution commands to each coil in the multi-coil array to dynamically adjust the coil on / off state and current magnitude, perform directional energy focusing and transmission, and obtain the initial charging efficiency data of multiple devices.

[0058] S33. Based on the initial charging efficiency data, start the dynamic power calibration module, compare the deviation between the actual charging efficiency and the theoretical value in real time, iteratively optimize the power distribution ratio of each coil, and generate optimal charging efficiency data for multiple devices.

[0059] S34. Synchronously start the online frequency tracking module to monitor the changes in the load impedance of the receiving end, the fluctuation of the coupling coefficient between coils, and the ambient temperature drift in the charging area in real time, and collect multi-dimensional monitoring data.

[0060] S35. Input the monitoring data into the frequency adaptive algorithm, dynamically adjust the working frequency of the wireless charger through the PID adjustment mechanism, generate adaptive frequency matching data, and minimize the energy loss caused by frequency detuning.

[0061] The working principle and effects of the above technical solution are as follows: Energy focusing parameters are generated by combining the precise location of multiple devices with actual needs such as battery capacity and rated power. This allows energy allocation to better suit the characteristics of each device, improving the targeted nature of energy transmission and avoiding energy waste caused by blind allocation. Dynamic adjustment of coil on / off states and current magnitude based on these parameters enables directional energy focusing and transmission, significantly improving the initial charging efficiency of multiple devices and avoiding inefficiency caused by energy dispersion. Dynamic power calibration and iterative optimization further reduce the deviation between actual and theoretical efficiency, enhancing the stability of charging efficiency and preventing excessive efficiency fluctuations from affecting the charging experience. Simultaneous monitoring of multi-dimensional data such as load, coupling coefficient, and temperature, combined with PID control to adaptively adjust the operating frequency, effectively reduces energy loss caused by frequency detuning, lowers energy waste, and prevents charging interruptions or equipment damage due to changes in operating conditions. The entire process ensures efficient charging of multiple devices at any location while adapting to dynamic changes in operating conditions in real time, balancing charging efficiency and energy consumption control, providing strong support for subsequent safe and stable charging.

[0062] In one embodiment of the present invention, S32 includes:

[0063] The energy focusing parameters are analyzed, and key information corresponding to each device is extracted. The key information includes the focusing angle adaptation coil group and the focusing intensity matching current threshold, and a parameter analysis comparison table is generated.

[0064] Based on the parameter analysis lookup table, the main control unit constructs the coil power distribution logic, generates on / off control signals and current adjustment amplitude instructions for each coil, and forms a multi-coil precise control instruction set;

[0065] The control instruction set is synchronously sent to the drive module of the multi-coil array via the industrial bus, the instruction response status of the drive module is obtained, and instruction execution feedback data is generated.

[0066] The drive module adjusts the on / off state and current magnitude of the corresponding coil according to the instructions to construct a directional energy focusing field and realize precise directional energy focusing and transmission to each device;

[0067] Real-time collection of charging voltage and current data from each device, calculation of initial energy transfer efficiency, and generation of initial charging efficiency data for multiple devices.

[0068] The working principle and effects of the above technical solution are as follows: Precise analysis of energy focusing parameters and generation of a lookup table clearly identifies the corresponding compatible coil groups and current thresholds for each device, avoiding control deviations caused by parameter information confusion and providing a clear basis for subsequent power allocation; Based on the lookup table, coil power allocation logic is constructed and a precise control instruction set is generated, enhancing the targeting and logic of coil control, avoiding coil malfunctions caused by instruction confusion, and improving the orderly operation of multi-coil collaboration; Synchronous instruction issuance and execution feedback via industrial bus ensure the timeliness and reliability of instruction transmission, reducing energy transmission interruptions caused by instruction delays or loss, and facilitating timely detection of execution anomalies; The drive module precisely adjusts the coil state according to the instructions to construct a directional focusing field, significantly improving the directionality and accuracy of energy transmission, avoiding waste caused by energy dispersion, and ensuring that each device receives a matching energy supply; Real-time data acquisition generates initial charging efficiency data, enabling timely understanding of energy transmission effects, providing accurate basic data for subsequent dynamic power calibration, avoiding blind optimization without a basis, and ensuring a steady improvement in charging efficiency.

