Charger adaptive charging control method and system

Abstract

The invention relates to the technical field of charging control, and discloses a charger adaptive charging control method and system, and the method comprises the steps: obtaining the electrical response reference information of a to-be-charged device in a healthy state, generating an electrical detection signal, and applying the electrical detection signal to the to-be-charged device, so as to collect the electrical response of the to-be-charged device; and then, comparing the collected electrical response with reference information, and comprehensively judging the health condition of the internal charging path of the to-be-charged equipment in combination with the battery state information of the to-be-charged equipment and the electrical characteristic information of the charging cable. Finally, according to the health condition judgment result, the charging parameters are adjusted in a self-adaptive mode, and clear diagnosis information is provided for the user. The method effectively solves the problems that in the prior art, a charger lacks the capability of directly diagnosing the health condition of a charging path in equipment, so that the problem root cannot be accurately judged, the charging efficiency is lower than expectation, the equipment is locally heated, and a user cannot obtain clear fault diagnosis information.

Description

Technical Field

[0001] This invention relates to the field of charging control technology, and in particular to an adaptive charging control method and system for a charger. Background Technology

[0002] With the increasing prevalence of smartphones and other mobile devices, users have higher expectations for charging efficiency and battery life. Modern smart chargers, especially those supporting advanced fast charging protocols, are designed to communicate intelligently with connected devices, flexibly adjusting charging strategies based on battery status information from the device's battery management system (BMS), such as remaining charge, temperature, and health status. This adaptive control aims to balance charging speed, battery life, and safety.

[0003] However, in practical use, due to the wide variety of devices and the varying levels of implementation of their built-in battery management systems and charging communication protocols, not all devices can provide detailed or fully standardized battery status information. Further complicating matters, even if the battery itself is in good health, the charging management module or its peripheral circuitry within the device may experience slight performance degradation due to long-term use or minor defects. In such cases, the battery management system may still report a "normal" status, causing the charger to attempt to output higher power after receiving seemingly normal battery data, but the actual charging efficiency is lower than expected, possibly accompanied by slight localized overheating of the device.

[0004] Because chargers lack the ability to directly diagnose the health of the device's internal charging path, they cannot accurately determine the root cause of the problem. They often adopt a conservative strategy, reducing output power, resulting in slow charging, a degraded user experience, and a lack of clear fault diagnosis information. This phenomenon leads users to misunderstand the charger or device, believing the product is in poor performance, yet they cannot obtain specific problem diagnosis.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] This invention provides an adaptive charging control method and system for chargers, aiming to solve the technical problem that existing chargers cannot accurately diagnose the root cause of problems when faced with slight performance degradation of the internal charging path of the device, resulting in decreased charging efficiency, poor user experience, and inability to provide clear fault diagnosis information.

[0007] The technical solution of this application is as follows:

[0008] In a first aspect, this application discloses an adaptive charging control method for a charger, comprising:

[0009] Obtain electrical response baseline information of the device to be charged in a healthy state;

[0010] An electrical detection signal is generated and applied to the device to be charged in order to collect the electrical response of the device to the electrical detection signal.

[0011] By comparing the electrical response with the electrical response benchmark information, and combining the battery status information of the device to be charged with the electrical characteristics information of the charging cable, the health status judgment result of the internal charging path of the device to be charged is obtained.

[0012] Based on the health status assessment, the charging parameters are adjusted, and diagnostic information is provided to the user.

[0013] This technical solution enables the charger to proactively detect the health status of the internal charging path of the device being charged and adaptively adjust the charging parameters based on the diagnostic results. It also provides clear diagnostic information to the user, thereby solving the problem in existing technologies where chargers cannot accurately determine the internal charging path of the device, resulting in low charging efficiency and poor user experience. This significantly improves the intelligence, safety, and user satisfaction of charging.

[0014] Furthermore, in one embodiment, by comparing the electrical response with electrical response reference information, and combining the battery status information of the device to be charged with the electrical characteristic information of the charging cable, a health status judgment result of the internal charging path of the device to be charged is obtained, including:

[0015] Connect the probe signal generator and analog-to-digital converter array to the internal reference loop to obtain the real-time reference response of the measurement link;

[0016] The deviation of the measurement link is calculated by comparing the real-time reference response with the pre-stored ideal reference response, and a correction matrix is ​​generated.

[0017] The electrical response is compensated by a correction matrix to eliminate the error of the measurement link and obtain the compensated instantaneous response data. The compensated instantaneous response data is then decomposed to obtain the components of the electrical detection signal, including linear response components, nonlinear harmonic components, environmental electromagnetic interference components, and internal transient load fluctuation components.

[0018] By combining contextual information and the characteristics of each component, the causal source of each component is inferred; based on the causal source determination results, the health status of the internal charging path of the device to be charged is determined.

[0019] This technical solution enables precise measurement link calibration and multi-dimensional signal decomposition analysis to accurately identify and distinguish between real anomalies in the internal charging path and external interference, thereby improving the accuracy and reliability of health status judgment, avoiding misjudgment and over-adjustment, and making charging control more precise and effective.

[0020] Based on the above, this application further proposes adjusting charging parameters according to the health status assessment results and providing diagnostic information to the user, including:

[0021] After the charger establishes a charging connection with the device to be charged, the real-time electrical response of the device to be charged is periodically acquired.

[0022] Based on the real-time electrical response, identify the dynamic change trend of abnormal internal charging path of the device to be charged;

[0023] Obtain instantaneous load information of other internal functions of the device to be charged; determine the user's current usage scenario;

[0024] Based on abnormal dynamic trends, instantaneous load information, and usage scenarios, adjust charging parameters and update diagnostic information.

[0025] Through this technical solution, the charger can dynamically monitor abnormal changes in the charging path inside the device and make a comprehensive judgment based on instantaneous load and user usage scenarios, thereby achieving real-time and fine-grained adjustment of charging parameters and providing more timely and contextualized diagnostic information, further optimizing the adaptability of charging strategies and user experience.

[0026] Furthermore, in some preferred embodiments, charging parameters are adjusted based on abnormal dynamic trends, instantaneous load information, and usage scenarios, including:

[0027] Assess the severity of internal charging path anomalies and obtain anomaly severity information; adjust the priority of charging efficiency according to the usage scenario;

[0028] Based on information on the severity of the anomaly, instantaneous load information, and the priority of charging efficiency, the impact on battery life and charging safety is estimated, and the compensation voltage increment is calculated.

[0029] Real-time monitoring of the internal temperature of the device to be charged to obtain internal temperature information;

[0030] Adjust the ramp rate or frequency of the charging current based on the internal temperature information.

[0031] Adjust the output waveform of the charging current based on the instantaneous load information.

[0032] This technical solution enables multi-dimensional and refined adjustments to charging parameters based on factors such as the severity of the anomaly, user scenario, instantaneous load, and internal temperature. These adjustments include compensation for voltage, current rise slope / frequency, and output waveform, thereby maximizing charging efficiency and user experience while ensuring charging safety and battery life.

[0033] As an optional solution, diagnostic information can be updated based on the abnormal dynamic trends, instantaneous load information, and usage scenarios, including:

[0034] Perform consistency checks on abnormal dynamic change trends, instantaneous load information, and usage scenario information;

[0035] Based on the consistency check results, weights are assigned to the abnormal dynamic change trends, instantaneous load information, and usage scenario information.

[0036] The stability of the diagnostic information was assessed.

[0037] Based on the results of the stability assessment, the diagnostic information was smoothed.

[0038] Output diagnostic information to the user.

[0039] This technical solution ensures the accuracy and reliability of diagnostic information through consistency checks, weight allocation, and stability assessments. Furthermore, by smoothing the data, it avoids false alarms caused by momentary fluctuations, thereby providing users with more stable and valuable diagnostic information.

[0040] To enhance functionality, a stability assessment of diagnostic information was conducted, including:

[0041] Retrieve current diagnostic information; retrieve the sequence of historical diagnostic information within a preset time window;

[0042] Calculate the statistical dispersion between the current diagnostic information and the historical diagnostic information sequence;

[0043] Obtain the real-time noise level of the internal measurement link of the charger;

[0044] Obtain the real-time intensity of environmental electromagnetic interference;

[0045] Based on the real-time noise level and real-time intensity, a comprehensive non-fault fluctuation threshold is obtained;

[0046] Compare statistical dispersion with the non-fault fluctuation threshold;

[0047] Based on the comparison results, determine whether there are any real abnormal fluctuations in the diagnostic information.

[0048] This technical solution can establish a dynamic non-fault fluctuation threshold by comprehensively considering the statistical dispersion, internal noise, and environmental interference of diagnostic information. This allows for a more accurate determination of whether there are real abnormal fluctuations in diagnostic information, effectively avoiding false alarms caused by non-fault factors and improving the reliability of diagnostic information.

[0049] To improve the solution, based on the stability assessment results, the diagnostic information was smoothed, including:

[0050] Based on the comparison results, determine whether there are any real abnormal fluctuations in the diagnostic information;

[0051] Based on the judgment results, the parameters of the smoothing process are dynamically adjusted to quickly respond to real abnormal changes and suppress non-fault fluctuations.

[0052] This technical solution allows for the dynamic adjustment of smoothing parameters based on the determination of whether there are real abnormal fluctuations in diagnostic information. This enables the rapid response to real abnormal changes while effectively suppressing non-fault fluctuations, ensuring that diagnostic information can reflect problems in a timely manner while maintaining stability.

