Nfc trigger control method fusing wireless charging and automatic data backup

Abstract

The application discloses an NFC trigger control method fusing wireless charging and automatic data backup, and relates to the field of data backup.The method comprises the following steps: realizing device identity authentication and power information acquisition through an NFC trigger mechanism, dynamically calculating a target charging power based on current power, and monitoring device temperature in real time during wireless charging.By calculating the difference between the upper limit temperature of the storage chip and the real-time temperature, the real-time reading frequency parameter is intelligently generated by using the pre-trained reading frequency determinator to dynamically control the working state of the storage chip in the data backup process.Further, a cloud collaborative optimization mechanism is introduced, the confidence weight of each determination unit is dynamically adjusted according to historical accuracy data, and adaptive optimization of the reading frequency determinator is realized.The application effectively solves the problem of device overheating when wireless charging and data backup are concurrently executed, realizes the balance of charging efficiency, backup safety and device thermal management, and significantly improves the user experience.

Description

Technical Field

[0001] This invention relates to the field of data backup, and more specifically to an NFC trigger control method that integrates wireless charging and automatic data backup. Background Technology

[0002] In applications where mobile devices simultaneously perform wireless charging and data backup, traditional temperature protection mechanisms have significant shortcomings. When the device is placed on a charging dock, both the wireless charging coil and the storage chip act as heat sources, causing the device temperature to rise rapidly.

[0003] Existing protection schemes typically employ a critical point response mechanism, triggering protection only when the storage chip temperature reaches its upper operating threshold. This passive data backup method has several drawbacks: first, it has a delayed response and cannot prevent high-temperature risks; second, it uses a crude intervention method of directly stopping backups or drastically reducing the frequency, severely disrupting the continuity of data backups; and third, frequent start-ups and shutdowns near the critical temperature cause drastic fluctuations in the operating state of the storage chip, affecting both backup efficiency and user experience. Summary of the Invention

[0004] This invention addresses the technical problem in the prior art where the data backup process of mobile devices simultaneously performs wireless charging and data backup suffers from lag and low efficiency due to temperature control, by providing an NFC trigger control method that integrates wireless charging and automatic data backup.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0006] In a first aspect, the present invention provides an NFC trigger control method integrating wireless charging and automatic data backup, comprising:

[0007] In response to a contact signal between the mobile device and the NFC recognition area, the current battery information of the mobile device is obtained based on the contact signal;

[0008] Based on the current battery level information, determine the target charging power for the mobile device;

[0009] The mobile device is wirelessly charged according to the target charging power, and the real-time device temperature of the mobile device is acquired in real time during the charging process.

[0010] Obtain the upper limit operating temperature of the storage chip of the mobile device, and calculate the real-time temperature difference based on the upper limit operating temperature and the real-time device temperature;

[0011] The real-time read parameters of the memory chip are determined based on the real-time temperature difference, and the data backup control of the memory chip is performed based on the real-time read parameters.

[0012] The beneficial effects of this invention are:

[0013] Compared to existing technologies, this invention firstly achieves precise coordination between device authentication and charging control through an NFC triggering mechanism, avoiding the risk of misoperation by unauthorized devices. Secondly, a predictive control strategy based on real-time temperature differences proactively adjusts the read / write frequency before the storage chip temperature approaches its upper limit, effectively preventing safety hazards caused by sudden temperature rises. Thirdly, a gradual parameter adjustment method avoids backup interruptions caused by frequent start-stop operations in traditional solutions, significantly improving the continuity and efficiency of data backup. Finally, by dynamically optimizing the coordination between charging power and backup strategies, the invention maximizes the use of the charging time window, achieving an overall improvement in charging and backup efficiency while ensuring device safety. Attached Figure Description

[0014] Figure 1 A flowchart illustrating the NFC trigger control method integrating wireless charging and automatic data backup provided by this invention;

