NFC-based wireless charging control method and system

Abstract

The application discloses a wireless charging control method and system based on NFC, relates to the technical field of wireless power transmission, and comprises the following steps: S100, before starting the wireless charging device, a dynamic analysis baseline of a multi-source near field communication induction spectrum is constructed, a continuous time domain signal and a frequency domain signal are used to reconstruct an energy flow trajectory, coupled paths, phase shifts and amplitude abnormal data generated in an induction field are extracted, and a holographic reference data surface is generated; S200, based on the holographic reference data surface, interference signal attribution recognition processing is performed, a differential decoupling method is applied to separate feedback signals and interference components, signal consistency weights are calculated according to time sequence continuity, and a misjudgment matrix is generated. The application constructs a holographic reference data surface, introduces a misjudgment matrix and an identity lock frequency mechanism, accurately identifies effective signals, combines counterfactual playback and topology reconstruction, realizes dynamic self-adjustment of a controller, improves anti-interference and charging stability, and guarantees the safe and intelligent operation of multi-device wireless charging.

Description

Technical Field

[0001] This invention relates to the field of wireless power transmission technology, and more specifically to a wireless charging control method and system based on NFC. Background Technology

[0002] NFC-based wireless charging control is an intelligent control method that deeply integrates Near Field Communication (NFC) technology with wireless charging technology. Its core idea is to introduce NFC's short-range, high-security communication capabilities into the identification, communication, and energy transfer control stages of charging devices, enabling automatic interaction and dynamic adjustment between the charging and receiving devices. By establishing an optimized NFC protocol stack at the software level, this method can achieve millisecond-level automatic identification, parameter synchronization, and charging permission confirmation when devices are close together, eliminating cumbersome manual pairing steps. At the energy control level, the system senses the relative position and distance between the two devices in real time based on the NFC signal strength, and executes a dynamic power adjustment algorithm to maintain efficient and stable energy transfer based on the charging needs of the receiving device. Simultaneously, the method incorporates foreign object detection and safety protection mechanisms. By monitoring abnormal fluctuations in the NFC signal, it identifies metallic foreign objects or abnormal media, automatically interrupting the charging circuit to prevent safety hazards such as overheating and short circuits. Overall, this method achieves automation, intelligence, and safety in the wireless charging process, significantly improving user experience and energy utilization efficiency, and is suitable for various short-range wireless power supply scenarios such as smartphones, smart wearable devices, and smart home appliances.

[0003] The existing technology has the following shortcomings: In existing technologies, when multiple wireless charging devices operate simultaneously in the same space, their sensing fields may experience complex superposition and interference phenomena due to the reliance on Near Field Communication (NFC) signals for device identification and power control. Existing technologies typically rely on fixed thresholds or single signal channels to distinguish between valid feedback signals and external interference signals. However, in dynamic operating scenarios, when the NFC sensing fields of devices momentarily overlap, the system is highly susceptible to misinterpreting interference signals from other devices as normal feedback signals from its own receiver. This misinterpretation leads to incorrect power closed-loop regulation by the control logic, creating an unexpected self-oscillating path. This causes the power amplifier to continuously output excessive energy for a very short period, resulting in transient overload. If this oscillation is not suppressed in time, it will cause power transistor breakdown in the transmitter drive circuit, instability in the energy feedback chain, and in severe cases, thermal runaway and electromagnetic coupling anomalies, seriously impacting device safety and system reliability.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide an NFC-based wireless charging control method and system to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an NFC-based wireless charging control method, comprising the following steps: S100 constructs a dynamic analytical baseline of the multi-source near-field communication sensing spectrum before the wireless charging device is activated, reconstructs the energy flow trajectory using continuous time-domain and frequency-domain signals, extracts coupling paths, phase shifts, and amplitude anomaly data generated in the sensing field, and generates a holographic reference data surface with tracking capabilities. S200, based on holographic reference data plane, performs interference signal attribution and identification processing, applies differential decoupling to separate feedback signal and interference components, and calculates signal consistency weight based on time series continuity to generate a misjudgment matrix with risk level indicators; The S300 uses a misjudgment matrix to establish an identity-locked frequency linkage control process, generates a uniquely bound interactive signal channel, and blocks unexpected feedback paths by setting a time interval window and an energy identification threshold, thus completing the purification process of the communication signal loop. After the communication signal loop is purified, the S400 performs a counterfactual playback calibration operation based on the historical energy trajectory to replay the high-risk transient state. It dynamically corrects the gain parameters, phase boundary and power ramp rate of the control loop by combining the historical energy evolution characteristics, so that the controller output characteristics converge to the stable range. Based on continuous feedback of calibration results, the S500 performs adaptive topology reconstruction adjustment. By introducing staggered resonance paths and phase conjugate interference suppression chains, it guides energy reflection and superimposes synchronous bias signals to construct a self-healing follower-type oscillation suppression mechanism, eliminating self-excited oscillations under multi-device sensing fields and maintaining system stability.

[0007] Preferably, step S100 includes: Before the wireless charging device is activated, the near-field communication signal transmission and detection component is activated to perform a full-band scan of the electromagnetic response in space, collect continuous time-domain signals and frequency-domain signals and perform normalization processing. After data acquisition is completed, frequency domain transformation is performed on the signal within each time domain sampling window to construct a joint frequency and time response spectrum, and energy distribution characteristics and phase spectrum features are extracted to determine the order of magnitude and coupling degree of the sensing source. Based on the joint frequency and time spectrum, the time sampling period is extended to extract peak abrupt signal points, track the sensing path and calculate the phase offset difference and waveform overlap to generate a path coupling feature vector. After obtaining the path coupling feature vector, the data is normalized and sorted according to the spatial location mapping relationship and energy flow direction. A holographic reference data surface is constructed with three-dimensional spatial coordinates, phase displacement and amplitude change values ​​as elements, and a time-series trajectory map is established by combining time sampling points.