[0069] In one embodiment of the present invention, S33 includes:

[0070] Extract core metrics (such as energy transfer conversion rate and charging power stability) from the initial charging efficiency data of multiple devices to generate a standardized efficiency evaluation dataset.

[0071] The dynamic power calibration module is activated to retrieve the theoretical charging efficiency thresholds for each device and generate baseline data for efficiency deviation comparison.

[0072] The standardized efficiency assessment dataset is compared with the deviation comparison benchmark data on a device-by-device basis to calculate the deviation between the actual efficiency and the theoretical value, and an efficiency deviation analysis table is generated.

[0073] Based on the efficiency deviation analysis table, a preliminary adjustment scheme for the power allocation of each coil is formulated using the gradient descent algorithm, and a draft of the power optimization instruction is generated.

[0074] The initial draft of the power optimization command is sent to the main control unit. After adjusting the coil power distribution ratio, new charging efficiency data is collected to verify the optimization effect.

[0075] The process involves iterative steps of repeated deviation calculation, optimization adjustment, and effect verification until the efficiency deviation is less than a preset threshold, ultimately generating optimal charging efficiency data for multiple devices.

[0076] The working principle and effects of the above technical solution are as follows: Extracting core efficiency indicators to generate a standardized evaluation dataset provides a unified reference standard for efficiency evaluation, avoiding evaluation biases caused by disorganized indicators and inconsistent definitions, and laying a reliable foundation for subsequent deviation analysis. Retrieving the theoretical efficiency thresholds of each device as a comparison benchmark makes the calculation of deviations between actual and theoretical values ​​more targeted, avoiding misjudgments caused by using a uniform standard to measure different devices. Generating a deviation analysis table by comparing each device accurately pinpoints the power allocation problem for each device, improving the accuracy of deviation tracing and avoiding the problem of general analysis failing to find optimization directions. Employing a gradient descent algorithm to formulate adjustment schemes enhances the scientific rigor and accuracy of power optimization, reduces the blindness caused by experience-based adjustments, and makes optimization instructions more aligned with actual needs. Through repeated iterative verification until the deviation reaches the target, the efficiency gap can be continuously narrowed, significantly improving the optimality and stability of multi-device charging efficiency, and avoiding residual deviations in the initial optimization scheme that lead to inefficient charging. The entire process ensures that each device achieves ideal charging efficiency while avoiding energy waste caused by over-optimization, providing a strong guarantee for efficient collaborative charging of multiple devices.

[0077] In one embodiment of the present invention, step S4 includes:

[0078] S41. Based on the electromagnetic field distribution data matrix, monitor the abnormal changes in the electromagnetic field distribution within the charging area in real time, and preliminarily screen out suspicious areas that may contain foreign objects by threshold judgment, complete the first-level foreign object protection judgment, and generate foreign object suspected area data.

[0079] S42. Combining the device fingerprint feature data, verify the identity of the signal source in the suspected foreign object area, distinguish between legitimate charging devices and suspected foreign objects without matching fingerprints, complete the secondary foreign object protection identification, and generate foreign object identification result data.

[0080] S43. Based on adaptive frequency matching data, collect power and voltage response curves in the suspected area, compare them with the preset characteristic curves of metallic and non-metallic foreign objects, accurately identify the type of foreign object (metallic, non-metallic, harmless, and harmful), complete the confirmation of three-level foreign object protection, and generate foreign object detection result data.

[0081] S44. If the foreign object detection results show the presence of harmful foreign objects, the main control unit immediately sends an energy cut-off or power drop command to adjust the energy transmission strategy. At the same time, the alarm module is activated to send a foreign object warning signal to avoid safety hazards.