[0015] Figure 2 A schematic diagram of the NFC trigger control method integrating wireless charging and automatic data backup provided by the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0018] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0019] Example 1, as Figure 1 , Figure 2 As shown, this embodiment of the invention provides an NFC trigger control method that integrates wireless charging and automatic data backup, including:

[0020] S10: Responding to the contact signal between the mobile device and the NFC identification area, obtain the current battery information of the mobile device based on the contact signal;

[0021] Specifically, in response to a contact signal between the mobile device and the NFC recognition area, the current battery information of the mobile device is obtained based on the contact signal, including:

[0022] When the mobile device comes into contact with the NFC recognition area, the device identification information of the mobile device is obtained;

[0023] Based on the device identification information, query the pre-stored device binding relationship and verify the authorization status of the mobile device;

[0024] When the verification is successful, the current battery information of the mobile device is obtained.

[0025] Mobile devices refer to smart terminal devices with wireless charging capabilities and near-field communication (NFC) support. The NFC identification area is a near-field communication sensing area integrated within a specific range on the surface of the charging device. First, when the mobile device physically contacts the NFC identification area, it actively acquires the device's identification information via the NFC protocol. This identification information includes the device model, serial number, or a specific authentication identifier, which uniquely identifies the device. Acquiring device identification information via NFC offers the advantage of contactless identification, requiring no additional user intervention.

[0026] Secondly, based on the acquired device identification information, a pre-established device binding relationship database is queried to verify the authorization status of the mobile device, ensuring that only authorized devices can initiate subsequent charging and data backup processes. Device binding relationships are established through a secure pairing process upon first use by the user and include the association information between the device identification and the user account. Specifically, the verification process checks whether the device has completed registration, is in a valid state, and has permission to use the current service. Checking the authorization status prevents unauthorized devices from abusing charging resources, ensuring operational safety.

[0027] Finally, once the device authorization verification is successful, the current battery information of the mobile device is obtained through the established communication connection. This battery information includes parameters such as the current remaining battery power, battery health status, and charging history. This information can not only be used to determine the initial charging power but also provide a reference for subsequent temperature prediction and backup strategy development.

[0028] This authorized method of obtaining battery information ensures both the legality of data acquisition and the protection of user privacy.

[0029] S20: Determine the target charging power for the mobile device based on the current battery level information;

[0030] Specifically, based on the current battery level information, determining the target charging power for the mobile device includes:

[0031] Obtain the battery capacity information and maximum charging power specifications of the mobile device;

[0032] Based on the current power level information and the battery capacity information, determine the estimated charging time required to charge to the preset target power level;

[0033] Based on the estimated charging time, a target charging power is determined to complete charging within the estimated charging time, wherein the target charging power does not exceed the maximum charging power specification of the mobile device.

[0034] After obtaining the device's current battery level information, the target charging power of the mobile device can be further determined based on the current battery level information.

[0035] First, obtain the mobile device's battery capacity and maximum charging power specifications. Specifically, battery capacity refers to the battery's total energy storage capacity, usually measured in milliampere-hours (mAh); maximum charging power specifications refer to the highest charging power value supported by the device hardware, a value set by the device manufacturer. Then, based on the current battery level and capacity information, calculate the estimated charging time required to reach a preset target battery level. The preset target battery level is typically set to full battery capacity, but can also be set to a specific percentage value according to user needs. The calculation process needs to consider the non-linear characteristics of the battery charging curve, employing an algorithm model based on historical charging data for time estimation to ensure the accuracy of the results. Estimated charging time = (Battery capacity - Current battery level) / (Average charging efficiency × Average charging power). The average charging efficiency is dynamically adjusted based on battery type and charging state, while the average charging power is determined based on the current battery level and historical charging characteristics.

[0036] For example, suppose the battery capacity is 4000 mAh, the current charge is 20%, the preset target charge is 100%, the average charging efficiency is 85%, and the average charging power is 15 watts. Then the estimated charging time = (4000 × (1 - 0.2)) / (0.85 × 15) = 2.5 hours.