[0053] To optimize the structure, the parameters of the smoothing process are dynamically adjusted based on the judgment results to quickly respond to real abnormal changes and suppress non-fault fluctuations, including:

[0054] After determining whether there are real abnormal fluctuations in the diagnostic information, obtain the adjustment history of the current smoothing parameters; based on the adjustment history, evaluate the frequency and magnitude of the current parameter adjustments.

[0055] When the evaluation results show that the parameter adjustment frequency is too high or the magnitude is too large, the parameter adjustment suppression mechanism is activated. The parameter adjustment suppression mechanism includes: limiting the number of parameter adjustments within a preset time period, or limiting the maximum change of a single parameter adjustment.

[0056] Based on the judgment results, under the parameter adjustment and suppression mechanism, the parameters of the smoothing process are adjusted to quickly respond to real abnormal changes and suppress non-fault fluctuations.

[0057] This technical solution introduces a parameter adjustment suppression mechanism to avoid system instability caused by frequent or large-scale adjustments when dynamically adjusting smoothing parameters, thereby achieving a better balance between rapidly responding to real abnormal changes and suppressing non-fault fluctuations.

[0058] To improve the design, after determining whether there are real abnormal fluctuations in the diagnostic information, the adjustment history of the current smoothing parameters is obtained, including:

[0059] Assign a unique identifier to each device to be charged or each charger.

[0060] When recording the adjustment actions, adjustment amounts, and adjustment timestamps of the smoothing parameters, the corresponding device identifier or charger identifier should also be recorded.

[0061] When retrieving adjustment history, filters are performed based on identifiers to obtain adjustment history for a specific device or charger.

[0062] This technical solution enables precise tracing of the parameter adjustment history for a specific device or charger by assigning a unique identifier to the device or charger and recording the adjustment history, thereby providing more refined data support for subsequent parameter optimization and fault analysis.

[0063] Secondly, this application also discloses a charger adaptive charging control system, comprising:

[0064] The detection end is used to obtain electrical response reference information of the device to be charged in a healthy state;

[0065] The judgment end is used to generate an electrical detection signal and apply the electrical detection signal to the device to be charged in order to collect the electrical response of the device to the electrical detection signal; compare the electrical response with the electrical response reference information, and combine the battery status information of the device to be charged and the electrical characteristic information of the charging cable to obtain the health status judgment result of the internal charging path of the device to be charged.

[0066] The adjustment terminal is used to adjust charging parameters based on the health status assessment results and provide diagnostic information to the user.

[0067] Through this technical solution, the system can achieve comprehensive perception, intelligent judgment, and adaptive control of the health status of the charging path inside the device by working together at the detection end, judgment end, and adjustment end. This effectively solves the problem that existing chargers cannot accurately diagnose internal device problems, resulting in low charging efficiency and poor user experience, and significantly improves the intelligence, safety, and user satisfaction of charging.

[0068] Beneficial effects

[0069] The adaptive charging control method disclosed in this application acquires electrical response benchmark information of the device under health conditions and generates an electrical detection signal to be applied to the device to collect its electrical response. Subsequently, the collected electrical response is compared with the benchmark information, and combined with the battery status information of the device under charge and the electrical characteristics of the charging cable, a comprehensive judgment is made on the health status of the internal charging path of the device. Finally, based on the health status judgment result, the charging parameters are adaptively adjusted, and clear diagnostic information is provided to the user. This method effectively solves the problems in existing technologies where chargers lack the ability to directly diagnose the health status of the internal charging path of the device, leading to inaccurate identification of the root cause of the problem, lower-than-expected charging efficiency, localized overheating of the device, and the inability of users to obtain clear fault diagnosis information. Through this technical solution, the charger can proactively and accurately identify potential anomalies in the internal charging path of the device, thereby avoiding conservative charging strategies adopted due to misjudgment or insufficient information, significantly improving charging efficiency, ensuring charging safety, and providing users with clear and valuable fault diagnosis information, greatly improving the user experience. Attached Figure Description

[0070] Figure 1 This is a flowchart of an adaptive charging control method for a charger provided in an embodiment of the present invention;

[0071] Figure 2 This is a flowchart of a method for comparing electrical response with electrical response reference information provided in an embodiment of the present invention;

[0072] Figure 3 This is a flowchart of a method for providing diagnostic information to a user according to an embodiment of the present invention;

[0073] Figure 4 This is a schematic diagram of the structure of an adaptive charging control system for a charger provided in an embodiment of the present invention. Detailed Implementation

[0074] 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.

[0075] Reference Figure 1 , Figure 1 This is a flowchart of a charger adaptive charging control method provided by an embodiment of the present invention, including the following steps: A charger adaptive charging control method, including:

[0076] S1, Obtain the electrical response reference information of the device to be charged in a healthy state;

[0077] S2, generate an electrical detection signal and apply the electrical detection signal to the device to be charged to collect the electrical response of the device to the electrical detection signal;

[0078] S3. Compare the electrical response with the electrical response reference information, and combine the battery status information of the device to be charged and the electrical characteristic information of the charging cable to obtain the health status judgment result of the internal charging path of the device to be charged.

[0079] S4. Based on the health status assessment results, adjust the charging parameters and provide diagnostic information to the user.

[0080] This application, by acquiring the electrical response reference information of the device to be charged and combining it with real-time electrical detection, battery status information, and the electrical characteristics of the charging cable, can accurately determine the health status of the charging path inside the device. Therefore, the charger can adaptively adjust charging parameters based on the judgment results and provide the user with detailed diagnostic information, thereby effectively solving the problems of low charging efficiency, poor user experience, and lack of clear fault diagnosis in existing technologies.

[0081] To better understand this application, some key terms need to be explained. "Electrical response reference information" refers to the electrical characteristic data exhibited by the device to be charged in response to a specific electrical detection signal under ideal health conditions, such as impedance spectrum and voltage-current curves. This information serves as a reference standard for judging the health status of the charging path within the device. "Electrical detection signal" refers to a specific signal generated by the charger and applied to the device to be charged; it can be a DC signal, AC signal, pulse signal, or a combination thereof, used to stimulate the electrical response of the device. "Electrical response" refers to the changes in electrical parameters such as voltage, current, and impedance generated by the internal circuitry of the device to be charged after receiving an electrical detection signal.

[0082] The core of the adaptive charging control method for chargers in this application lies in the accurate judgment and adaptive adjustment of the health status of the internal charging path of the device.

[0083] Firstly, there are several ways to obtain baseline information on the electrical response of devices under healthy conditions. For example, at the factory, each device can be comprehensively tested using professional testing equipment, recording its response data to a series of preset electrical detection signals under standard healthy conditions, and storing this data in the device's firmware or a cloud database. Another approach is for the charger to actively generate various electrical detection signals and apply them to the device when it is first connected to the charger and its healthy condition is confirmed, collecting its electrical response and storing it as baseline information. Furthermore, big data analysis can be used to extract common features from the charging data of a large number of healthy devices, constructing a universal electrical response baseline model.

[0084] Secondly, regarding the generation and application of electrical detection signals to the device being charged to acquire the device's electrical response, the charger can integrate a signal generator to generate electrical detection signals of different frequencies, amplitudes, and waveforms. For example, a swept-frequency sine wave signal covering a range from low to high frequencies can be generated to detect different frequency response characteristics within the device. This signal is applied to the device being charged through the charging interface. Simultaneously, the charger's internal analog-to-digital converter (ADC) array monitors and acquires the voltage and current responses of the device to these detection signals in real time, converting the analog signals into digital signals for subsequent processing.

[0085] Furthermore, in comparing the electrical response with reference information, and combining this with the battery status information of the device being charged and the electrical characteristics of the charging cable, the charger receives battery status information from the device, such as remaining battery capacity, cycle count, temperature, and internal resistance. Simultaneously, the charger identifies the type and specifications of the connected charging cable and acquires its electrical characteristics, such as impedance and maximum current carrying capacity. Then, the real-time electrical response is compared with pre-stored reference information. This comparison can employ various algorithms, such as feature matching, pattern recognition, and machine learning. By comprehensively analyzing the deviation in the electrical response, battery status information, and the electrical characteristics of the charging cable, it is possible to more accurately determine whether there are abnormalities in the internal charging path of the device being charged, such as poor connector contact, degraded charging management chip performance, or increased internal circuit impedance.

[0086] Finally, regarding adjusting charging parameters and providing diagnostic information to users based on health status assessments, once the health status of the internal charging path is assessed, the charger dynamically adjusts the charging parameters accordingly. For example, if the assessment indicates a minor abnormality in the charging path, the charger may appropriately reduce the charging current or adjust the charging voltage to avoid further damage to the device while ensuring charging safety. If the abnormality is more severe, the charger may significantly reduce the charging power or even suspend charging. Furthermore, the charger generates corresponding diagnostic information and provides users with intuitive fault diagnosis information, such as "poor contact of the charging cable" or "abnormality of the internal charging module of the device," through the charger's display screen, accompanying application, or indicator lights, along with corresponding suggestions, such as "Please check the charging cable connection" or "Contact after-sales service."

[0087] This application's adaptive charging control method significantly improves the intelligence level of charging by introducing direct diagnostic capabilities for the health status of the device's internal charging path. Compared to existing technologies that rely solely on feedback information from the battery management system, this application can more comprehensively and accurately assess potential problems during the charging process. For example, in existing technologies, when there is a slight increase in impedance in the device's internal charging path, the battery management system may still report a "normal" state, causing the charger to output higher power, but the actual charging efficiency is low and accompanied by heat generation. This application, by acquiring electrical response benchmark information and combining it with real-time electrical detection, battery status information, and the electrical characteristics of the charging cable, can identify such anomalies in the internal charging path and adjust charging parameters accordingly, avoiding ineffective high-power charging and improving charging efficiency and safety. Simultaneously, providing users with clear diagnostic information also addresses the pain point of user confusion regarding charging problems, improving the user experience. This method not only optimizes the charging process and extends battery life but also provides users with a more transparent and reliable charging service.