[0082] The working principle and effects of the above technical solution are as follows: The three-tiered foreign object protection system progressively enhances the safety of the charging process. Level 1 protection monitors abnormal electromagnetic field changes to screen suspicious areas, quickly identifying potential foreign object risks and providing a clear initial direction for foreign object detection, avoiding missed or delayed detections due to large-scale blind searches. Level 2 protection combines device fingerprint verification to effectively distinguish legitimate devices from suspected foreign objects, reducing the chance of misidentifying normal charging devices as foreign objects, improving the accuracy of foreign object identification, and preventing unnecessary charging interruptions. Level 3 protection accurately identifies foreign object types through power and voltage response curve comparison, clearly distinguishing between harmful and harmless foreign objects, avoiding over-protection or omissions of dangerous foreign objects, making protection more targeted. Once a harmful foreign object is detected, the energy transmission strategy is immediately adjusted and an alarm is triggered, quickly curbing safety hazards and preventing overheating, short circuits, and other problems caused by foreign objects from damaging the equipment or causing safety accidents. The entire solution comprehensively covers foreign object detection scenarios while precisely controlling the protection level, balancing charging continuity and safety, giving users greater peace of mind.

[0083] In one embodiment of the present invention, S43 includes:

[0084] The adaptive frequency matching data is preprocessed to remove noise and interference signals caused by frequency fluctuations, and purified adaptive frequency reference data is generated.

[0085] Based on the purified adaptive frequency reference data, the control signal acquisition module acquires power and voltage response signals in the suspicious area to generate the original power and voltage response curve dataset.

[0086] The original response curve dataset is smoothed and feature points are extracted, retaining key features such as peak value and slope, to generate standardized power and voltage response curves.

[0087] Retrieve the preset standard feature curve library of metallic foreign objects, non-metallic foreign objects, harmless foreign objects, and harmful foreign objects, and generate a foreign object feature comparison benchmark set;

[0088] The dynamic time warping algorithm is used to match the standardized response curve with various curves in the comparison benchmark set to calculate the matching confidence. Based on the matching confidence threshold, the foreign object category is determined, the three-level foreign object protection confirmation is completed, and foreign object detection result data containing foreign object type, location and risk level is generated.

[0089] The working principle and effects of the above technical solution are as follows: Noise reduction preprocessing of adaptive frequency matching data effectively eliminates interference signals caused by frequency fluctuations, significantly improving the purity of the frequency reference data and preventing deviations in subsequent signal acquisition and comparison caused by interference signals, thus laying a reliable foundation for accurate identification; based on the purified data acquisition of power and voltage response signals and the generation of standardized curves, smoothing and feature point extraction make the key information of the curves clearer, reducing comparison interference caused by the clutter of the original data and improving the recognizability of curve features; a well-categorized standard feature curve library is retrieved as the comparison benchmark, avoiding… It avoids misclassification of foreign objects due to the lack of a unified reference, making identification systematic; it adopts a dynamic time warping algorithm for similarity matching, which can accurately capture subtle differences between curves, improve matching confidence, and effectively reduce confusion between metal and non-metal, and harmful and harmless foreign objects; finally, it generates detection results that include foreign object type, location, and risk level, making protective measures more targeted and avoiding inappropriate protection due to ambiguous foreign object information. It accurately blocks the safety hazards caused by harmful foreign objects, and avoids charging interruptions caused by over-protection of harmless foreign objects, further improving the reliability and practicality of the three-level protection.

[0090] One embodiment of the present invention, such as Figure 3 As shown, S5 includes:

[0091] S51. Input the optimal charging efficiency data of multiple devices and the foreign object detection results into the comprehensive evaluation model, and quantify the scoring from three dimensions: charging energy efficiency, safety risk, and device compatibility to generate comprehensive evaluation data of charging safety and energy efficiency.

[0092] S52. Based on the energy efficiency score in the comprehensive evaluation data, generate charging optimization instructions, which include power adjustment and frequency fine-tuning, and dynamically iteratively optimize the energy transmission parameters of the wireless charger to continuously improve charging efficiency.