[0008] Preferably, step S200 includes: Based on the holographic reference data surface, the spatial coordinates, phase displacement and amplitude change information of the calibration path are extracted to construct a set of basic signal flow sequences. The temporal position of any feedback signal is matched with the reference path for bidirectional comparison to identify abnormal interference trajectories. After matching is completed, the difference residual signal between the reference path signal and the current feedback signal is extracted. The phase offset angle, energy change rate and spatial projection overlap are analyzed to separate the interference residual signal and form the interference feature vector. For the feedback signal segment after differential decoupling, multiple sampling periods are extended based on the continuity of the time series. The similarity scores of energy trend, phase smoothness and delay structure are calculated to determine their consistency in the time dimension. The scoring results and feature vectors of the feedback signal and the interference signal are summarized to construct a two-dimensional misjudgment matrix with the sampling time window as the horizontal axis and the signal path identifier as the vertical axis, and the risk value, difference value and similarity score of each signal segment are output.

[0009] Preferably, the interference feature vector extracted during the differential decoupling process includes directional parameters, amplitude variation parameters, and time delay parameters. When the consistency score of the feedback signal is higher than that of the interference residual signal and remains stable within the continuous sampling period, the feedback signal is confirmed as a valid feedback signal.

[0010] Preferably, step S300 includes: Based on the misjudgment matrix, signal identifiers with high consistency scores, low risk levels and minimum amplitude deviations are extracted, a set of trustworthy paths is selected, and each trustworthy path is assigned an identity code consisting of frequency domain identifier, spatial source location, phase response characteristics and amplitude stability. An interactive signal channel is established based on the identity code and is completely bound to the frequency. A frequency locking protocol is introduced during the communication process to limit the range of the signal frequency and monitor its amplitude fluctuation and phase consistency in real time to ensure the uniqueness and stability of the interactive channel. During the interaction, a time interval window is set, and combined with the energy identification threshold strategy, the feedback signal is filtered by both time and energy to remove signals that are not within the effective time window or have abnormal energy. After completing frequency locking, identity matching, time filtering, and energy discrimination, a final set of feedback signals is formed. Before control feedback, the signal source and parameters are verified to ensure that the control chain only receives reliable feedback signals.

[0011] Preferably, step S400 includes: The system calls upon the energy evolution data of the feedback signal saved during the communication interaction, filters out historical high-risk transient trajectories based on the high-risk level indicators in the misjudgment matrix, and extracts the energy rise rate, peak amplitude, phase shift amplitude, and response delay structure information. Based on the extracted historical energy trajectory, it is injected into the control chain in chronological order for simulation interaction. The deviation of the historical trajectory from the current control response in terms of gain, phase and energy slope is compared to generate an error correction vector. Based on the error correction vector, the gain adjustment unit, phase boundary judgment unit and power output control unit in the control chain are adjusted one by one, and the parameter correction range and boundary limit are set to prevent over-correction from causing false triggering. The modified control parameters are injected into the closed-loop control chain for simulation verification. The adaptive capability of the control parameters under complex disturbances is confirmed by dynamic reenactment of typical high-risk trajectories. Multi-level thresholds and backoff mechanisms are established before solidification.

[0012] Preferably, the gain adjustment value in the error correction vector is set according to the maximum rate of change of energy ramp rate in the historical trajectory, the phase boundary compression value is set according to the upper limit of the stable offset interval, and the power ramp rate is limited according to the minimum oscillation gradient interval.

[0013] Preferably, step S500 includes: Based on the calibrated control parameters, the timing relationship, phase delay and frequency distribution of each energy path in the induction field are analyzed, signal pairs with resonance risk are identified, staggered resonance paths are established and dynamic time delay windows are set to avoid energy peak overlap. Based on the staggered resonant path, a compensation signal with the same frequency but opposite phase as the high interference path is introduced to construct a phase conjugate interference suppression chain. By setting a time synchronization tag and an energy amplitude adjustment factor, interference coverage is achieved. A synchronous energy return mechanism is introduced, and a switchable bypass channel is set on the energy output path to guide energy exceeding the threshold to the secondary path to form a return flow. A synchronous bias signal with a slight frequency difference is inserted during the peak period of the path to disperse harmonic overlap. After the above operations are completed, a follow-up oscillation suppression structure is constructed. The structure is reconstructed based on the phase change, energy ramp rate and path identification characteristics of the feedback signal, and the frequency configuration, phase compensation and energy channel are dynamically adjusted.

[0014] Preferably, when the servo-type oscillation suppression structure detects a phase change or an energy ramp rate exceeding a preset threshold, it prioritizes adjusting the frequency configuration of the corresponding energy path and limits the adjusted frequency to not exceed the initial frequency band range to ensure that the stability of the control chain is not compromised.

[0015] The NFC-based wireless charging control system includes an induction spectrum modeling module, an interference identification and risk assessment module, a channel isolation and signal purification module, a closed-loop calibration and control correction module, and an oscillation suppression and steady-state control module. The induction spectrum modeling module constructs a dynamic analytical baseline of the multi-source near-field communication induction spectrum before the wireless charging device is activated. It reconstructs the energy flow trajectory using continuous time-domain and frequency-domain signals, extracts coupling paths, phase shifts, and amplitude anomaly data generated in the induction field, and generates a holographic reference data surface with tracking capabilities. The interference identification and risk assessment module, based on the holographic reference data surface, performs interference signal attribution identification processing, applies differential decoupling to separate the feedback signal and interference components, and calculates the signal consistency weight based on the continuity of the time series to generate a misjudgment matrix with risk level indicators. The channel isolation and signal purification module uses a misjudgment matrix to establish an identity-locked frequency linkage control process, generates a uniquely bound interactive signal channel, and blocks unexpected feedback paths by setting a time interval window and an energy identification threshold, thus completing the purification process of the communication signal loop. After the communication signal loop is purified, the closed-loop calibration and control correction module performs counterfactual playback calibration based on the historical energy trajectory to replay the high-risk transient state. It dynamically corrects the gain parameters, phase boundary and power ramp rate of the control loop by combining the historical energy evolution characteristics, so that the controller output characteristics converge to the stable range. The oscillation suppression and steady-state control module, based on continuous feedback of calibration results, performs adaptive topology reconstruction adjustment operations. By introducing staggered resonance paths and phase conjugate interference suppression chains, it guides energy reflection and superimposes synchronous bias signals to construct a self-healing follower-type oscillation suppression mechanism, eliminating self-excited oscillations under multi-device sensing fields and maintaining the system's stable state.