[0088] In some embodiments described above, this application proposes comparing the electrical response with electrical response benchmark information, combining the battery status information of the device to be charged with the electrical characteristics of the charging cable, to obtain a health status assessment result for the internal charging path of the device to be charged. However, in actual operation, the acquisition and comparison process of the electrical response may be affected by various factors such as measurement link errors, environmental electromagnetic interference, and internal transient load fluctuations, which limits the accuracy of the health status assessment result. If these interference factors are not addressed, the health status of the charging path may be misjudged, affecting charging efficiency and safety. Therefore, this application further proposes a more accurate and robust assessment method, which improves the accuracy of health status assessment by performing multi-dimensional decomposition and causal analysis of the electrical response.

[0089] refer to Figure 2 , Figure 2 This is a flowchart of a method for comparing electrical response with electrical response reference information provided by an embodiment of the present invention, including the following steps:

[0090] S31 connects the probe signal generator and analog-to-digital converter array to the internal reference loop to obtain the real-time reference response of the measurement link;

[0091] S32, compare the real-time reference response with the pre-stored ideal reference response, calculate the deviation of the measurement link, and generate a correction matrix;

[0092] S33, perform the correction matrix compensation on the electrical response to eliminate the error of the measurement link and obtain the compensated instantaneous response data;

[0093] S34, decompose the compensated instantaneous response data to obtain the components of the electrical detection signal, wherein the components include linear response components, nonlinear harmonic components, environmental electromagnetic interference components and internal transient load fluctuation components.

[0094] S35, combining contextual information and the characteristic analysis of each component, infer the causal source of each component;

[0095] S36, Based on the cause-and-effect judgment result, determine the health status of the internal charging path of the device to be charged.

[0096] Specifically, a probe signal generator can be understood as a circuit or module used to generate an electrical probe signal of a specific frequency, amplitude, or waveform. For example, it can be an arbitrary waveform generator or a pulse generator, its purpose being to apply a controllable excitation signal to the device to be charged. An analog-to-digital converter (ADC) array refers to a group or more ADCs used to convert the analog electrical response of the device to the electrical probe signal into a digital signal for subsequent digital processing and analysis. An internal reference loop refers to a reference circuit inside the charger used to calibrate the performance of the measurement system itself. Its electrical characteristics are known and stable; for example, it can be a high-precision resistor-capacitor network. By connecting the probe signal generator and the ADC array to the internal reference loop, the real-time reference response of the measurement link under the current environment can be obtained, which can be used to evaluate the performance drift or error of the measurement system itself.

[0097] The pre-stored ideal reference response refers to the standard response data obtained by measuring the internal reference circuit under ideal conditions during charger manufacturing or calibration. It represents the theoretical performance of the measurement link under error-free conditions. By comparing the real-time reference response with the pre-stored ideal reference response, the deviation of the measurement link can be calculated, for example, by calculating the difference in mean square error or correlation coefficient between the two. Based on this deviation, a correction matrix can be generated. This correction matrix is ​​used to correct subsequently acquired electrical responses to eliminate systematic and random errors introduced by the measurement link itself.

[0098] In practical applications, correction matrix compensation for electrical response involves performing mathematical operations, such as matrix multiplication or weighted averaging, between the acquired raw electrical response data and the generated correction matrix to offset the influence of measurement link errors. This yields compensated instantaneous response data that more accurately reflects the electrical characteristics of the device being charged, eliminating interference from measurement system errors.

[0099] Furthermore, decomposing the compensated instantaneous response data involves using signal processing techniques, such as Fourier transform, wavelet analysis, or independent component analysis, to break down the complex instantaneous response data into multiple independent or semi-independent components. The components of the electrical detection signal include linear response components, nonlinear harmonic components, environmental electromagnetic interference components, and internal transient load fluctuation components. Linear response components typically reflect the basic electrical characteristics of the device under normal operating conditions, such as resistance, capacitance, and inductance. Nonlinear harmonic components may indicate the presence of nonlinear components in the charging path or rectification effects caused by poor connections. Environmental electromagnetic interference components refer to noise signals coupled into the measurement system or the device under charge by external electromagnetic fields. Internal transient load fluctuation components originate from instantaneous current or voltage changes generated by other functional modules within the device under charge during the charging process.

[0100] By combining contextual information and the characteristic analysis of each component, the causal source of each component can be inferred. Contextual information may include the model of the device to be charged, battery type, current charging stage, ambient temperature, and charging cable specifications. Characteristic analysis refers to in-depth study of the frequency, amplitude, phase, duration, and other characteristics of each component. For example, harmonics at a specific frequency may be associated with a specific fault mode, while random broadband noise is more likely to be environmental interference. By integrating this information, it is possible to more accurately determine whether each component is caused by measurement error, external interference, or a specific problem within the device to be charged.

[0101] Finally, based on the causal source determination results, the health status of the internal charging path of the device to be charged is determined. For example, if the nonlinear harmonic component is abnormally high, and combined with the context information it is determined that it is not required for the normal charging process, it may indicate poor contact or component aging in the charging path; if the environmental electromagnetic interference component is too high, it may prompt the user to change the charging environment or cable.

[0102] This application's solution effectively addresses the potential interference issues in health status assessment inherent in basic schemes by introducing real-time calibration of the measurement link and multi-component decomposition of the electrical response. First, by connecting the probe signal generator and analog-to-digital converter array to the internal reference loop and acquiring the real-time reference response, the performance of the measurement link itself can be dynamically monitored and evaluated. Second, by comparing the real-time reference response with a pre-stored ideal reference response, the deviation of the measurement link is calculated, and a correction matrix is ​​generated. This allows subsequent electrical responses to be compensated for using the correction matrix, thereby eliminating errors in the measurement system itself and ensuring the accuracy of the original electrical response data. It is precisely this precise calibration of the original data that allows subsequent analysis to be built on a reliable foundation. Based on this, the compensated instantaneous response data is decomposed into linear response components, nonlinear harmonic components, environmental electromagnetic interference components, and internal transient load fluctuation components, clearly separating the originally mixed signals. This decomposition allows the characteristics of each component to be analyzed independently, thereby, combined with contextual information, more accurately inferring the causal origin of each component. For example, identifying abnormal nonlinear harmonic components can directly pinpoint nonlinear faults in the charging path; identifying excessively high levels of environmental electromagnetic interference can rule out the possibility of internal equipment malfunctions. Ultimately, based on these precise causal source determinations, a more accurate and detailed assessment of the health status of the internal charging path can be made, avoiding misjudgments caused by external interference or measurement errors.

[0103] Through the above technical solution, this application can significantly improve the accuracy and reliability of judging the health status of the internal charging path of the device to be charged. Compared with the basic solution that only makes simple comparisons, this solution effectively eliminates the influence of the measurement system's own errors on the judgment results by introducing a real-time calibration and error compensation mechanism for the measurement link. In addition, by performing a refined multi-component decomposition of the electrical response and combining it with contextual information for causal source analysis, the system can not only identify anomalies, but also further locate the specific nature and cause of the anomalies, such as whether they are caused by internal component aging, poor contact, external electromagnetic interference, or fluctuations in other loads within the device. This in-depth diagnostic capability enables the charger to provide more targeted diagnostic information and more reasonable charging parameter adjustment strategies, thereby extending battery life, improving charging safety, and optimizing user experience.

[0104] In some preferred embodiments, a specific example is given below. Suppose that during the charging process of a device to be charged, a slight contact failure occurs in its charging path.

[0105] First, the charger generates an electrical probe signal and applies it to the device to be charged, while simultaneously acquiring its electrical response. To ensure measurement accuracy, the probe signal generator and analog-to-digital converter array are connected to an internal reference loop to obtain the real-time reference response of the measurement link. This real-time reference response is then compared with a pre-stored ideal reference response to calculate the deviation of the measurement link and generate a correction matrix. For example, if a slight gain drift is detected in the analog-to-digital converter array, the correction matrix will include a corresponding correction factor.

[0106] Next, the acquired raw electrical response data will be compensated using the correction matrix to eliminate the error of the measurement link itself and obtain the compensated instantaneous response data.

[0107] Subsequently, the compensated instantaneous response data is decomposed. Due to poor contact, the decomposition results may show an abnormally high nonlinear harmonic component, while the linear response component, environmental electromagnetic interference component, and internal transient load fluctuation component remain within the normal range.

[0108] By combining contextual information (e.g., the device model is prone to charging interface wear after long-term use) and the characteristic analysis of each component (e.g., the frequency characteristics of the nonlinear harmonic are consistent with the rectification effect caused by poor contact), the system can infer that the causal source of the nonlinear harmonic component is poor contact in the charging path.

[0109] Ultimately, based on this causal relationship assessment, the system determined that the internal charging path of the device to be charged had a health issue due to poor contact. Based on this assessment, the charger can adjust charging parameters, such as appropriately reducing the charging current to avoid overheating of the contact points, and provide the user with diagnostic information such as "Please check the charging cable connection or try replacing the cable," thereby effectively avoiding potential safety hazards and extending the device's lifespan.