[0093] S53. Based on the safety score in the comprehensive evaluation data, monitor the temperature, voltage and current abnormalities in real time during the charging process. If any abnormality occurs, immediately trigger the protection mechanism and generate a safety protection command.

[0094] S54. Based on the comprehensive evaluation data, send charging status feedback information (including charging progress, remaining time, energy efficiency level, and safety status of each device) to the user terminal or the charger's built-in display screen via the wireless communication module.

[0095] S55 continuously cycles through evaluation, optimization, and feedback processes until all devices have finished charging or the user terminates charging, ultimately completing the smart charging process of the wireless charger.

[0096] The working principle and effects of the above technical solution are as follows: Quantitative evaluation from three dimensions—charging energy efficiency, safety risks, and device compatibility—enables a comprehensive grasp of the core status of the entire charging process, avoiding the one-sidedness of single-dimensional evaluation and providing accurate and reliable decision-making basis for subsequent adjustments; Dynamically iteratively optimizes power and frequency parameters based on energy efficiency scores, continuously improving charging efficiency, reducing energy waste, and avoiding the problem of inefficient charging in the later stages caused by fixed parameters being difficult to adapt to changes in operating conditions; Real-time monitoring of temperature, voltage, and current anomalies based on safety scores and triggering protection mechanisms can quickly respond to potential safety risks, effectively preventing equipment damage or safety accidents caused by overheating, voltage surges, etc.; Detailed charging status information is fed back to the user terminal or display screen, allowing users to intuitively grasp the progress, remaining time, and safety status of each device, avoiding user anxiety or misoperation caused by information asymmetry; Continuously cyclically executing the evaluation, optimization, and feedback process ensures dynamic adaptation to changes in devices and environment throughout the charging process, guaranteeing a steady improvement in the charging efficiency of multiple devices while constantly strengthening safety defenses, significantly improving the intelligence level of wireless charging and the user experience.

[0097] One embodiment of the present invention provides a wireless charger, comprising:

[0098] One or more processors;

[0099] Memory, used to store one or more programs;

[0100] When the one or more programs are executed by the one or more processors, the one or more processors implement the charging method described in any one of the above descriptions.

[0101] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A charging method for a wireless charger, characterized in that, The method includes: S1. Design a multi-coil array layout for the wireless charger, generate multi-coil array layout data, and deploy an electromagnetic field imaging module and a device fingerprint recognition module to build an intelligent charging architecture with electromagnetic field sensing and device recognition capabilities. S2. Based on the intelligent charging architecture, perform electromagnetic field spatial scanning on the charging area to generate electromagnetic field distribution data; extract features of devices entering the charging area through the device fingerprint recognition module to obtain device fingerprint feature data; combine the electromagnetic field distribution data and the device fingerprint feature data to determine the location information of multiple devices in the charging area. S3. Perform energy focusing calculations based on the arbitrary location information of multiple devices to generate energy focusing parameters for each device; dynamically adjust the power distribution of each coil in the multi-coil array according to the energy focusing parameters to obtain charging efficiency data for multiple devices; monitor the corresponding data in real time, and adaptively adjust the working frequency according to the monitoring results to generate adaptive frequency data; S4. Based on electromagnetic field distribution data, device fingerprint feature data, and adaptive frequency data, perform three-level foreign object protection processing; identify metallic foreign objects and generate foreign object detection result data; if a foreign object is detected, immediately adjust the energy transmission strategy to avoid safety hazards. S5. Based on the charging efficiency data of multiple devices and the foreign object detection results, a comprehensive evaluation is conducted to generate comprehensive evaluation data on charging safety and energy efficiency; based on the comprehensive evaluation data on charging safety and energy efficiency, a charging optimization instruction is generated to dynamically optimize the charging process of the wireless charger.