[0016] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention constructs a holographic reference data surface to accurately reconstruct the energy flow trajectory and visualize the path coupling behavior. Combined with a misjudgment matrix-driven identity-locking mechanism and loop purification operation, a unique and reliable interaction channel is established, eliminating the possibility of non-target signals entering the control chain. Simultaneously, relying on counterfactual playback and dynamic calibration, the controller optimizes its response characteristics based on historical energy evolution patterns, enhancing its adaptability to sudden situations. Furthermore, the introduction of an adaptive topology reconstruction and oscillation suppression mechanism enables the system to self-adjust with environmental changes, not only enhancing overall anti-interference performance but also improving energy transmission efficiency and control stability under multi-device collaboration, achieving intelligent, safe, and highly reliable operation of the wireless charging process. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0018] Figure 1 This is a flowchart of the NFC-based wireless charging control method of the present invention.

[0019] Figure 2 This is a schematic diagram of the NFC-based wireless charging control system of the present invention. Detailed Implementation

[0020] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0021] This invention provides, for example Figure 1 The NFC-based wireless charging control method shown includes the following steps: S100 constructs a dynamic analytical baseline of the multi-source near-field communication sensing spectrum before the wireless charging device is activated, reconstructs the energy flow trajectory using continuous time-domain and frequency-domain signals, extracts coupling paths, phase shifts, and amplitude anomaly data generated in the sensing field, and generates a holographic reference data surface with tracking capabilities. Before wireless charging control begins, to effectively avoid coupling interference between near-field communication signals and ensure the accuracy of the signal feedback path, it is necessary to perform in-depth data modeling and time-frequency feature extraction processing on the multi-source near-field communication behavior within the sensing field. This will establish a holographic reference data surface that can be used for subsequent signal identification and path attribution. The specific steps are as follows: Based on the initial startup conditions of the wireless charging device, the near-field communication signal transmission and detection component is activated. Before the wireless charging device begins energy transfer, a full-band scan of the electromagnetic response in space is continuously performed, and at least one complete cycle of response acquisition is conducted in the time domain. A fixed-frequency step-scan strategy is employed to gradually capture the response characteristics within different frequency bands, ensuring complete coverage from the lowest near-field interference band to the main operating frequency band. At each frequency point, the acquisition time window must be longer than the system response settling time to ensure that signal sampling is not affected by residual charge and background noise drift. Simultaneously, to avoid edge signal distortion, bidirectional acquisition is required during sampling; that is, both the response signal from the device transmitter to the receiver and the energy disturbance signal reflected back from the receiver must be recorded and normalized.

[0022] Based on the completed data acquisition, frequency domain transformation is performed on the data within each time-domain sampling window to map the acquired induced field response signal from the time dimension to the frequency dimension, in order to identify the relative energy distribution and dynamic change characteristics of different frequency components. During the frequency domain mapping process, the signals in the low-frequency, mid-frequency, and high-frequency regions need to be partitioned and deconstructed to analyze the differences in energy response paths at different frequencies. Based on this, the main peak delay time, peak width, and peak amplitude of the response signal in each frequency band are calculated, and a relative phase spectrum is established between different frequency bands. By comparing the slope characteristics of the phase change curves in different frequency bands, the order of magnitude of the induced sources in space and their potential coupling degree can be derived. After the frequency component analysis is completed, it needs to be cross-mapped with the initial time response data to construct a frequency-time joint response spectrum, which is used to identify the order of appearance and superposition of different coupling sources on the timeline.

[0023] Based on the aforementioned frequency-time joint spectrum, further extraction of coupling paths between multiple sensing sources is performed. Specifically, using the dominant frequency response point of each frequency band as the core, several sampling periods are extended in both positive and negative directions along the time axis to form a time-slice sequence. Then, all signal points with significant peak abrupt changes in this sequence are extracted. On this basis, by pairing analysis of the signal delay difference and energy attenuation rate between adjacent slices, each possible sensing path is traced and uniquely labeled. Simultaneously, to accurately describe the degree of mutual interference between paths, the phase offset difference between paths in the same frequency band is extracted and paired with the actual waveform overlap to obtain a complete set of path coupling feature vectors. These path coupling feature vectors can be used to characterize the degree of intersection and energy influence intensity of two or more signal sources in physical space.

[0024] After acquiring the coupling feature vectors of all sensing paths, each path is normalized and sorted according to spatial location mapping rules and energy flow direction. A multi-dimensional data surface is then constructed based on interference intensity and phase shift level. In this data surface, all energy paths are calibrated using three-dimensional spatial coordinates, phase displacement, and amplitude change values, forming a holographic reference data structure with tracking capabilities. To enable this data structure to have feedback and traceability capabilities, each data point is bound to its time sampling point, and its changing trend and direction in different time periods are appended to generate a complete time-series trajectory map. As the foundational structure for subsequent interference identification and signal attribution, the holographic reference data surface not only stores multi-dimensional information about near-field communication behavior but also provides a reference data space for dynamic decision-making, establishing a stable and reliable space-time framework for wireless charging control in complex scenarios.