[0110] In some embodiments described above, this application proposes adjusting charging parameters and providing diagnostic information based on the health status assessment of the internal charging path of the device to be charged. However, in practical applications, the health status of the internal charging path of the device to be charged may not be constant; its abnormal conditions may exhibit dynamic changes. Furthermore, if the adjustment of charging parameters and the provision of diagnostic information do not fully consider the instantaneous load of the device and the user's usage scenario, it may lead to poor charging efficiency, reduced safety, or inaccurate diagnostic information. Therefore, this application further proposes a more refined scheme for adjusting charging parameters and providing diagnostic information to address the dynamic changes in the internal charging path and optimize the charging experience.

[0111] In this regard, refer to Figure 3 , Figure 3This is a flowchart of a method for providing diagnostic information to a user according to an embodiment of the present invention. S4 includes:

[0112] S41, after the charger establishes a charging connection with the device to be charged, the real-time electrical response of the device to be charged is periodically acquired.

[0113] S42, Based on the real-time electrical response, identify the dynamic change trend of the internal charging path anomaly of the device to be charged;

[0114] S43, Obtain instantaneous load information of other internal functions of the device to be charged;

[0115] S44 determines the user's current usage scenario;

[0116] S45, adjust the charging parameters and update the diagnostic information based on the abnormal dynamic change trend, the instantaneous load information, and the usage scenario.

[0117] Specifically, after the charger establishes a charging connection with the device to be charged, periodically acquiring the real-time electrical response of the device means that during the charging process, the charger continuously collects electrical characteristic data of the device under its current operating state at preset time intervals or trigger conditions. This data can include parameters such as voltage, current, impedance, and capacitance, reflecting the instantaneous state of the charging path within the device. Its purpose is to monitor the health of the charging path in real time so as to promptly detect potential anomalies.

[0118] Identifying the dynamic trends of internal charging path anomalies in the device being charged based on real-time electrical response can be understood as performing time-series analysis, trend prediction, or pattern recognition on periodically acquired real-time electrical response data to determine whether the charging path anomaly is worsening, improving, or remaining stable. For example, moving averages, Kalman filtering, or machine learning algorithms can be used to analyze the rate of change and volatility of the electrical response data, thereby revealing the evolution pattern of the anomaly. The aim is to grasp the dynamic characteristics of the anomaly and provide a more accurate basis for subsequent parameter adjustments.

[0119] In practical applications, obtaining instantaneous load information of other internal functions of the device being charged specifically refers to the charger or the power management unit inside the device monitoring in real time the instantaneous power consumption of other components (such as the processor, screen, communication module, etc.) besides the charging function. For example, data such as CPU usage, screen brightness, and network activity can be obtained and converted into corresponding instantaneous current or power requirements. The purpose is to understand the actual power load of the device while charging, avoiding misjudgment of the charging path or unstable charging due to load fluctuations.

[0120] Furthermore, determining the user's current usage scenario involves analyzing sensor data from the device being charged (such as GPS, accelerometer, and ambient light sensor), application usage patterns, or user settings to infer the user's current environment and usage pattern. For example, it can be determined whether the user is indoors, outdoors, stationary, moving, playing games, or watching videos. The goal is to personalize and optimize charging parameter adjustment strategies and diagnostic information presentation methods based on different usage scenarios, thereby enhancing the user experience.

[0121] Therefore, adjusting charging parameters and updating diagnostic information based on abnormal dynamic trends, instantaneous load information, and usage scenarios involves comprehensively analyzing and making decisions based on the various pieces of information obtained. Adjustments to charging parameters can include charging voltage, charging current, and charging mode (such as constant current, constant voltage, trickle charging) to adapt to dynamic changes in the charging path, instantaneous load demands, and user preferences. Simultaneously, updating diagnostic information means providing users with more timely, accurate, and contextualized device health status and charging recommendations based on this real-time information. The aim is to achieve intelligent and adaptive management of the charging process, ensuring charging safety, extending battery life, and optimizing the user experience.

[0122] This application's solution overcomes the limitations of adjusting parameters based solely on a one-time health status assessment by periodically acquiring the real-time electrical response of the device to be charged after the charging connection is established, and identifying dynamic trends of abnormal changes in the internal charging path accordingly. Through real-time monitoring, the charger can promptly detect subtle changes and potential risks in the charging path, thereby avoiding decreased charging efficiency or safety hazards caused by abnormal deterioration. Furthermore, the solution acquires instantaneous load information of other internal functions of the device to be charged and determines the user's current usage scenario. This ensures that charging parameter adjustments are no longer isolated but fully consider the actual operating load of the device and the user's personalized needs for the charging experience. For example, in scenarios where the device is operating under high load or the user urgently needs fast charging, even with minor anomalies, the system can prioritize meeting the charging speed requirement while ensuring safety. Conversely, in scenarios where the device is idle or the user is not in a hurry to charge, a more conservative charging strategy can be adopted to maximize battery life. Therefore, this application's solution, through comprehensive consideration of multi-dimensional information, achieves refined and dynamic adjustment of charging parameters and provides more context-aware diagnostic information, significantly improving charging adaptability, safety, and user satisfaction.

[0123] Through the above technical solution, this application enables more refined and intelligent management of the charging process. Specifically, by periodically acquiring real-time electrical responses and identifying abnormal dynamic trends, the charger can promptly detect and respond to potential problems in the charging path, effectively preventing charging interruptions or equipment damage caused by the deterioration of abnormal conditions. Simultaneously, by combining instantaneous load information and user scenarios, the adjustment of charging parameters is more closely aligned with actual needs, ensuring both charging efficiency and safety while also considering user experience, such as providing optimal charging strategies under different usage scenarios. Furthermore, the updated diagnostic information is more accurate and practical, providing users with more valuable equipment health status and charging suggestions, thereby extending the lifespan of the equipment being charged and improving the overall reliability of the charging system.

[0124] In some preferred embodiments, this application is implemented as follows.

[0125] Suppose a smartphone is being charged via a charger. The charger first assesses the health of the phone's internal charging path using the method described above. After the charging connection is established, the charger acquires the phone's real-time electrical response every 5 seconds, measuring instantaneous fluctuations in charging current and voltage. By analyzing this real-time data, the charger identifies a component in the phone's internal charging path (such as the charging IC or battery connector) whose impedance is slowly increasing, exhibiting an abnormally deteriorating dynamic trend.

[0126] Meanwhile, the charger obtains information from the phone's power management unit indicating that the phone is currently running a demanding game, with high CPU and GPU loads and high screen brightness, suggesting a significant instantaneous load. Furthermore, using data from the phone's GPS and ambient light sensors, the charger determines that the user is using the phone outdoors in sunlight, a high-intensity usage scenario. Combining this information, the charger assesses the severity of the anomaly and dynamically adjusts charging parameters based on the user's priority for charging speed under high-intensity usage conditions. For example, the charger might slightly increase the charging voltage and adjust the output waveform of the charging current to accommodate instantaneous load fluctuations, while simultaneously sending a diagnostic message to the user: "A slight anomaly detected in the charging path; please check at your convenience. The charging strategy has been optimized to ensure charging efficiency." If the anomaly worsens, the charger might reduce the charging current to protect the device and provide more urgent diagnostic information, such as "The charging path anomaly has worsened; charging speed has been reduced to protect the device; please send it for inspection as soon as possible." In this way, the charger can intelligently adjust its charging strategy and provide personalized diagnostics based on real-time dynamic changes, device load, and user scenarios, thereby optimizing the charging experience and extending device lifespan.

[0127] In some embodiments described above, this application proposes adjusting charging parameters and updating diagnostic information based on abnormal dynamic trends, instantaneous load information, and usage scenarios. However, in practical applications, adjusting parameters solely based on this information may not adequately consider the severity of internal charging path anomalies, the priority of charging efficiency, the impact on battery life and charging safety, as well as the real-time changes in the internal temperature and instantaneous load of the device being charged. This could result in insufficiently precise adjustment of charging parameters, affecting charging performance or safety.

[0128] In response, this application further proposes the following steps for adjusting charging parameters based on abnormal dynamic trends, instantaneous load information, and usage scenarios:

[0129] Assess the severity of the internal charging path anomaly to obtain anomaly severity information;

[0130] Adjust the priority of the charging efficiency according to the usage scenario;

[0131] Based on the severity of the anomaly, the instantaneous load information, and the priority of the charging efficiency, the impact on battery life and charging safety is estimated, and the compensation voltage increment is calculated.

[0132] The internal temperature of the device to be charged is monitored in real time to obtain internal temperature information;

[0133] Based on the internal temperature information, adjust the rising slope or frequency of the charging current;

[0134] The output waveform of the charging current is adjusted based on the instantaneous load information.

[0135] Specifically, assessing the severity of internal charging path anomalies involves analyzing the dynamic trends of these anomalies and, in conjunction with preset thresholds or models, quantifying the severity level of the internal charging path problem. This level can be categorized as minor, moderate, or severe, and anomaly severity information can be generated. This information serves as an important basis for subsequent adjustments to charging parameters.

[0136] Adjusting the priority of charging efficiency based on usage scenarios can be understood as balancing charging speed with charging safety or battery life under different user conditions. For example, in scenarios where users urgently need fast charging, the priority of charging efficiency can be appropriately increased; while in scenarios where users charge for extended periods or have high requirements for battery life, the priority of charging efficiency can be decreased, prioritizing safety and battery health.