[0037] Finally, based on the estimated charging time, the target charging power required to complete charging within that time period is determined. The calculation of the target charging power needs to comprehensively consider multiple factors such as the current battery state, charging efficiency, and ambient temperature, while strictly ensuring that it does not exceed the maximum charging power specification of the mobile device. This constraint effectively ensures the safety of the charging process, avoiding equipment damage or safety hazards caused by exceeding power limits. Specifically, the target charging power = [(battery capacity - current battery level) / estimated charging time] / overall charging efficiency. The overall charging efficiency takes into account efficiency losses during energy conversion and the impact of environmental factors such as temperature.

[0038] For example, suppose the battery capacity is 4000 mAh, the current charge is 20%, and the required charge is 3200 mAh. The estimated charging time is 2.5 hours, and the overall charging efficiency is 85%. Then the target charging power = (3200 ÷ 2.5) ÷ 0.85 = 1500 mW. This calculation result needs to be compared with the device's maximum charging power specification. If the calculated value exceeds the maximum allowable power, then the maximum allowable power will be used as the actual target charging power.

[0039] In summary, this calculation method scientifically determines the optimal charging power while ensuring charging safety. This guarantees charging within the predetermined time while avoiding equipment risks caused by excessive power. Furthermore, by comprehensively considering charging efficiency and environmental factors, the power calculation is more closely aligned with real-world usage scenarios, effectively improving the reliability and overall efficiency of the charging process.

[0040] S30: Wirelessly charge the mobile device according to the target charging power, and obtain the real-time device temperature of the mobile device during the charging process;

[0041] Specifically, the wireless charging process is based on pre-determined target charging power parameters. Simultaneously, temperature data is collected in real time by thermal sensors distributed throughout the mobile device. Monitoring points are preferentially placed in key heat-generating areas such as the memory chip and battery regions. Temperature data is continuously acquired at a fixed sampling frequency, forming a continuous temperature change curve. The temperature data at each sampling point is timestamped for subsequent analysis of temperature trends over time.

[0042] S40: Obtain the upper limit operating temperature of the storage chip of the mobile device, and calculate the real-time temperature difference based on the upper limit operating temperature and the real-time device temperature;

[0043] The upper operating temperature limit refers to the highest ambient temperature threshold at which a chip can operate normally. Exceeding this temperature may lead to data errors or hardware damage. This upper operating temperature limit is usually provided by the chip manufacturer and is stated in the device's technical specifications document.

[0044] After obtaining the upper operating temperature limit, calculate the real-time temperature difference between it and the real-time equipment temperature. Real-time temperature difference = Upper operating temperature limit - Real-time equipment temperature. This real-time temperature difference reflects the distance between the current temperature state and the safety boundary; a positive value indicates a safety margin, while a negative value indicates that the safety range has been exceeded.

[0045] S50: Determine the real-time read parameters of the memory chip based on the real-time temperature difference, and perform data backup control on the memory chip based on the real-time read parameters.

[0046] Specifically, determining the real-time read parameters of the memory chip based on the real-time temperature difference includes:

[0047] Retrieve the read frequency determiner of the memory chip;

[0048] The real-time temperature difference is processed by the read frequency determiner to obtain the real-time read frequency of the memory chip, which is used as the real-time read parameter of the memory chip.

[0049] The real-time read parameter of a memory chip is a dynamically adjusted read frequency value used to precisely control the data transfer rate of the memory chip during data backup. This real-time read parameter is calculated based on the real-time temperature difference, and based on this parameter, data backup control of the memory chip can be performed to achieve an optimal balance between temperature and performance.

[0050] First, the read frequency determiner of the memory chip is retrieved. The read frequency determiner is a pre-trained intelligent decision-making model, trained by the correspondence between historical temperature data and the optimal read frequency. It is constructed using machine learning algorithms and can establish a non-linear mapping relationship between temperature difference and the optimal read frequency.