[0025] S200, based on holographic reference data plane, performs interference signal attribution and identification processing, applies differential decoupling to separate feedback signal and interference components, and calculates signal consistency weight based on time series continuity to generate a misjudgment matrix with risk level indicators; After constructing the holographic reference data surface, it is necessary to use this data surface to conduct attribution and identification of interference signals. Differential decoupling is used to accurately distinguish between feedback signals and interference signals, and a consistency assessment mechanism is established by combining time series characteristics. This results in a misjudgment matrix with risk level indicators, providing clear data support for subsequent control actions. The specific steps are as follows: Based on the established holographic reference data surface, the three-dimensional spatial coordinates, phase displacement information, and amplitude variation trends of each calibration path are retrieved one by one to construct a basic signal stream sequence set for attribution identification. In this set, each path is uniquely identified and accompanied by a time sampling index. For any detected feedback signal, its temporal position is first matched with the signal segments of adjacent reference paths. The overlap in the time and frequency domains is compared using a sliding window method, and the phase matching degree and energy deviation degree are calculated. During the matching process, to ensure accuracy, all signal comparisons employ a bidirectional matching mode, considering both the alignment of the feedback signal on historical paths and simultaneously calculating its similarity to the trend of preceding signals. This bidirectional comparison method effectively identifies potential abnormal interference trajectories and establishes a preliminary distinction line between them and normal path signals.

[0026] Assuming basic path matching is completed, differential decoupling processing is performed on all signal segments to be identified. This involves extracting the residual signal from the difference between the segment and the calibrated path in the holographic reference data plane at the same time, and then separating them directionally. During the differential process, three parameters are emphasized: the instantaneous offset angle at the phase abrupt change point, the rate of increase / decrease of the energy response amplitude in the time dimension, and the spatial projection overlap between paths. Through comprehensive analysis of these three dimensions, the main response component and suspected interference components in the signal can be clearly distinguished. Specifically, the original signal waveform is segmented, and point-by-point feature extraction is performed on the abnormal peaks, jumps, and negative wave packet structures appearing in each segment, forming a set of interference feature vectors containing directionality, amplitude, and time delay. These vectors are then extracted from the original signal to form the stripped interference residual sequence. The remaining portion serves as the feedback signal segment for preliminary judgment and is retained for subsequent signal consistency weight calculation.

[0027] For signal sequences that have undergone differential decoupling, a consistency weight calculation is performed based on the continuity characteristics of the time series. The specific process is as follows: taking each feedback signal segment as an analysis unit, multiple sampling periods are extended before and after it to extract its energy change trend, phase smoothness, and response delay structure within adjacent time windows, forming multiple time-correlated signal subsequences. Based on this, the consistency score in the time dimension is quantified by cross-comparing the trend similarity, amplitude stability, and phase continuity among these subsequences. The scoring results need to be back-verified with the interference residual sequence obtained from the differential decoupling. If the consistency score of the feedback signal segment is significantly higher than that of the interference segment and maintains high similarity over multiple consecutive sampling periods, then the signal segment can be identified as a valid feedback path signal. Conversely, if the signal segment exhibits unstable characteristics such as frequent oscillations, phase jumps, and energy fluctuations in the time dimension, its consistency score will be significantly lower, and it should be marked as a delayed residual signal suspected of being an interference source.

[0028] The consistency scores and differential stripping feature vectors obtained from all feedback and interference signals during the above process are aggregated to construct a misjudgment matrix with risk level indicators. When constructing the misjudgment matrix, each sampling time window is plotted on the horizontal axis and the signal source path identifier on the vertical axis, filling in the risk value, difference value, and similarity score of the corresponding signal segment at the current moment. The risk value is determined based on the comparison between the consistency score of the feedback signal and the energy intensity of the interference segment; the difference value reflects the absolute magnitude and phase shift of the signal deviating from the reference path; and the similarity score indicates the degree of fit of the signal behavior in both spatial and temporal dimensions. The final misjudgment matrix is ​​a two-dimensional data structure that dynamically changes over time. It can be used to calibrate the credibility level of a signal at a specific time point and to provide early warning and targeted identification of potential misidentification, false triggering, or feedback interference, providing a basis for subsequent communication signal purification and identification.

[0029] The S300 uses a misjudgment matrix to establish an identity-locked frequency linkage control process, generates a uniquely bound interactive signal channel, and blocks unexpected feedback paths by setting a time interval window and an energy identification threshold, thus completing the purification process of the communication signal loop. After completing the differential decoupling and consistency assessment of the feedback and interference signals, it is necessary to further construct an identity-based frequency-locked linkage control process with accurate identification capabilities based on the multi-dimensional output results of the misjudgment matrix. This generates a uniquely bound interactive signal channel, and by setting a time interval window and energy identification threshold, unwanted feedback components in the communication path are eliminated, achieving thorough purification of the communication signal loop. The specific steps are as follows: Based on the generated misjudgment matrix, signal identifiers with high consistency scores, low risk levels, and minimal amplitude deviation in the time dimension are extracted to form a set of trustworthy paths. Each trustworthy path is then assigned an initial identity code. This identity code must contain four types of fields: first, frequency domain identification information, i.e., the frequency band and corresponding frequency point of the signal; second, spatial source location information, composed of the three-dimensional coordinate centers in the inductive spectrum; third, phase response characteristics, calculated from the phase evolution trajectory of the signal over multiple time periods; and fourth, amplitude stability index, used to measure the overall stability and response persistence of the signal waveform. Through the joint annotation of these four types of information, a uniquely identifiable identity feature set can be formed for each legitimate feedback signal. Subsequently, during the access phase of any new signal, its current behavioral characteristics must be compared field by field with this feature set. Only when all fields are within the tolerance range can interactive permissions be granted.