[0137] In practical applications, based on information about the severity of anomalies, instantaneous load information, and the priority of charging efficiency, the impact on battery life and charging safety is estimated, and the compensation voltage increment is calculated. This involves comprehensively considering these factors, using a pre-set algorithm or model, predicting the potential long-term damage or short-term risks to the battery under current charging conditions, and calculating the amount of compensation needed for the charging voltage to optimize the charging process while ensuring safety. The compensation voltage increment can be used to fine-tune the charging voltage to adapt to internal path anomalies.

[0138] In addition, real-time monitoring of the internal temperature of the device being charged is crucial for understanding its internal thermal state. Internal temperature is a key factor affecting battery charging performance and safety; both excessively high and low temperatures can damage the battery.

[0139] Furthermore, adjusting the rising slope or frequency of the charging current based on internal temperature information means that when the internal temperature is too high, the rising slope or frequency of the charging current can be reduced to slow down heat generation; when the internal temperature is moderate, it can be adjusted as needed to optimize the charging speed.

[0140] Simultaneously, the output waveform of the charging current is adjusted based on instantaneous load information to adapt to the power demands of other functions within the device being charged. For example, when there is a high instantaneous load inside the device, the waveform of the charging current (such as pulse charging or trickle charging) can be adjusted to avoid interfering with other functions within the device or to utilize charging energy more effectively.

[0141] This application's solution introduces an assessment of the severity of internal charging path anomalies, transforming charging parameter adjustments from simple responses into a quantifiable understanding of the problem's severity. Based on this, it prioritizes charging efficiency according to user scenarios, achieving personalized and intelligent charging strategies that better balance charging speed and battery health. By anticipating the impact on battery life and charging safety and calculating compensating voltage increments, the charger can still provide safe and battery-friendly charging even when internal path anomalies exist. Simultaneously, by real-time monitoring of internal temperature and adjusting the charging current's rise rate or frequency, as well as adjusting the charging current's output waveform based on instantaneous load information, this application dynamically adapts to changes in the internal environment of the device being charged, effectively managing thermal effects and power distribution, thereby preventing damage to the device or interference with the normal operation of other functions.

[0142] Through the above technical solutions, this application enables more refined and adaptive adjustments to charging parameters. Compared to coarse adjustments based solely on abnormal trends, load, and scenarios, this application significantly improves the safety of the charging process and the protection level of battery life by assessing the severity of anomalies, dynamically adjusting charging efficiency priorities, estimating battery impact, and calculating compensation voltage increments. Furthermore, real-time monitoring of internal temperature and adjustment of current slope or frequency, as well as adjustment of the current output waveform based on instantaneous load, allow the charging process to better adapt to the internal dynamic changes of the device being charged, effectively avoiding overheating risks and internal functional interference, thereby providing a more stable, efficient, and intelligent charging experience.

[0143] In some preferred embodiments, a specific example is given below. Suppose that the charger detects a moderate anomaly in the internal charging path of the device to be charged and generates corresponding anomaly severity information.

[0144] Specifically, if a user's current usage scenario is determined to be "urgently requiring fast charging," the system will prioritize charging efficiency accordingly. In this case, the system will comprehensively consider information on the severity of moderate anomalies, the instantaneous load information of the device being charged (e.g., the device is running a high-power application), and the higher charging efficiency priority to estimate the impact on battery life and charging safety. For example, the system might calculate a small compensation voltage increment to maximize charging speed while ensuring basic safety.

[0145] Meanwhile, the system monitors the internal temperature of the device being charged in real time. If the internal temperature information shows that the temperature is rising rapidly, the system will appropriately reduce the rate or frequency of the charging current increase to prevent the device from overheating. In addition, if the instantaneous load information shows that there is a large instantaneous power consumption demand inside the device, the system may adjust the output waveform of the charging current, such as by using a pulse charging mode, to better adapt to internal load fluctuations, reduce interference with other functions of the device, and optimize charging efficiency.

[0146] Conversely, if the user scenario is "long-term charging overnight", the priority of charging efficiency will be adjusted to a lower level. The system will be more inclined to protect battery life and charging safety, and may calculate a larger compensation voltage increment and more conservatively adjust the rising slope and frequency of charging current, even if the internal temperature only rises slightly.

[0147] Specifically, in some embodiments of the above-mentioned adaptive charging control method for chargers, the process of updating diagnostic information is further refined to ensure the accuracy and reliability of the provided diagnostic information.

[0148] The step of updating the diagnostic information based on the dynamic trend of the anomaly, the instantaneous load information, and the usage scenario includes:

[0149] A consistency check is performed on the dynamic change trend of the anomaly, the instantaneous load information, and the usage scenario information;

[0150] Based on the consistency check results, weights are assigned to the dynamic change trend of the anomaly, the instantaneous load information, and the usage scenario information;

[0151] The stability of the diagnostic information was assessed.

[0152] Based on the results of the stability assessment, the diagnostic information is smoothed.

[0153] The diagnostic information is output to the user.

[0154] Specifically, consistency checks refer to verifying whether there are logical conflicts or data anomalies between the acquired abnormal dynamic trends, instantaneous load information, and usage scenario information. For example, if an abnormal trend indicates severe battery aging, but the instantaneous load information shows the device is in a low-power standby state, and the usage scenario is indoors and stationary, there may be data acquisition errors or inconsistencies. Consistency checks can effectively eliminate false alarms or inaccurate data input.

[0155] Weighting refers to assigning different importance coefficients to abnormal dynamic trends, instantaneous load information, and usage scenario information based on the results of consistency checks. For example, when a consistency check finds that an information source has high uncertainty, its weight can be reduced; conversely, for information with high certainty, its weight can be increased. This aims to ensure that all input data are reasonably considered during the subsequent diagnostic information generation process, avoiding the excessive impact of a single abnormal data point on the overall diagnostic result.

[0156] In practical applications, stability assessment refers to judging the reliability of the diagnostic information to be output. This may include analyzing the fluctuation of the diagnostic information over a period of time, or comparing it with historical diagnostic data to determine whether the current diagnostic result is a genuine anomaly, rather than being caused by measurement noise or transient interference.

[0157] Furthermore, smoothing refers to filtering or averaging diagnostic information based on the results of stability assessment. Its purpose is to eliminate short-term fluctuations and noise in the diagnostic information, making it more representative and stable, thereby avoiding providing users with frequently changing or inaccurate diagnostic prompts.

[0158] Finally, the diagnostic information processed as described above will be output to the user so that the user can understand the health status of the internal charging path of the device to be charged.

[0159] This application's solution effectively identifies and eliminates unreasonable or conflicting data by performing consistency checks on abnormal dynamic trends, instantaneous load information, and usage scenario information, thereby ensuring the reliability of the diagnostic information input source. Therefore, by assigning weights to this information, its impact on the diagnostic results can be finely controlled according to its reliability and importance, avoiding excessive interference from a single abnormal data point on the overall diagnostic results. Furthermore, a stability assessment of the diagnostic information distinguishes between genuine abnormal fluctuations and non-fault-related fluctuations, ensuring the accuracy of the diagnostic results. It is precisely because of this smoothing process that the diagnostic information output to the user is more stable and easier to understand, avoiding false alarms or frequent prompts caused by instantaneous fluctuations, thus improving the user experience and the practicality of the diagnosis.

[0160] Through the above technical solutions, this application can significantly improve the accuracy, stability, and reliability of the provided diagnostic information. Specifically, by introducing consistency checks and weight allocation mechanisms, inaccurate or conflicting input data can be effectively filtered out, making the diagnostic results closer to the actual condition of the device being charged. Furthermore, by performing stability assessments and smoothing processes on the diagnostic information, false alarms caused by measurement noise and transient interference can be effectively suppressed, ensuring that the diagnostic information received by the user is verified and has practical guiding significance. This not only avoids the inconvenience caused to users by frequent or inaccurate diagnostic information but also enables users to understand the health status of the device more promptly and accurately, thereby taking appropriate maintenance measures, extending device lifespan, and ensuring charging safety.

[0161] In some of the embodiments described above in this application, stability assessment of the diagnostic information is necessary to output accurate diagnostic information to the user. However, during actual charging, the diagnostic information may be affected by noise in the charger's internal measurement link and environmental electromagnetic interference, leading to unnecessary fluctuations in the diagnostic results, thereby affecting the accuracy of the diagnosis and the user experience. If these problems are not addressed, the system may frequently report false alarms or miss actual faults, thus affecting charging safety and battery life.

[0162] In this regard, this application further proposes steps for conducting a stability assessment of diagnostic information, which include:

[0163] Obtain current diagnostic information;

[0164] Obtain the historical diagnostic information sequence within a preset time window;

[0165] Calculate the statistical dispersion between the current diagnostic information and the historical diagnostic information sequence;

[0166] Obtain the real-time noise level of the internal measurement link of the charger;

[0167] Obtain the real-time intensity of environmental electromagnetic interference;

[0168] Based on the real-time noise level and the real-time intensity, a comprehensive non-fault fluctuation threshold is obtained;

[0169] The statistical dispersion is compared with the non-fault fluctuation threshold;

[0170] Based on the comparison results, it is determined whether the diagnostic information exhibits any real abnormal fluctuations.

[0171] Specifically, when assessing the stability of diagnostic information, it is first necessary to obtain the diagnostic information at the current moment. This current diagnostic information can be a preliminary diagnostic result obtained after weighting the dynamic trend of the anomaly, instantaneous load information, and usage scenario information as described above. Simultaneously, for time-series comparison, it is also necessary to obtain the historical diagnostic information sequence stored within a preset time window. This preset time window can be configured according to actual application needs; for example, it can be diagnostic data from the past 1 minute, 5 minutes, or longer.