[0051] Specifically, the construction steps of the read frequency determiner for the memory chip include:

[0052] Obtain read records of memory chips of the same model, construct a sample temperature difference set based on the read records, and label the read frequency of each sample temperature difference in the sample temperature difference set to obtain a sample read frequency set.

[0053] Construct a multi-frequency-determined unit architecture;

[0054] The multiple frequency determination unit architectures are trained using the sample temperature difference set and the sample read frequency set, respectively, to obtain multiple read frequency determination units and multiple default unit confidence levels.

[0055] The multiple read frequency determination units are integrated based on the confidence levels of the multiple default units to obtain the read frequency determiner.

[0056] First, historical read records of the same model of memory chip need to be obtained, and a sample temperature difference set is constructed based on these records. The historical read records originate from actual operating data of multiple devices of the same model, including temperature monitoring records and the corresponding memory chip operating status. Temperature difference data is extracted from the read records to form a sample set, with each sample containing the temperature difference value at a specific time. Second, the read frequency of each sample temperature difference is labeled. The labeled value is the optimal read frequency value proven in practice under that temperature difference condition, thus forming a sample read frequency set. The labeling process combines expert evaluation and data analysis to ensure the accuracy and reliability of the labeled values.

[0057] Furthermore, multiple frequency determination unit architectures are constructed. Specifically, different algorithm models are employed, such as linear regression models, decision tree models, and neural network models. Each frequency determination unit architecture possesses unique computational characteristics and applicable scenarios, capable of learning the mapping relationship between temperature differences and reading frequencies from different perspectives. The architecture design considers a balance between computational complexity and prediction accuracy, ensuring that each unit has real-time processing capabilities.

[0058] For example, since there is a complex nonlinear mapping relationship between the temperature difference and the optimal reading frequency, and neural networks have powerful function approximation capabilities and pattern recognition advantages, a neural network model is chosen as the core architecture of the frequency determination unit.

[0059] Specifically, this neural network model mainly consists of an input layer, hidden layers, and an output layer. The input layer receives real-time temperature difference data and first performs standardization to eliminate data bias. The hidden layer adopts a three-layer fully connected network structure, with the number of neurons in each layer dynamically configured according to the numerical range of the temperature difference and the accuracy requirements of the frequency output. The hidden layer uses the ReLU activation function to introduce non-linear transformation capability and embeds Dropout regularization layers between layers, with the dropout rate set between 0.2 and 0.4 to effectively suppress overfitting and improve the model's generalization performance. The output layer uses a linear activation function to map the final features to continuous reading frequency values.

[0060] During training, key hyperparameters included a learning rate of 0.0005, 150 training epochs, and a batch size of 64. The learning rate ensured the stability of the gradient descent process, the number of training epochs guaranteed the model fully learned the mapping relationship between temperature differences and reading frequencies, and the batch size balanced training efficiency with memory usage. Specifically, a supervised learning approach was adopted, collecting sample temperature differences from historical data to form the input sample set, and simultaneously acquiring the optimal reading frequency values ​​to form the label set. The sample set was divided into training, validation, and test sets in an 8:1:1 ratio.

[0061] Furthermore, the temperature differences of samples in the training set are used as input, with the corresponding optimal reading frequency as the supervision signal. The network weight parameters are iteratively optimized using the backpropagation algorithm and the Adam optimizer. The mean squared error loss function is used to measure the deviation between the predicted frequency value and the actual optimal frequency value, and the training process is monitored using a validation set. When the validation set loss value no longer decreases for several consecutive rounds and reaches a predetermined accuracy requirement, such as 90%, training is terminated, and a converged frequency-determined unit is obtained. After training, the prediction accuracy of this unit on the test set is calculated as the default unit confidence score. This default unit confidence score reflects the prediction accuracy and generalization ability of the unit on the training dataset.