2. The charging method of a wireless charger according to claim 1, characterized in that, S1 includes: S11. Based on the charging scenario requirements, the number of coils, the arrangement spacing, and the coil diameter of the wireless charger are designed through multi-dimensional parameter iteration to generate multiple sets of candidate coil array layout data. S12. Perform electromagnetic field simulation and energy transfer efficiency simulation on multiple sets of layout candidate data, select the optimal layout scheme, and generate the final multi-coil array layout data. S13. Based on the optimal layout data, deploy a high-resolution electromagnetic field imaging module in the corresponding area of ​​the coil array to achieve real-time capture of the electromagnetic field in the charging space. S14. Based on the coil distribution logic of the layout data, deploy multi-node device fingerprint recognition modules at the edge of the charging area to build a device recognition network covering the entire charging range; S15. The electromagnetic field imaging module and the device fingerprint recognition module are interconnected with the main control unit through an industrial bus to integrate sensing, recognition and control functions, and generate an intelligent charging architecture with electromagnetic field sensing and device recognition capabilities.

3. The charging method of a wireless charger according to claim 1, characterized in that, The S2 includes: S21. The main control unit based on the intelligent charging architecture sends a scanning command to the electromagnetic field imaging module to perform a point-by-point scanning of the three-dimensional electromagnetic field space of the charging area and generate a high-density electromagnetic field distribution data matrix. S22. Through the radio frequency sensing unit of the device fingerprint recognition module, an identity detection request is initiated for the device entering the charging area, the core features of the device are extracted, and the device fingerprint feature data is obtained. S23. Input the electromagnetic field distribution data matrix and the device fingerprint feature data into the location solution model, and determine the three-dimensional coordinate information of a single device or multiple devices in the charging area through the correlation analysis of the electromagnetic field intensity gradient change and the attenuation law of the device feature signal. S24. Perform real-time filtering on the three-dimensional coordinate information to generate precise arbitrary location information of multiple devices within the charging area.

4. The charging method of a wireless charger according to claim 3, characterized in that, S23 includes: The electromagnetic field distribution data matrix is ​​subjected to noise filtering and data standardization to remove abnormal data points caused by environmental electromagnetic interference, and a purified electromagnetic field distribution data matrix is ​​generated. Feature enhancement processing is performed on device fingerprint feature data to extract key derived features and generate enhanced device fingerprint feature dataset; The purified electromagnetic field distribution data matrix and the enhanced device fingerprint feature dataset are synchronously input into the location solution model to initialize the model correlation analysis parameters. The correlation coefficient between the change in electromagnetic field intensity gradient and the attenuation law of the equipment's characteristic signals is calculated by modeling, and a location-signal correlation mapping relationship is constructed. Based on the correlation mapping relationship, the spatial coordinates of the device are inferred to determine the three-dimensional coordinate information of a single device or multiple devices within the charging area.

5. The charging method of a wireless charger according to claim 1, characterized in that, The S3 includes: S31. Based on the precise location information of multiple devices, combined with the remaining battery power and rated charging power requirements of each device, perform energy focusing algorithm calculations to generate core parameters for each device. S32. Based on the energy focusing parameters, the main control unit sends power distribution commands to each coil in the multi-coil array to dynamically adjust the coil on / off state and current magnitude, perform directional energy focusing and transmission, and obtain the initial charging efficiency data of multiple devices. S33. Based on the initial charging efficiency data, start the dynamic power calibration module, compare the deviation between the actual charging efficiency and the theoretical value in real time, iteratively optimize the power distribution ratio of each coil, and generate optimal charging efficiency data for multiple devices. S34. Synchronously start the online frequency tracking module to monitor the changes in the load impedance of the receiving end, the fluctuation of the coupling coefficient between coils, and the ambient temperature drift in the charging area in real time, and collect multi-dimensional monitoring data. S35. Input the monitoring data into the frequency adaptive algorithm, and dynamically adjust the operating frequency of the wireless charger through the PID adjustment mechanism to generate adaptive frequency matching data.