[0172] Furthermore, by calculating the statistical dispersion between the current diagnostic information and the historical diagnostic information sequence, the degree of fluctuation of the current diagnostic information relative to the historical trend can be quantified. The statistical dispersion can be calculated using various statistical methods, such as standard deviation, variance, mean absolute deviation, or moving average deviation, to reflect the degree of dispersion of data points.

[0173] Furthermore, to distinguish between fluctuations caused by actual faults and non-fault-related fluctuations, it is necessary to obtain the real-time noise level of the charger's internal measurement links and the real-time intensity of environmental electromagnetic interference. The real-time noise level of the charger's internal measurement links can be obtained by real-time monitoring and analysis of the outputs of sensors or measurement circuits within the charger, for example, by collecting baseline noise when there is no signal input. The real-time intensity of environmental electromagnetic interference can be detected in real-time by electromagnetic sensors integrated into the charger or the device being charged, or estimated by analyzing signal fluctuations within a specific frequency range.

[0174] Therefore, based on the real-time noise level and the real-time intensity, a comprehensive non-fault fluctuation threshold can be obtained. This threshold is designed to characterize the range of diagnostic information fluctuations caused by internal noise and external interference under normal operating conditions. The comprehensive non-fault fluctuation threshold can be calculated by weighted summation of the real-time noise level and real-time intensity, taking the maximum value, or by using a preset empirical formula.

[0175] Finally, the calculated statistical dispersion is compared with the non-faulty fluctuation threshold. If the statistical dispersion significantly exceeds the non-faulty fluctuation threshold, it can be determined that the diagnostic information exhibits genuine abnormal fluctuations, indicating a possible actual internal charging path problem. Conversely, if the statistical dispersion is within the threshold range, the current fluctuation is considered to be within the normal range of non-faulty fluctuations.

[0176] This application's solution effectively addresses the problem of misjudgments caused by internal noise and external interference affecting diagnostic information in complex charging environments by introducing a mechanism for stability assessment of diagnostic information. Specifically, by acquiring current diagnostic information and combining it with historical diagnostic information sequences within a preset time window, the temporal evolution background of the diagnostic information can be established, thereby calculating the statistical dispersion of the current diagnostic information relative to historical trends and quantifying its fluctuation degree. Simultaneously, by acquiring the noise level of the charger's internal measurement links and the intensity of environmental electromagnetic interference in real time, non-fault-related fluctuation sources can be accurately identified and quantified. These non-fault-related fluctuation sources are then combined to form a dynamic non-fault-related fluctuation threshold, enabling the system to effectively distinguish actual fluctuations in diagnostic information from normal fluctuations caused by noise and interference. It is precisely this dual consideration based on historical data and real-time interference information that allows the system to more accurately determine whether there are genuine abnormal fluctuations in diagnostic information, avoiding false alarms or missed alarms caused by occasional or environmental factors.

[0177] Through the above technical solution, this application can significantly improve the accuracy and reliability of diagnostic information. By conducting a stability assessment of the diagnostic information, non-fault fluctuations caused by internal measurement link noise and environmental electromagnetic interference can be effectively filtered out, thereby avoiding providing users with incorrect diagnostic information or frequent false alarms. This allows users to receive more accurate and reliable assessments of device health status, thus improving the user experience and helping to promptly detect and address real internal charging path anomalies, ensuring charging safety and battery life.

[0178] In some embodiments described above in this application, after a stability assessment of the diagnostic information, the information is smoothed based on the assessment results. However, if the smoothing parameters are fixed, it may be difficult to simultaneously satisfy both rapid response to real abnormal changes and effective suppression of non-fault fluctuations. For example, overly aggressive smoothing may lead to a delayed response to critical anomalies, while overly conservative smoothing may fail to effectively filter out non-fault fluctuations caused by environmental noise or transient loads, thereby affecting the accuracy of the diagnostic information and the user experience.

[0179] In response, this application further proposes a smoothing process for the diagnostic information based on the aforementioned stability assessment results, including:

[0180] Based on the comparison results, it is determined whether the diagnostic information exhibits any real abnormal fluctuations.

[0181] Based on the judgment results, the parameters of the smoothing process are dynamically adjusted to quickly respond to real abnormal changes and suppress non-fault fluctuations.

[0182] Specifically, determining whether there are real abnormal fluctuations in the diagnostic information based on the comparison results refers to using the comparison results of the statistical dispersion obtained in the stability assessment and the non-fault fluctuation threshold to determine whether the change in the current diagnostic information originates from a real fault or abnormality within the equipment, or is merely caused by non-fault factors such as measurement noise, environmental interference, or normal transient loads. Real abnormal fluctuations typically manifest as persistent deviations with large amplitudes, while non-fault fluctuations are characterized by randomness, transience, or periodicity but with smaller amplitudes.

[0183] Furthermore, the aforementioned dynamic adjustment of the smoothing parameters based on the judgment result to quickly respond to real abnormal changes and suppress non-fault fluctuations means that when a real abnormal fluctuation is determined to exist, the smoothing parameters are adjusted to a smaller time constant or a lower filter order to reduce the smoothing intensity, thereby enabling the diagnostic information to reflect the actual abnormal situation more quickly. Conversely, when no real abnormal fluctuation is determined to exist, but only non-fault fluctuations exist, the smoothing parameters are adjusted to a larger time constant or a higher filter order to enhance the smoothing effect, effectively filter out noise, and avoid false alarms. For example, the smoothing parameters may include, but are not limited to, the filter cutoff frequency, time constant, weighting coefficients, or number of iterations.

[0184] The proposed solution achieves intelligent adaptive processing of diagnostic information by combining the results of stability assessment with the adjustment of smoothing parameters. When the system identifies genuine abnormal fluctuations in the diagnostic information by comparing statistical dispersion with the non-fault fluctuation threshold, it immediately adjusts the smoothing parameters, reducing the smoothing intensity. This allows abnormal information to quickly penetrate the filtering layer and be captured and presented in a timely manner. This ensures that the system can respond quickly when critical faults occur, avoiding delays in diagnosis due to over-smoothing. Conversely, when the system determines that the current fluctuation is a non-fault fluctuation, it correspondingly strengthens the smoothing parameters, increasing the filtering intensity. This effectively suppresses false fluctuations caused by noise, transient interference, etc., thereby avoiding unnecessary alarms and user annoyance. It is precisely because of this dynamic adjustment mechanism that the diagnostic information maintains high sensitivity while also possessing good anti-interference capabilities.

[0185] Through the above technical solution, this application can significantly improve the accuracy and real-time performance of diagnostic information in the adaptive charging control method of chargers. This solution avoids the difficulty of balancing response speed and noise suppression in traditional fixed-parameter smoothing processing, enabling the system to react quickly to real anomalies, ensuring charging safety and device health. Simultaneously, when no real anomalies are present, it effectively filters out interference, providing stable and reliable diagnostic information, greatly improving user experience and reducing false alarms and unnecessary concerns. Furthermore, this adaptive smoothing mechanism also helps extend battery life, optimize charging efficiency, and reduce the complexity of system maintenance.

[0186] In some preferred embodiments, it is assumed that the charger is charging a device and periodically acquiring its real-time electrical response. The system first performs a stability assessment of the diagnostic information according to the method described above, calculates the statistical dispersion between the current diagnostic information and the historical diagnostic information sequence, and compares it with a comprehensive non-fault fluctuation threshold.

[0187] Specifically, if the comparison results show that the statistical dispersion is significantly higher than the non-faulty fluctuation threshold, it indicates the presence of genuine abnormal fluctuations (e.g., obvious signs of short circuits or open circuits in the internal charging path). In this case, the system will determine that the diagnostic information contains genuine abnormal fluctuations and immediately adjust the smoothing parameters (e.g., the attenuation factor of an exponentially weighted moving average filter) from 0.9 to 0.5. This adjustment allows the diagnostic information to reflect the actual abnormalities in the internal charging path more quickly, for example, displaying abnormal voltage or current fluctuations within seconds rather than tens of seconds, thereby triggering more timely warnings or charging parameter adjustments.

[0188] Conversely, if the statistical dispersion is below or close to the non-fault-related fluctuation threshold, it indicates that the current fluctuation is mainly caused by environmental electromagnetic interference or instantaneous load fluctuations and does not constitute a true anomaly. In this case, the system will determine that the diagnostic information does not contain any true abnormal fluctuations and adjust the smoothing parameter to 0.95 to enhance the smoothing effect. For example, by increasing the attenuation factor, the system can more effectively filter out these non-fault-related fluctuations, ensuring that the diagnostic information received by the user is stable and reliable, avoiding false alarms caused by minor, harmless fluctuations, thereby increasing the user's trust in the diagnostic information.

[0189] In adaptive charging control methods for chargers, the purpose of smoothing diagnostic information is to quickly respond to real abnormal changes and suppress non-fault fluctuations. However, during its implementation, if the dynamic adjustment of smoothing parameters lacks proper control, it may lead to excessively frequent or large parameter adjustments. Such over-adjustment may cause unnecessary fluctuations in the diagnostic information itself, and in some cases, even mask the true abnormal trend, thereby reducing the reliability and stability of the diagnostic results.