[0062] Furthermore, multiple frequency determination unit architectures are constructed using different model architectures. These architectures are trained using sample temperature difference sets and sample readout frequency sets, respectively, to obtain multiple readout frequency determination units and multiple default unit confidence scores. Then, the readout frequency determination units are integrated based on the confidence scores of the multiple default units. Specifically, a weighted fusion strategy is used in the integration process, giving units with higher confidence scores greater weight in the final decision. Through ensemble learning techniques, the prediction results of each unit are organically combined to form the final readout frequency determiner.

[0063] The readout frequency determiner obtained through the above steps can intelligently convert temperature differences into optimal readout frequencies, providing core technical support for predictive temperature management. Furthermore, this readout frequency determiner features strong adaptability, high prediction accuracy, and good computational efficiency, making it suitable for real-time operation in mobile device environments.

[0064] Further, the real-time temperature difference is processed by the read frequency determiner to obtain the real-time read frequency of the memory chip, which is used as the real-time read parameter of the memory chip, including:

[0065] The real-time temperature difference value is input into the multiple reading frequency determination units respectively to obtain the multiple unit output frequencies;

[0066] Obtain the default cell confidence level for each read frequency determination cell;

[0067] Based on the output frequencies of the multiple units and the corresponding default unit confidence levels, the real-time read frequency of the memory chip is calculated by weighted averaging and used as the real-time read parameter of the memory chip.

[0068] First, the real-time temperature difference is simultaneously input into multiple readout frequency determination units. Each unit independently calculates based on its internal algorithm model and outputs its corresponding unit output frequency. Multiple determination units employ parallel processing to ensure efficiency and real-time performance. Second, the default unit confidence score for each readout frequency determination unit is obtained. This default unit confidence score is derived from the performance evaluation results during the training phase, reflecting the prediction accuracy and reliability of each determination unit on historical data.

[0069] Furthermore, based on the output frequencies of multiple cells and their corresponding default cell confidence levels, the final real-time read frequency is calculated through a weighted average. Real-time read frequency = [∑(cell output frequency × cell confidence level)] / (∑cell confidence level). This real-time read frequency serves as a real-time read parameter for the storage chip, directly controlling its operating state during data backup. By employing a weighted average calculation method, the advantages of ensemble learning are fully utilized, allowing cells with higher confidence levels to have a greater influence on the final result, while reducing interference from lower-performing cells, ensuring the accuracy and reliability of the output results.

[0070] Since the real-time reading frequency obtained by the aforementioned calculation depends on the default unit confidence of each determinant, and the default unit confidence is generated based on the static data during the training phase, in actual applications, the actual performance of determinant units may change due to the different usage environments and working states of different devices. Therefore, it is necessary to obtain big data statistics of the same model of devices through cloud connection and use swarm intelligence to optimize the confidence weight of each model.

[0071] Therefore, the method further includes:

[0072] Establish a data connection with the cloud server to obtain historical accuracy data of each reading frequency determination unit of the same model of mobile device under the same real-time temperature difference conditions;

[0073] Based on the historical accuracy data, the historical accuracy of each reading frequency determination unit under the same real-time temperature difference condition is calculated.

[0074] Based on the historical accuracy and the preset benchmark accuracy, calculate the confidence correction factor corresponding to each reading frequency determination unit;

[0075] The default unit confidence of each read frequency determination unit is adjusted using the confidence correction factor of each read frequency determination unit to obtain multiple dynamic unit confidences;

[0076] The read frequency determiner is reconfigured based on the confidence levels of the multiple dynamic units.

[0077] First, a secure data connection is established with the cloud server to obtain historical accuracy data for each frequency determination unit of the same model of mobile device under the same real-time temperature difference conditions. The cloud server aggregates operational data from multiple devices of the same model, including records of the deviation between the actual output frequency and the optimal frequency of each determination unit under different temperature difference conditions. Data transmission employs an encryption protocol to ensure data security and integrity.