[0030] Based on the successful generation of a unique identity feature set, a communication channel is established for each authenticated signal source, with its frequency fully bound to the source. The frequency points used must be selected from the main response frequency band corresponding to the high-confidence path. To further enhance the security and timeliness of the linkage control, a frequency locking protocol is introduced during channel establishment. Before the interaction process begins, the frequency signal is anchored within the initial matching frequency band, and its frequency variation range is limited to prevent identity re-judgment due to external frequency offset disturbances. The frequency locking process also requires continuous monitoring of the amplitude fluctuation and phase consistency of the signal at that frequency point. If a drastic offset occurs within multiple consecutive sampling periods, it is determined that the signal has deviated from the initial identity trajectory, automatically triggering frequency locking release and closing the channel connection. Furthermore, to improve interaction stability, the identity signal channel needs to undergo a frequency domain reset operation after each interaction to ensure that the next round of communication is not interfered with by historical residual signals.

[0031] To complement the aforementioned frequency-locking linkage control mechanism, a clear time interval window needs to be set within each interaction cycle to limit the effective arrival time range of the feedback signal. This time interval window needs to be dynamically adjusted based on the delay statistics in the previous misjudgment matrix. The initial window length is set to the maximum feedback delay time plus a time safety margin to avoid falsely eliminating boundary signals. After the time window is opened, all feedback signals falling outside this interval will be identified as out-of-time feedback and will not participate in the signal linkage control process. Furthermore, an energy identification threshold strategy is needed to perform energy intensity discrimination on each signal entering the time window. The energy threshold is set with amplitude stability as a reference. If the signal peak value is below the lower threshold, it is judged as a low-confidence signal; if it is above the upper threshold, it may be a non-target high-intensity interference source. Through dual screening of time and energy, non-target feedback waveforms caused by environmental reflection, equipment resonance, or transient pulses can be further eliminated.

[0032] After frequency locking, identity matching, time filtering, and energy discrimination are all completed, a set of feedback signals that are finally allowed to enter the communication loop is formed, and the communication signal loop purification process is completed accordingly. During the purification process, all feedback signals that fail to meet identity verification or fail the time and energy gating screening are discarded and do not enter the control chain closed-loop execution process. To ensure that the purification results are effective in the control chain, the signal source must also be verified and traced in the control feedback link. That is, before each power adjustment command is issued, it is necessary to check whether the current feedback signal comes from the only authenticated interaction channel and confirm that its timestamp is within the window range and its energy amplitude is stable. If any condition is not met, the current closed-loop adjustment action is stopped, and the next reliable feedback cycle is waited for a judgment. Through the above methods, illegal signal paths and false triggering sources can be effectively separated from the communication loop, fundamentally avoiding erroneous adjustment behavior caused by signal mismatch, and laying the foundation for the stability and security of subsequent control.

[0033] After the communication signal loop is purified, the S400 performs a counterfactual playback calibration operation based on the historical energy trajectory to replay the high-risk transient state. It dynamically corrects the gain parameters, phase boundary and power ramp rate of the control loop by combining the historical energy evolution characteristics, so that the controller output characteristics converge to the stable range. After the communication signal loop undergoes identity-based frequency locking and feedback filtering, interference signals have been purified. However, further detailed calibration of the control loop's dynamic response is still required. High-risk transient behavior is reconstructed using historical energy trajectory information, allowing for targeted calibration of the controller's gain, phase, and power response characteristics to ensure long-term stable system operation. The specific steps are as follows: The system retrieves feedback signal energy evolution data saved during previous communication interactions and reconstructs the complete trajectories of each high-risk transient response in chronological order. During data retrieval, the high-risk level index in the misjudgment matrix is ​​used as the filtering criterion to extract signal response records within corresponding time periods and reconstruct their waveform characteristic curves, including multiple dimensions such as energy ramp-up rate, peak amplitude, phase shift amplitude, and response delay structure. To ensure the representativeness of the calibration reference data, each historical trajectory selection must meet three conditions: First, the trajectory must have occurred before communication cleanup, representing an unstable system phase; second, the time window containing the trajectory must exhibit behavioral characteristics such as feedback path identification interruption or frequency lock release; third, the response must cause instability such as overshoot, oscillation, or undesirable delay in closed-loop power regulation operations. Trajectory sequences meeting these conditions will serve as the core reference basis for counterfactual playback and will be replayed segment by segment before control loop calibration.

[0034] Based on the extracted historical energy trajectory sequence, it is sequentially injected into the control chain according to the original occurrence time, simulating interaction with the current system state. During this playback process, the current control loop does not execute actual energy output, only retaining the internal response stage to simulate feedback processing results. By comparing the deviations between the historical trajectory and the current controller response curve in terms of gain response, phase stability, and energy slope, structural deviations in the current control parameters when handling similar abrupt changes can be identified. For example, if the energy waveform rise rate in the historical trajectory exceeds the currently set threshold, but the current control response still does not initiate power reduction, it indicates that the power response of the control chain is lagging in this segment; if the phase shift in the historical trajectory has clearly exceeded the limit, but the current control loop does not trigger the phase reconstruction mechanism, it indicates that there is a potential problem with the phase boundary setting being too lenient. All deviation data will be quantified into correction vectors as the basis for subsequent calibration.

[0035] Based on the error correction vector obtained during the simulation interaction, the parameters of the gain adjustment unit, phase boundary judgment unit, and power output control unit in the control loop are adjusted item by item. The adjustment process must adhere to three principles: First, gain correction must prioritize ensuring the timeliness of the system response during sudden events to avoid overdrive of the power amplifier due to response lag; second, phase boundary narrowing must be based on ensuring the continuity of the control link. Excessive narrowing may cause misinterpretation of normal signals; therefore, the maximum stable offset value of the historical trajectory should be used as the compression lower limit; third, the power ramp-up rate must be set within the gradient range that causes minimal system oscillation in the historical trajectory to ensure a smooth transition in energy supply. Each parameter correction should be accompanied by boundary limits and hysteresis correction mechanisms to ensure a fallback path in case of loop mis-triggered events or excessive correction.