[0190] In response, this application further proposes a method for dynamically adjusting the parameters of the smoothing process after determining whether the diagnostic information exhibits the true abnormal fluctuations, in order to quickly respond to true abnormal changes and suppress non-faulty fluctuations. The method specifically includes:

[0191] After determining whether the diagnostic information contains the actual abnormal fluctuations, the adjustment history of the current smoothing parameters is obtained;

[0192] Based on the adjustment history, assess the frequency and magnitude of the current parameter adjustments;

[0193] When the evaluation results show that the parameter adjustment frequency is too high or the magnitude is too large, the parameter adjustment suppression mechanism is activated; the parameter adjustment suppression mechanism includes: limiting the number of parameter adjustments within a preset time period, or limiting the maximum change of a single parameter adjustment;

[0194] Based on the determination result, under the parameter adjustment and suppression mechanism, the parameters of the smoothing process are adjusted to quickly respond to real abnormal changes and suppress non-fault fluctuations.

[0195] Specifically, "obtaining the adjustment history of the current smoothing parameters" refers to the system recording and storing the action, adjustment amount, and timestamp of each adjustment to the smoothing parameters. This historical record can be stored in the charger's internal non-volatile memory or synchronized with a cloud service. Its purpose is to provide a basis for decision-making regarding subsequent parameter adjustments. To distinguish the adjustment history of different devices or chargers, a unique identifier can be assigned to each device to be charged or each charger. When recording the adjustment action, adjustment amount, and adjustment timestamp of the smoothing parameters, the corresponding device identifier or charger identifier is also recorded. When obtaining the adjustment history, the system filters based on the identifier to obtain the adjustment history of a specific device or charger.

[0196] "Assessing the frequency and magnitude of current parameter adjustments" refers to analyzing the acquired adjustment history to calculate the number of parameter adjustments (frequency) and the average or maximum change (magnitude) of each adjustment within a specific time window. For example, the number of adjustments over the past minute, five minutes, or longer can be calculated and compared with preset thresholds.

[0197] The "parameter adjustment suppression mechanism" refers to a control strategy activated by the system to limit subsequent parameter adjustments when evaluation results indicate that parameter adjustments are too frequent or excessive. This mechanism aims to prevent the system from responding too sensitively or erratically to diagnostic information.

[0198] "The parameter adjustment suppression mechanism includes: limiting the number of parameter adjustments within a preset time period, or limiting the maximum change in a single parameter adjustment."

[0199] Specifically, it can be set that parameters can be adjusted at most once every 10 seconds, or that the maximum change in each adjustment cannot exceed a certain percentage (e.g., 5%). These limitations can be dynamically configured according to the actual application scenario and the requirements for the stability of diagnostic information.

[0200] "Adjusting the parameters for smoothing under the aforementioned parameter adjustment suppression mechanism" means that even if the system determines that there are real abnormal fluctuations requiring parameter adjustment, the adjustment must be made under the constraints of the suppression mechanism. This implies that parameter adjustment will be controlled and gradual, rather than unlimited.

[0201] This application's solution effectively addresses the potential over-adjustment or instability issues that may arise when dynamically adjusting smoothing parameters by introducing a parameter adjustment suppression mechanism. When the system determines that there are genuine abnormal fluctuations in diagnostic information, although a rapid response is required, frequent or large-scale adjustments to the smoothing parameters without restrictions may cause unnecessary fluctuations in the diagnostic information itself, or even mask the true abnormal trend, thereby reducing the reliability of the diagnosis. By acquiring and evaluating the parameter adjustment history, the system can identify potential over-adjustment behavior. Once excessively high adjustment frequency or excessively large magnitude is detected, the parameter adjustment suppression mechanism is activated, imposing restrictions on subsequent parameter adjustments. For example, limiting the number of adjustments within a specific time period or the maximum change in a single adjustment makes the parameter adjustment process more stable and controllable. Thus, even in scenarios requiring rapid response to genuine abnormal changes, the adjustment of smoothing parameters can maintain a certain degree of stability, avoiding noise introduced by parameter fluctuations themselves, thereby ensuring the accuracy and reliability of diagnostic information.

[0202] Through the above technical solution, this application can significantly improve the robustness and stability of the smooth processing of diagnostic information in the adaptive charging control method of the charger. While ensuring a rapid response to real abnormal changes, it effectively suppresses non-fault fluctuations that may be introduced due to excessive parameter adjustment, avoiding unnecessary oscillations or false alarms in the diagnostic information. This makes the diagnostic information received by the user more stable and reliable, increases the user's trust in the charger's diagnostic results, and helps to more accurately guide the user to take corresponding measures, thereby improving the overall safety of the charging system and the user experience.

[0203] In some preferred embodiments, a specific example is given below. Assume a charger is charging a device, and the system continuously diagnoses the health of the internal charging path. At some point, the system determines through stability assessment that the diagnostic information exhibits genuine abnormal fluctuations, requiring dynamic adjustment of the smoothing parameters for a faster response.

[0204] At this point, the system will first query the adjustment history of the smoothing parameter over a period of time (e.g., the past 30 seconds). If it finds that the parameter has been adjusted 5 times in the past 30 seconds, and each adjustment exceeds the preset 2% threshold, this indicates that the parameter adjustment frequency is too high and the magnitude is too large.

[0205] In this scenario, a parameter adjustment suppression mechanism will be activated. This mechanism may stipulate that the smoothing parameter can be adjusted a maximum of once within the next 10 seconds, and the maximum change in a single adjustment cannot exceed 1%. Therefore, even if the system determines that parameter adjustment is necessary again, its adjustment behavior will be subject to these restrictions; for example, only one small adjustment can be made, rather than multiple large adjustments. In this way, the system can respond to genuine anomalies while avoiding instability in the diagnostic information itself due to over-adjustment of parameters, thus providing more reliable diagnostic results.

[0206] After determining whether there are real abnormal fluctuations in the diagnostic information, the aforementioned adaptive charging control method for chargers needs to obtain the adjustment history of the current smoothing parameters in order to evaluate the frequency and magnitude of parameter adjustments and activate the parameter adjustment suppression mechanism in a timely manner. Specifically, obtaining the adjustment history of the current smoothing parameters may include the following steps:

[0207] Assign a unique identifier to each device to be charged or each charger.

[0208] When recording the adjustment action, adjustment amount, and adjustment timestamp of the parameters of the smoothing process, the corresponding device identifier or charger identifier is also recorded.

[0209] When retrieving adjustment history, the system filters based on the identifier to obtain adjustment history for a specific device or charger.

[0210] The unique identifier can be a serial number, MAC address, UUID, or any other code that can uniquely distinguish the device or charger being charged. This identifier can be preset during device manufacturing or dynamically generated and bound by the system upon initial charger connection.

[0211] Furthermore, the adjustment actions of the smoothing parameters may include operations such as increasing, decreasing, or resetting the parameters; the adjustment amount refers to the specific numerical value of the parameter change; and the adjustment timestamp records the exact time when each adjustment occurred. This information is stored in a traceable database or log system for subsequent querying and analysis.

[0212] Specifically, when retrieving adjustment history, the system retrieves all parameter adjustment records associated with the device identifier or charger identifier specified by the user or internal logic from the stored historical records. This filtering mechanism ensures that the acquired historical data is specific to the target device or charger being charged, thus avoiding data confusion and improving the accuracy of historical data analysis.

[0213] This application's solution achieves fine-grained management of parameter adjustment history by assigning a unique identifier to each device or charger to be charged and simultaneously recording these identifiers when recording the adjustment history of smoothing processing parameters. Therefore, when adjustment history is needed, it can be precisely filtered based on specific identifiers, ensuring that the acquired data is specific to the device or charger to be charged. This mechanism enables the system to accurately track the parameter adjustment behavior of each independent entity (device or charger to be charged), providing a reliable data foundation for subsequent evaluation of the frequency and magnitude of parameter adjustments. It is precisely because of the ability to obtain precise, device-specific adjustment history that the parameter adjustment suppression mechanism can make decisions based on real and detailed data, avoiding misjudgments or inappropriate suppression caused by data confounding.

[0214] Through the above technical solution, this application can achieve accurate tracking and management of the adjustment history of smoothing parameters in the adaptive charging control method of a charger. By introducing a unique identifier and associating data, the problem of potential confusion in parameter adjustment history data in scenarios with multiple devices or multiple chargers is effectively solved. Therefore, the system can accurately evaluate the adjustment frequency and magnitude of smoothing parameters for a specific device or charger, providing solid data support for the effective operation of subsequent parameter adjustment suppression mechanisms, thereby improving the stability and accuracy of diagnostic information and optimizing the adaptability of charging control.

[0215] refer to Figure 4 , Figure 4 This is a schematic diagram of a charger adaptive charging control system provided in an embodiment of the present invention, comprising:

[0216] The detection end is used to obtain electrical response reference information of the device to be charged in a healthy state;

[0217] The judgment end is used to generate an electrical detection signal and apply the electrical detection signal to the device to be charged in order to collect the electrical response of the device to be charged to the electrical detection signal;

[0218] By comparing the electrical response with the electrical response reference information, and combining the battery status information of the device to be charged and the electrical characteristic information of the charging cable, the health status judgment result of the internal charging path of the device to be charged is obtained.

[0219] The adjustment terminal is used to adjust the charging parameters based on the health status assessment results and provide diagnostic information to the user.

[0220] This system, through the collaborative operation of its detection, judgment, and adjustment ends, achieves precise assessment and adaptive charging control of the health status of the internal charging path of the charging device. Specifically, the detection end acquires the electrical response baseline information of the device to be charged, providing a reference for subsequent health status judgment. The judgment end generates and applies electrical detection signals, collects the device's real-time electrical response, and combines the baseline information, battery status, and charging cable characteristics to comprehensively judge the health status of the internal charging path. Finally, the adjustment end dynamically adjusts the charging parameters based on the judgment results and provides users with intuitive diagnostic information, thereby effectively solving the problems of low charging efficiency, poor user experience, and lack of clear fault diagnosis in existing technologies, significantly improving the intelligence and safety of charging.