[0078] Furthermore, based on the historical accuracy data, the historical accuracy of each reading frequency determination unit under the same real-time temperature difference condition is calculated, including:

[0079] A first deviation threshold and a second deviation threshold are set, wherein the first deviation threshold is less than the second deviation threshold;

[0080] Based on the first deviation threshold and the second deviation threshold, an accurate determination rule is set, wherein the accurate determination rule is:

[0081] When the historical output frequency of the frequency determination unit is greater than the actual optimal frequency, and the deviation between the historical output frequency and the actual optimal frequency is greater than or equal to the first deviation threshold, it is determined to be inaccurate.

[0082] When the historical output frequency of the frequency determination unit is less than the actual optimal frequency, and the deviation between the historical output frequency and the actual optimal frequency is greater than or equal to the second deviation threshold, it is determined to be inaccurate.

[0083] When the historical output frequency of the frequency determination unit is equal to the actual optimal frequency, or when the historical output frequency of the frequency determination unit is greater than the actual optimal frequency and the deviation between the historical output frequency and the actual optimal frequency is less than the first deviation threshold, or when the historical output frequency of the frequency determination unit is less than the actual optimal frequency and the deviation between the historical output frequency and the actual optimal frequency is less than the second deviation threshold, it is determined to be accurate.

[0084] Based on the accuracy determination rules and the historical accuracy data of each reading frequency determination unit, the historical accuracy of each reading frequency determination unit is calculated.

[0085] Specifically, the process of calculating the historical accuracy of each read frequency determination unit based on historical accuracy data employs a refined judgment strategy. First, two deviation thresholds are set: a first deviation threshold and a second deviation threshold, where the first deviation threshold is smaller than the second deviation threshold. The threshold settings are based on the performance characteristics and safety requirements of the memory chip; the first deviation threshold typically addresses excessively high frequencies, while the second deviation threshold addresses excessively low frequencies. This asymmetric error assessment strategy and asymmetric threshold design reflect a safety-oriented design philosophy, reflecting the different degrees of impact of excessively high and low frequencies on device safety.

[0086] Accurate judgment rules are set based on dual thresholds. For situations where excessively high output frequencies may lead to overheating, a strict first deviation threshold is used for judgment; while for situations where excessively low output frequencies only affect efficiency, a more lenient second deviation threshold is used. Specifically, when the historical output frequency of the read frequency determination unit is greater than the actual optimal frequency, and the deviation value is greater than or equal to the first deviation threshold, the output is judged as inaccurate. In this case, an excessively high read frequency may cause a sharp increase in heat generation, exceeding the first deviation threshold is considered a safety hazard. When the historical output frequency of the read frequency determination unit is less than the actual optimal frequency, and the deviation value is greater than or equal to the second deviation threshold, it is also judged as inaccurate. In this case, although an excessively low read frequency does not affect safety, it will significantly reduce backup efficiency, exceeding the second deviation threshold is considered substandard performance.

[0087] The conditions for accurate determination include three situations: when the historical output frequency is equal to the actual optimal frequency, it is directly determined to be accurate; when the output frequency is greater than the optimal frequency but the deviation is less than the first deviation threshold, it is determined to be accurate. In this case, a slight deviation will not affect safety and may even improve efficiency; when the output frequency is less than the optimal frequency but the deviation is less than the second deviation threshold, it is determined to be accurate. In this case, a slight deviation is within the acceptable range.

[0088] Based on the judgment rule and the historical accuracy data of each unit, historical accuracy is calculated. All historical output records of each unit under specific temperature difference conditions are statistically analyzed, and the judgment rule is applied to mark each record as accurate or inaccurate. Historical accuracy = number of accurate judgments / total number of judgments; this ratio quantifies the reliability performance of the unit in historical operation.