[0036] All revised control parameters are injected into the current closed-loop control chain, and a new round of full-process simulation verification is performed. During this process, some typical high-risk trajectories are replayed dynamically to confirm whether the adjusted control parameters have sufficient adaptive capability in the face of complex disturbances. The simulation focuses on the following three indicators: first, whether the peak settling time of the controller output is shortened when responding to sudden inputs; second, whether the closed-loop power regulation path avoids secondary oscillations or oscillation expansion; and third, whether the system energy output curve is continuous, smooth, and without abrupt jumps. If all three indicators meet the design expectations in most historical trajectory verifications, the current control chain can be determined to have converged to the stable range. Before the control parameters are solidified, multi-level threshold ranges need to be set for key control variables, and a critical value backoff mechanism needs to be established to cope with possible new feedback modes or edge disturbance behaviors in the future, ensuring that the control output maintains a long-term balance between high responsiveness and high stability.

[0037] Based on continuous feedback of calibration results, S500 performs adaptive topology reconstruction adjustment operation. By introducing staggered resonance path and phase conjugate interference suppression chain, it guides energy reflection and superimposes synchronous bias signal to construct a follower-type oscillation suppression mechanism with self-healing capability, eliminates self-excited oscillation under multi-device sensing field and maintains system stability. After completing the counterfactual playback calibration based on historical energy trajectories, to further improve the dynamic stability during wireless charging, it is necessary to introduce an adaptive structural reconfiguration adjustment method into the control loop. This will actively suppress potential self-excited oscillations under the superposition of multiple device sensing fields, ultimately constructing a self-healing follow-up steady-state maintenance mechanism. The specific steps are as follows: Based on the calibrated and optimized control loop parameters, the temporal relationships, phase delays, and frequency distribution densities of all energy transmission paths in the current induction field are re-analyzed to identify potential resonance concentration areas and coupling overlap zones. Furthermore, by calculating the spectral similarity and phase interference intensity between any two paths, signal pairs with resonance risk are extracted. During extraction, priority should be given to signal pairs with extremely small frequency intervals, phase differences within boundary ranges, and those that have historically triggered power oscillations or false feedback. Their resonance risk weights are calculated based on their transmission intensity and duration in the energy space. Subsequently, based on the time-frequency characteristics of these high-risk signal pairs, a staggered resonance path strategy is established. This involves offsetting and adjusting some paths with high frequency overlap to create an interleaved excitation pattern in the time domain, thereby preventing multiple signals from simultaneously falling into the energy peak region within a fixed time period. To implement this interleaved excitation strategy, the frequency-adjusted paths need to be bound to their historical response time periods, and a dynamic delay window needs to be set to control their activation rhythm, preventing them from overlapping with other strong signals in the same response cycle.

[0038] Based on the established staggered resonance path, a phase conjugate interference suppression chain is introduced as the backbone structure to eliminate the resonance amplification path. This suppression chain consists of a set of phase-complementary signal nodes. Its core principle is to inject a compensation signal with the same frequency but opposite phase as the high-interference path into the induction field to form an interference cancellation effect during spatial superposition. To construct a stable and effective suppression chain, it is necessary to first screen representative phase anomalies from previous counterfactual playback, extract their response behavior in different energy ranges, and then calculate the phase compensation factor based on the control parameters set in the current loop. This compensation factor will be inserted into the energy flow path to form a set of relatively fixed phase-exclusive segments, covering the energy peak areas where self-excitation frequently occurs. In each compensation path, a time synchronization tag and energy amplitude adjustment factor also need to be set to ensure that the compensation signal enters the induction space at the correct time and completes interference coverage with appropriate intensity, preventing new phase disturbances caused by the compensation signal itself.

[0039] After the phase conjugate interference suppression chain is constructed, an energy return mechanism needs to be introduced simultaneously, along with a synchronous bias signal, to further improve the energy balance and dynamic response stability of the induced field. The energy return mechanism guides energy that might otherwise concentrate at the center due to phase amplification to diffuse towards the edge scattering region, thereby reducing the probability of oscillations induced in high-energy-density areas. Specifically, a switchable bypass channel is set up on the original energy output path. When the energy amplitude on the main path exceeds the dynamic threshold, a portion of the energy is automatically guided to the secondary path to form a circular return flow, reducing the instantaneous load on the main path and creating an energy attenuation buffer zone in the outer region. Simultaneously, a synchronous bias signal is inserted into the energy transmission path. Its frequency maintains a slight difference from the main signal, and its phase remains within an adjustable range. Injected during peak periods of the path, it disperses harmonic overlap caused by concentrated transmission, thereby reducing the spatial superposition effect. The insertion point of the synchronous bias signal needs to be precisely positioned according to the phase boundary settings in the control chain to ensure that it achieves uniform expansion without disrupting effective feedback.

[0040] Based on the superposition of multiple mechanisms including staggered resonant paths, phase conjugate suppression chains, energy return mechanisms, and synchronous bias signals, a self-healing follower-type oscillation suppression structure is constructed. The core features of this structure are dynamic adaptation and local repair capabilities. Specifically, if any unstable signs such as phase abrupt changes, abnormal energy ramp-up rates, or discontinuous path identification are detected in the feedback signal at any given time period, the corresponding structural reconstruction operation is immediately triggered, reconfiguring the frequency of the relevant energy paths, adjusting phase compensation, and redistributing energy channels. The activation rule of the follower-type structure follows the dynamic stability interval judgment criteria established in the preceding calibration steps, ensuring that each structural response originates from a valid risk signal and automatically reverts to the normal structural configuration after the risk is eliminated, avoiding resource redundancy and over-control. Through the continuous operation of this structure, the system can maintain the stability of energy flow and the linearity of control response in complex operating environments such as multi-device interference, inductive superposition, and external excitation disturbances, fundamentally suppressing the generation and expansion of self-excited oscillations and ensuring overall stability and safe operation during wireless charging.