[0221] The core of the charger adaptive charging control system in this application lies in achieving refined management of the charging process through the coordinated action of various functional modules.

[0222] Specifically, the detection terminal is configured to acquire electrical response reference information of the device to be charged in a healthy state. This detection terminal can be a storage module integrated within the charger, used to store reference data pre-collected at the device's factory or during the first healthy charge. In a preferred embodiment, the detection terminal can also communicate with a cloud server to obtain or update the electrical response reference information of the device to be charged from a cloud database. In some embodiments, the detection terminal may also include a communication interface for data interaction with the device to be charged to obtain reference information stored within the device itself.

[0223] The judgment terminal is configured to generate an electrical detection signal and apply it to the device to be charged to acquire the electrical response of the device to the detection signal. It then compares the electrical response with reference information, and combines this with the battery status information of the device and the electrical characteristics of the charging cable to obtain a health status assessment result for the internal charging path of the device. This judgment terminal may include a signal generator to generate various forms of electrical detection signals, such as swept-frequency sine waves or pulse signals. Simultaneously, the judgment terminal may integrate an analog-to-digital converter array for real-time acquisition of the electrical response of the device to the detection signal. Furthermore, the judgment terminal includes a processing unit, such as a microcontroller or digital signal processor, to execute algorithms such as data comparison, feature extraction, pattern recognition, or machine learning to comprehensively analyze deviations in the electrical response, battery status information, and the electrical characteristics of the charging cable, thereby accurately determining the health status of the internal charging path.

[0224] The adjustment terminal is configured to adjust charging parameters based on the health status assessment result and provide diagnostic information to the user. This adjustment terminal may include a power management unit for dynamically adjusting charging parameters such as charging voltage, charging current, and charging power based on the health status assessment result output by the assessment terminal. For example, when a minor abnormality is detected, the power management unit can appropriately reduce the charging current to protect the device. Furthermore, the adjustment terminal may also include a user interface module, such as a display driver circuit, LED indicator control circuit, or wireless communication module, for presenting diagnostic information to the user in the form of text, graphics, or indicator light status, and providing corresponding operation suggestions. For example, when a charging cable abnormality is detected, the adjustment terminal can prompt the user to check the cable connection through the user interface module.

[0225] This application's adaptive charging control system for chargers, by introducing functional modules such as detection, judgment, and adjustment terminals, achieves direct diagnosis and adaptive control of the health status of the charging path within the device, significantly surpassing the limitations of existing technologies that rely solely on feedback information from the battery management system. Traditional chargers, when faced with subtle anomalies in the charging path, often fail to accurately identify the root cause of the problem, resorting to conservative charging strategies that result in low charging efficiency and a lack of clear fault diagnosis. This application's system, however, can accurately identify anomalies in the internal charging path through electrical detection and comprehensive analysis, and dynamically adjust charging parameters accordingly, thereby optimizing charging efficiency, extending battery life, and improving charging safety. Simultaneously, the system provides users with clear diagnostic information, addressing the pain point of user confusion regarding charging issues and greatly enhancing user experience and product reliability.

[0226] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A charger adaptive charging control method, characterized in that, include: Obtain electrical response baseline information of the device to be charged in a healthy state; An electrical detection signal is generated and applied to the device to be charged to collect the electrical response of the device to the electrical detection signal; By comparing the electrical response with the electrical response reference information, and combining the battery status information of the device to be charged and the electrical characteristic information of the charging cable, the health status judgment result of the internal charging path of the device to be charged is obtained. Based on the health status assessment results, the charging parameters are adjusted, and diagnostic information is provided to the user.

2. The adaptive charging control method for a charger according to claim 1, characterized in that, The step of comparing the electrical response with the electrical response reference information, and combining the battery status information of the device to be charged and the electrical characteristic information of the charging cable, to obtain the health status judgment result of the internal charging path of the device to be charged includes: Connect the probe signal generator and analog-to-digital converter array to the internal reference loop to obtain the real-time reference response of the measurement link; The deviation of the measurement link is calculated by comparing the real-time reference response with the pre-stored ideal reference response, and a correction matrix is ​​generated. The electrical response is compensated using the correction matrix to eliminate the error in the measurement link, thereby obtaining the compensated instantaneous response data. The compensated instantaneous response data is decomposed to obtain the components of the electrical detection signal, which include linear response components, nonlinear harmonic components, environmental electromagnetic interference components, and internal transient load fluctuation components. By combining contextual information and the characteristic analysis of each component, the causal origin of each component is inferred; Based on the causal source determination result, the health status of the internal charging path of the device to be charged is determined.

3. The adaptive charging control method for a charger according to claim 1, characterized in that, The step of adjusting charging parameters based on the health status assessment result and providing diagnostic information to the user includes: After the charger establishes a charging connection with the device to be charged, the real-time electrical response of the device to be charged is periodically acquired. Based on the real-time electrical response, the dynamic change trend of the internal charging path anomaly of the device to be charged is identified; Obtain instantaneous load information of other internal functions of the device to be charged; Determine the user's current usage scenario; Based on the abnormal dynamic change trend, the instantaneous load information, and the usage scenario, adjust the charging parameters and update the diagnostic information.

4. The adaptive charging control method for a charger according to claim 3, characterized in that, The step of adjusting the charging parameters based on the abnormal dynamic change trend, the instantaneous load information, and the usage scenario includes: Assess the severity of the internal charging path anomaly to obtain anomaly severity information; Adjust the priority of the charging efficiency according to the usage scenario; Based on the severity of the anomaly, the instantaneous load information, and the priority of the charging efficiency, the impact on battery life and charging safety is estimated, and the compensation voltage increment is calculated. The internal temperature of the device to be charged is monitored in real time to obtain internal temperature information; Based on the internal temperature information, adjust the rising slope or frequency of the charging current; The output waveform of the charging current is adjusted based on the instantaneous load information.

5. The adaptive charging control method for a charger according to claim 3, characterized in that, The step of updating the diagnostic information based on the dynamic trend of the anomaly, the instantaneous load information, and the usage scenario includes: A consistency check is performed on the dynamic change trend of the anomaly, the instantaneous load information, and the usage scenario information; Based on the consistency check results, weights are assigned to the dynamic change trend of the anomaly, the instantaneous load information, and the usage scenario information; The stability of the diagnostic information was assessed. Based on the results of the stability assessment, the diagnostic information is smoothed. The diagnostic information is output to the user.

6. The adaptive charging control method for a charger according to claim 5, characterized in that, The stability assessment of the diagnostic information includes: Obtain current diagnostic information; Obtain the historical diagnostic information sequence within a preset time window; Calculate the statistical dispersion between the current diagnostic information and the historical diagnostic information sequence; Obtain the real-time noise level of the internal measurement link of the charger; Obtain the real-time intensity of environmental electromagnetic interference; Based on the real-time noise level and the real-time intensity, a comprehensive non-fault fluctuation threshold is obtained; Compare the statistical dispersion with the non-fault fluctuation threshold; Based on the comparison results, it is determined whether the diagnostic information exhibits any real abnormal fluctuations.

7. The adaptive charging control method for a charger according to claim 6, characterized in that, The smoothing process of the diagnostic information based on the stability assessment results includes: Based on the comparison results, it is determined whether the diagnostic information exhibits any real abnormal fluctuations. Based on the judgment results, the parameters of the smoothing process are dynamically adjusted to quickly respond to real abnormal changes and suppress non-fault fluctuations.

8. The adaptive charging control method for a charger according to claim 7, characterized in that, The step of dynamically adjusting the smoothing parameters based on the determination result to quickly respond to real abnormal changes and suppress non-fault fluctuations includes: After determining whether the diagnostic information contains the actual abnormal fluctuations, the adjustment history of the current smoothing parameters is obtained; Based on the adjustment history, assess the frequency and magnitude of the current parameter adjustments; When the evaluation results show that the parameter adjustment frequency is too high or the magnitude is too large, the parameter adjustment suppression mechanism is activated. The parameter adjustment suppression mechanism includes: limiting the number of parameter adjustments within a preset time period, or limiting the maximum change in a single parameter adjustment; Based on the determination result, under the parameter adjustment and suppression mechanism, the parameters of the smoothing process are adjusted to quickly respond to real abnormal changes and suppress non-fault fluctuations.

9. The adaptive charging control method for a charger according to claim 8, characterized in that, The step of obtaining the adjustment history of the current smoothing parameters after determining whether the diagnostic information contains the actual abnormal fluctuations includes: Assign a unique identifier to each device to be charged or each charger. When recording the adjustment action, adjustment amount, and adjustment timestamp of the parameters of the smoothing process, the corresponding device identifier or charger identifier is also recorded. When retrieving adjustment history, the system filters based on the identifier to obtain adjustment history for a specific device or charger.

10. A charger adaptive charging control system, characterized in that, include: The detection end is used to obtain electrical response reference information of the device to be charged in a healthy state; The judgment end is used to generate an electrical detection signal and apply the electrical detection signal to the device to be charged in order to collect the electrical response of the device to be charged to the electrical detection signal; By comparing the electrical response with the electrical response reference information, and combining the battery status information of the device to be charged and the electrical characteristic information of the charging cable, the health status judgment result of the internal charging path of the device to be charged is obtained. The adjustment terminal is used to adjust the charging parameters based on the health status assessment results and provide diagnostic information to the user.

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