[0089] Based on the historical accuracy and the preset benchmark accuracy, calculate the confidence correction factor corresponding to each reading frequency determination unit;

[0090] The default unit confidence of each read frequency determination unit is adjusted using the confidence correction factor of each read frequency determination unit to obtain multiple dynamic unit confidences;

[0091] The read frequency determiner is reconfigured based on the confidence levels of the multiple dynamic units.

[0092] Furthermore, based on the comparison between historical accuracy and preset benchmark accuracy, the confidence correction factor corresponding to each reading frequency determination unit is calculated. The preset benchmark accuracy refers to the minimum acceptable accuracy threshold set according to equipment performance requirements and safety standards, such as 90% or 95%. The confidence correction factor = historical accuracy / preset benchmark accuracy. When the historical accuracy is higher than the preset benchmark accuracy, the correction factor is greater than one, indicating that the performance of the determination unit is better than expected; when the historical accuracy is equal to the preset benchmark accuracy, the correction factor is equal to one, indicating that the performance meets the requirements; when the historical accuracy is lower than the preset benchmark accuracy, the correction factor is less than one, indicating that the performance does not meet the standard. The correction magnitude is positively correlated with the degree of accuracy deviation.

[0093] Furthermore, a confidence correction factor is used to adjust the default unit confidence of each readout frequency determination unit to obtain a dynamic unit confidence. Dynamic unit confidence = default unit confidence × confidence correction factor. This dynamic confidence is updated periodically to ensure timely reflection of the latest performance of each determination unit.

[0094] Finally, the read frequency determiner is reconfigured based on the dynamic unit confidence level. The weight parameters within the read frequency determiner are updated, giving units with higher confidence levels greater weight in subsequent decisions. Through this optimization mechanism, the read frequency determiner can dynamically adjust the weight allocation of each unit based on actual operational performance, continuously improving prediction accuracy and environmental adaptability. Simultaneously, the introduction of cloud data enables cross-device knowledge sharing and experience transfer, allowing individual devices to benefit from the operational experience of the entire device group, significantly improving the overall performance and intelligence level of the device group.

[0095] In summary, the embodiments of this application have at least the following technical effects:

[0096] Compared to existing technologies, this application firstly achieves an organic integration of device authentication and charging control. NFC technology ensures that only authorized devices can initiate the charging backup process, improving operational security and reliability. Secondly, it employs a predictive control strategy based on temperature differences, proactively adjusting operating parameters before the storage chip temperature reaches its upper limit, effectively avoiding the temperature lag response problem in traditional solutions. Thirdly, a gradual parameter adjustment mechanism achieves a smooth transition in the data backup process, eliminating the impact of frequent start-stop cycles on the storage chip and significantly improving the stability and efficiency of the backup process. Finally, by intelligently coordinating charging power and backup strategies, it fully utilizes the charging time window, maximizing data backup while ensuring device safety, thus improving overall energy efficiency and user experience.

[0097] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0098] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0099] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. An NFC trigger control method integrating wireless charging and automatic data backup, characterized in that, The method includes: In response to the contact signal between the mobile device and the NFC identification area, the device is authenticated through the NFC triggering mechanism. After successful authentication, the current battery information of the mobile device is obtained based on the contact signal. Based on the current battery level information, determine the target charging power for the mobile device; The mobile device is wirelessly charged according to the target charging power, and the real-time device temperature of the mobile device is acquired in real time during the charging process. Obtain the upper limit operating temperature of the storage chip of the mobile device, and calculate the real-time temperature difference based on the upper limit operating temperature and the real-time device temperature; The real-time read parameters of the memory chip are determined based on the real-time temperature difference, and data backup control of the memory chip is performed based on the real-time read parameters, including: The read frequency determiner of the memory chip is invoked, wherein the read frequency determiner can convert the real-time temperature difference into the optimal read frequency to achieve predictive temperature control. The construction steps of the read frequency determiner for the memory chip include: Obtain read records of memory chips of the same model, construct a sample temperature difference set based on the read records, and label the read frequency of each sample temperature difference in the sample temperature difference set to obtain a sample read frequency set. Construct a multi-frequency-determined unit architecture; The multiple frequency determination unit architectures are trained using the sample temperature difference set and the sample read frequency set, respectively, to obtain multiple read frequency determination units and multiple default unit confidence levels. Based on the confidence levels of the multiple default units, the multiple read frequency determination units are integrated to obtain the read frequency determiner; The read frequency determiner processes the real-time temperature difference to obtain the optimal read frequency of the memory chip, which serves as the real-time read parameter of the memory chip. This includes: The real-time temperature difference value is input into the multiple reading frequency determination units respectively to obtain the multiple unit output frequencies; Obtain the default cell confidence level for each read frequency determination cell; Based on the output frequencies of the multiple units and the corresponding default unit confidence levels, the optimal read frequency of the memory chip is calculated by weighted averaging and used as the real-time read parameter of the memory chip.