[0041] This invention constructs a holographic reference data surface to accurately reconstruct the energy flow trajectory and visualize the path coupling behavior. Combined with a misjudgment matrix-driven identity-locking mechanism and loop purification operation, a unique and reliable interaction channel is established, eliminating the possibility of non-target signals entering the control chain. Simultaneously, relying on counterfactual playback and dynamic calibration, the controller optimizes its response characteristics based on historical energy evolution patterns, enhancing its adaptability to sudden situations. Furthermore, the introduction of an adaptive topology reconstruction and oscillation suppression mechanism enables the system to self-adjust with environmental changes, not only enhancing overall anti-interference performance but also improving energy transmission efficiency and control stability under multi-device collaboration, achieving intelligent, safe, and highly reliable operation of the wireless charging process.

[0042] This invention provides, for example Figure 2 The NFC-based wireless charging control system shown includes an induction spectrum modeling module, an interference identification and risk assessment module, a channel isolation and signal purification module, a closed-loop calibration and control correction module, and an oscillation suppression and steady-state control module. The induction spectrum modeling module constructs a dynamic analytical baseline of the multi-source near-field communication induction spectrum before the wireless charging device is activated. It reconstructs the energy flow trajectory using continuous time-domain and frequency-domain signals, extracts coupling paths, phase shifts, and amplitude anomaly data generated in the induction field, and generates a holographic reference data surface with tracking capabilities. The interference identification and risk assessment module, based on the holographic reference data surface, performs interference signal attribution identification processing, applies differential decoupling to separate the feedback signal and interference components, and calculates the signal consistency weight based on the continuity of the time series to generate a misjudgment matrix with risk level indicators. The channel isolation and signal purification module uses a misjudgment matrix to establish an identity-locked frequency linkage control process, generates a uniquely bound interactive signal channel, and blocks unexpected feedback paths by setting a time interval window and an energy identification threshold, thus completing the purification process of the communication signal loop. After the communication signal loop is purified, the closed-loop calibration and control correction module performs counterfactual playback calibration based on the historical energy trajectory to replay the high-risk transient state. It dynamically corrects the gain parameters, phase boundary and power ramp rate of the control loop by combining the historical energy evolution characteristics, so that the controller output characteristics converge to the stable range. The oscillation suppression and steady-state control module, based on continuous feedback of calibration results, performs adaptive topology reconstruction adjustment operations. By introducing staggered resonance paths and phase conjugate interference suppression chains, it guides energy reflection and superimposes synchronous bias signals to construct a self-healing follower-type oscillation suppression mechanism, eliminating self-excited oscillations under multi-device sensing fields and maintaining the system's stable state.

[0043] The NFC-based wireless charging control method provided in this embodiment of the invention is implemented through the aforementioned NFC-based wireless charging control system. For details of the specific methods and processes of the NFC-based wireless charging control system, please refer to the embodiments of the aforementioned NFC-based wireless charging control method, which will not be repeated here.

[0044] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A wireless charging control method based on NFC, characterized in that, Includes the following steps: S100 constructs a dynamic analytical baseline of the multi-source near-field communication sensing spectrum before the wireless charging device is activated, reconstructs the energy flow trajectory using continuous time-domain and frequency-domain signals, extracts coupling paths, phase shifts, and amplitude anomaly data generated in the sensing field, and generates a holographic reference data surface. S200, based on the holographic reference data plane, performs interference signal attribution and identification processing, applies differential decoupling to separate the feedback signal and interference components, and calculates the signal consistency weight based on the continuity of the time series to generate a misjudgment matrix; The S300 uses a misjudgment matrix to establish an identity-locked frequency linkage control process, generates a uniquely bound interactive signal channel, and blocks unexpected feedback paths by setting a time interval window and an energy identification threshold, thus completing the purification process of the communication signal loop. After the communication signal loop is purified, the S400 performs a counterfactual playback calibration operation based on the historical energy trajectory to replay the high-risk transient state. It dynamically corrects the gain parameters, phase boundary and power ramp rate of the control loop by combining the historical energy evolution characteristics, so that the controller output characteristics converge to the stable range. Based on continuous feedback of calibration results, the S500 performs adaptive topology reconstruction adjustment. By introducing staggered resonance paths and phase conjugate interference suppression chains, it guides energy reflection and superimposes synchronous bias signals to construct a follower-type oscillation suppression mechanism, thereby eliminating self-excited oscillations under multi-device induction fields and maintaining the system's stable state.

2. The NFC-based wireless charging control method according to claim 1, characterized in that, Step S100 includes: Before the wireless charging device is activated, the near-field communication signal transmission and detection component is activated to perform a full-band scan of the electromagnetic response in space, collect continuous time-domain signals and frequency-domain signals and perform normalization processing. After data acquisition is completed, frequency domain transformation is performed on the signal within each time domain sampling window to construct a joint frequency and time response spectrum, and energy distribution characteristics and phase spectrum features are extracted to determine the order of magnitude and coupling degree of the sensing source. Based on the joint frequency and time spectrum, the time sampling period is extended to extract peak abrupt signal points, track the sensing path and calculate the phase offset difference and waveform overlap to generate a path coupling feature vector. After obtaining the path coupling feature vector, the data is normalized and sorted according to the spatial location mapping relationship and energy flow direction. A holographic reference data surface is constructed with three-dimensional spatial coordinates, phase displacement and amplitude change values ​​as elements, and a time-series trajectory map is established by combining time sampling points.

3. The NFC-based wireless charging control method according to claim 1, characterized in that, Step S200 includes: Based on the holographic reference data surface, the spatial coordinates, phase displacement and amplitude change information of the calibration path are extracted to construct a set of basic signal flow sequences. The temporal position of any feedback signal is matched with the reference path for bidirectional comparison to identify abnormal interference trajectories. After matching is completed, the difference residual signal between the reference path signal and the current feedback signal is extracted. The phase offset angle, energy change rate and spatial projection overlap are analyzed to separate the interference residual signal and form the interference feature vector. For the feedback signal segment after differential decoupling, multiple sampling periods are extended based on the continuity of the time series. The similarity scores of energy trend, phase smoothness and delay structure are calculated to determine their consistency in the time dimension. The scoring results and feature vectors of the feedback signal and the interference signal are summarized to construct a two-dimensional misjudgment matrix with the sampling time window as the horizontal axis and the signal path identifier as the vertical axis, and the risk value, difference value and similarity score of each signal segment are output.