6. The charging method of a wireless charger according to claim 5, characterized in that, S32 includes: The energy focusing parameters are analyzed, key information corresponding to each device is extracted, and a parameter analysis comparison table is generated. Based on the parameter analysis lookup table, the main control unit constructs the coil power distribution logic, generates on / off control signals and current adjustment amplitude instructions for each coil, and forms a multi-coil precise control instruction set; The control instruction set is synchronously sent to the drive module of the multi-coil array via the industrial bus, the instruction response status of the drive module is obtained, and instruction execution feedback data is generated. The drive module adjusts the on / off state and current magnitude of the corresponding coil according to the instructions to construct a directional energy focusing field and realize precise directional energy focusing and transmission to each device; Real-time collection of charging voltage and current data from each device, calculation of initial energy transfer efficiency, and generation of initial charging efficiency data for multiple devices.

7. The charging method of a wireless charger according to claim 1, characterized in that, The S4 includes: S41. Based on the electromagnetic field distribution data matrix, monitor the abnormal changes in the electromagnetic field distribution within the charging area in real time, and preliminarily screen out suspicious areas that may contain foreign objects by threshold judgment, complete the first-level foreign object protection judgment, and generate foreign object suspected area data. S42. Combining the device fingerprint feature data, verify the identity of the signal source in the suspected foreign object area, distinguish between legitimate charging devices and suspected foreign objects without matching fingerprints, complete the secondary foreign object protection identification, and generate foreign object identification result data. S43. Based on adaptive frequency matching data, collect the power and voltage response curves in the suspicious area, compare them with the preset characteristic curves of metallic and non-metallic foreign objects, accurately identify the type of foreign object, complete the confirmation of level three foreign object protection, and generate foreign object detection result data. S44. If the foreign object detection results show the presence of harmful foreign objects, the main control unit immediately sends an energy cut-off or power drop command to adjust the energy transmission strategy, and at the same time activates the alarm module to send a foreign object warning signal.

8. The charging method of a wireless charger according to claim 7, characterized in that, S43 includes: The adaptive frequency matching data is preprocessed to remove noise and interference signals caused by frequency fluctuations, and purified adaptive frequency reference data is generated. Based on the purified adaptive frequency reference data, the control signal acquisition module acquires power and voltage response signals in the suspicious area to generate the original power and voltage response curve dataset. The original response curve dataset is smoothed and feature points are extracted, retaining key features such as peak value and slope, to generate standardized power and voltage response curves. Retrieve the preset standard feature curve library of metallic foreign objects, non-metallic foreign objects, harmless foreign objects, and harmful foreign objects, and generate a foreign object feature comparison benchmark set; The dynamic time warping algorithm is used to match the standardized response curve with various curves in the comparison benchmark set to calculate the matching confidence. Based on the matching confidence threshold, the foreign object category is determined, the three-level foreign object protection confirmation is completed, and foreign object detection result data containing foreign object type, location and risk level is generated.

9. The charging method of a wireless charger according to claim 1, characterized in that, The S5 includes: S51. Input the optimal charging efficiency data of multiple devices and the foreign object detection results into the comprehensive evaluation model, and quantify the scoring from three dimensions: charging energy efficiency, safety risk, and device compatibility to generate comprehensive evaluation data of charging safety and energy efficiency. S52. Based on the energy efficiency score in the comprehensive evaluation data, generate charging optimization instructions to dynamically iterate and optimize the energy transmission parameters of the wireless charger to continuously improve charging efficiency. S53. Based on the safety score in the comprehensive evaluation data, monitor the temperature, voltage and current abnormalities in real time during the charging process. If any abnormality occurs, immediately trigger the protection mechanism and generate a safety protection command. S54. Based on the comprehensive evaluation data, send charging status feedback information to the user terminal or the charger's built-in display screen via the wireless communication module. S55 continuously cycles through evaluation, optimization, and feedback processes until all devices have finished charging or the user terminates charging, ultimately completing the smart charging process of the wireless charger.

10. A wireless charger, comprising: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the charging method according to any one of claims 1 to 9.

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