2. The method according to claim 1, characterized in that, In response to a contact signal between the mobile device and the NFC identification area, the device is authenticated via an NFC trigger mechanism. Upon successful authentication, the current battery information of the mobile device is obtained based on the contact signal, including: When the mobile device comes into contact with the NFC recognition area, the device identification information of the mobile device is obtained; Based on the device identification information, query the pre-stored device binding relationship and verify the authorization status of the mobile device; When the verification is successful, the current battery information of the mobile device is obtained.

3. The method according to claim 1, characterized in that, Determining the target charging power for the mobile device based on the current battery level information includes: Obtain the battery capacity information and maximum charging power specifications of the mobile device; Based on the current power level information and the battery capacity information, determine the estimated charging time required to charge to the preset target power level; Based on the estimated charging time, a target charging power is determined to complete charging within the estimated charging time, wherein the target charging power does not exceed the maximum charging power specification of the mobile device.

4. The method according to claim 1, characterized in that, The method further includes: Establish a data connection with the cloud server to obtain historical accuracy data of each reading frequency determination unit of the same model of mobile device under the same real-time temperature difference conditions; Based on the historical accuracy data, the historical accuracy of each reading frequency determination unit under the same real-time temperature difference condition is calculated. Based on the historical accuracy and the preset benchmark accuracy, calculate the confidence correction factor corresponding to each reading frequency determination unit; The default unit confidence of each read frequency determination unit is adjusted using the confidence correction factor of each read frequency determination unit to obtain multiple dynamic unit confidences; The read frequency determiner is reconfigured based on the confidence levels of the multiple dynamic units.

5. The method according to claim 4, characterized in that, Based on the historical accuracy data, the historical accuracy of each reading frequency determination unit under the same real-time temperature difference condition is calculated, including: A first deviation threshold and a second deviation threshold are set, wherein the first deviation threshold is less than the second deviation threshold; Based on the first deviation threshold and the second deviation threshold, an accurate determination rule is set, wherein the accurate determination rule is: When the historical output frequency of the frequency determination unit is greater than the actual optimal frequency, and the deviation between the historical output frequency and the actual optimal frequency is greater than or equal to the first deviation threshold, it is determined to be inaccurate. When the historical output frequency of the frequency determination unit is less than the actual optimal frequency, and the deviation between the historical output frequency and the actual optimal frequency is greater than or equal to the second deviation threshold, it is determined to be inaccurate. When the historical output frequency of the frequency determination unit is equal to the actual optimal frequency, or when the historical output frequency of the frequency determination unit is greater than the actual optimal frequency and the deviation between the historical output frequency and the actual optimal frequency is less than the first deviation threshold, or when the historical output frequency of the frequency determination unit is less than the actual optimal frequency and the deviation between the historical output frequency and the actual optimal frequency is less than the second deviation threshold, it is determined to be accurate. Based on the accuracy determination rules and the historical accuracy data of each reading frequency determination unit, the historical accuracy of each reading frequency determination unit is calculated.

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