4. The NFC-based wireless charging control method according to claim 3, characterized in that, The interference feature vector extracted during the differential decoupling process includes directional parameters, amplitude variation parameters, and time delay parameters. When the consistency score of the feedback signal is higher than that of the interference residual signal and remains stable within the continuous sampling period, the feedback signal is confirmed as a valid feedback signal.

5. The NFC-based wireless charging control method according to claim 1, characterized in that, Step S300 includes: Based on the misjudgment matrix, signal identifiers with high consistency scores, low risk levels and minimum amplitude deviations are extracted, a set of trustworthy paths is selected, and an identity code is assigned to each trustworthy path. An interactive signal channel that is fully bound to the frequency is established based on the identity code, and a frequency locking protocol is introduced during the communication process to limit the range of the signal frequency and monitor its amplitude fluctuation and phase consistency in real time. During the interaction, a time interval window is set, and combined with the energy identification threshold strategy, the feedback signal is filtered by both time and energy to remove signals that are not within the effective time window or have abnormal energy. After completing frequency locking, identity matching, time filtering, and energy discrimination, a final set of feedback signals is formed, and the signal source and parameters are verified before control feedback.

6. The NFC-based wireless charging control method according to claim 1, characterized in that, Step S400 includes: The system calls upon the energy evolution data of the feedback signal saved during the communication interaction, filters out historical high-risk transient trajectories based on the high-risk level indicators in the misjudgment matrix, and extracts the energy rise rate, peak amplitude, phase shift amplitude, and response delay structure information. Based on the extracted historical energy trajectory, it is injected into the control chain in chronological order for simulation interaction. The deviation of the historical trajectory from the current control response in terms of gain, phase and energy slope is compared to generate an error correction vector. Based on the error correction vector, the gain adjustment unit, phase boundary judgment unit and power output control unit in the control chain are adjusted one by one, and the parameter correction range and boundary limit are set to prevent over-correction from causing false triggering. The modified control parameters are injected into the closed-loop control chain for simulation verification. The adaptive capability of the control parameters under complex disturbances is confirmed by dynamic reenactment of typical high-risk trajectories. Multi-level thresholds and backoff mechanisms are established before solidification.

7. The NFC-based wireless charging control method according to claim 6, characterized in that, The gain adjustment value in the error correction vector is set based on the maximum rate of change of energy ramp rate in the historical trajectory, the phase boundary compression value is set based on the upper limit of the stable offset interval, and the power ramp rate is limited based on the minimum oscillation gradient interval.

8. The NFC-based wireless charging control method according to claim 1, characterized in that, Step S500 includes: Based on the calibrated control parameters, the timing relationship, phase delay and frequency distribution of each energy path in the induction field are analyzed, signal pairs with resonance risk are identified, staggered resonance paths are established and dynamic time delay windows are set to avoid energy peak overlap. Based on the staggered resonant path, a compensation signal with the same frequency but opposite phase as the high interference path is introduced to construct a phase conjugate interference suppression chain. By setting a time synchronization tag and an energy amplitude adjustment factor, interference coverage is achieved. A synchronous energy return mechanism is introduced, and a switchable bypass channel is set on the energy output path to guide energy exceeding the threshold to the secondary path to form a return flow. A synchronous bias signal with a slight frequency difference is inserted during the peak period of the path to disperse harmonic overlap. After the above operations are completed, a follow-up oscillation suppression structure is constructed. The structure is reconstructed based on the phase change, energy ramp rate and path identification characteristics of the feedback signal, and the frequency configuration, phase compensation and energy channel are dynamically adjusted.

9. The NFC-based wireless charging control method according to claim 8, characterized in that, When the servo-type oscillation suppression structure detects a phase change or an energy ramp rate exceeding a preset threshold, it prioritizes adjusting the frequency configuration of the corresponding energy path and limits the adjusted frequency to not exceed the initial frequency band range to ensure that the stability of the control chain is not compromised.

10. An NFC-based wireless charging control system, used to implement the NFC-based wireless charging control method according to any one of claims 1-9, characterized in that, It includes modules for inductive spectrum modeling, interference identification and risk assessment, channel isolation and signal purification, closed-loop calibration and control correction, and oscillation suppression and steady-state control. The induction spectrum modeling module constructs a dynamic analytical baseline of the multi-source near-field communication induction spectrum before the wireless charging device is activated. It reconstructs the energy flow trajectory using continuous time-domain and frequency-domain signals, extracts coupling paths, phase shifts, and amplitude anomaly data generated in the induction field, and generates a holographic reference data surface. The interference identification and risk assessment module, based on the holographic reference data surface, performs interference signal attribution identification processing, applies differential decoupling to separate the feedback signal and interference components, and calculates the signal consistency weight based on the continuity of the time series to generate a misjudgment matrix; The channel isolation and signal purification module uses a misjudgment matrix to establish an identity-locked frequency linkage control process, generates a uniquely bound interactive signal channel, and blocks unexpected feedback paths by setting a time interval window and an energy identification threshold, thus completing the purification process of the communication signal loop. After the communication signal loop is purified, the closed-loop calibration and control correction module performs counterfactual playback calibration based on the historical energy trajectory to replay the high-risk transient state. It dynamically corrects the gain parameters, phase boundary and power ramp rate of the control loop by combining the historical energy evolution characteristics, so that the controller output characteristics converge to the stable range. The oscillation suppression and steady-state control module performs adaptive topology reconstruction adjustment based on continuous feedback of calibration results. By introducing staggered resonance paths and phase conjugate interference suppression chains, it guides energy reflection and superimposes synchronous bias signals to construct a follower-type oscillation suppression mechanism, eliminating self-excited oscillations under multi-device induction fields and maintaining the system's stable state.

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