Intelligent terminal data line voltage dynamic adjustment control system

Abstract

The present application relates to the technical field of intelligent terminal data line power supply control, in particular to an intelligent terminal data line voltage dynamic regulation control system. The method comprises collecting running state parameters of the data line in the working process, processing the collected data to form effective physical quantity data reflecting the working characteristics of the data line; based on the effective physical quantity data, reverse modeling of the data line manufacturing characteristics is performed to obtain characteristic parameters representing individual differences of the data line; based on the characteristic parameters, a high-frequency loss characteristic model is constructed to obtain individualized coupling impedance data of the data line under the current working state, and the system output voltage is dynamically regulated in combination with the running state parameters to compensate for the data line loss voltage drop and improve the load end power supply stability; the present application adapts to the performance changes of the data line in the use process through the parameter iterative calibration mechanism, and triggers the corresponding protection strategy when detecting abnormal state to ensure the system operation safety and regulation accuracy.

Description

Technical Field

[0001] This invention relates to the field of power supply control for smart terminal data lines, specifically to a dynamic voltage adjustment and control system for smart terminal data lines. Background Technology

[0002] With the widespread adoption of smart terminal devices, data cables are not only used for data transmission but also for terminal power supply and charging control. In practical applications, factors such as the length of the data cable, the contact status of the interface, load changes, and the electromagnetic environment continuously affect its transmission characteristics, easily causing voltage attenuation and power supply instability. Existing data cable power supply control schemes mostly adopt fixed voltage output or adjustment methods based on simple current detection, which are difficult to reflect the real-time changes in the operating status of the data cable, resulting in inaccurate line loss voltage drop compensation and affecting the stability and energy efficiency of the terminal power supply.

[0003] Some technologies regulate voltage through fast charging protocols or power negotiation mechanisms, but their control strategies mainly rely on preset parameters on the terminal or power supply side, lacking the ability to perceive the characteristics and state changes of the data line itself. In scenarios where data transmission and power supply are carried out in parallel, it is difficult to balance voltage stability and transmission reliability. At the same time, existing solutions mostly respond passively to identify and protect against abnormal states, making it difficult to take timely and graded control measures, which poses safety hazards.

[0004] To address this, a dynamic voltage adjustment and control system for intelligent terminal data lines is proposed. Summary of the Invention

[0005] This invention provides a dynamic voltage adjustment and control system for intelligent terminal data lines. It collects and processes the operating status parameters of the data line during operation to form effective physical quantity data reflecting the actual working characteristics of the data line. Based on this effective physical quantity data, it performs reverse analysis on the manufacturing-related characteristics of the data line to obtain key parameters describing the individual differences of the data line. On this basis, it constructs a loss characteristic model matching the operating state of the data line, calculates the line loss characteristics of the data line under the current operating conditions, and dynamically adjusts the system output voltage in conjunction with the operating status parameters to compensate for the line loss voltage drop of the data line. This improves the stability and consistency of power supply to the load end and enhances the reliability of the system in complex usage scenarios.

[0006] To achieve the above objectives, the present invention provides the following technical solution: Operation status acquisition module: detects the data cable connection status and identifies the cable specification, collects data reflecting the operation status of the data cable during operation, obtains operation status parameters, filters and verifies the consistency of the collected data, and forms valid physical quantity data; Manufacturing characteristic inverse modeling module: Based on the effective physical quantity data, the manufacturing-related characteristics of the data line are inversely deduced through multi-temperature point impedance calculation, and the characterization parameters are obtained after validity verification; High-frequency loss characteristic modeling module: Receives the characterization parameters, constructs a high-frequency loss model based on the characterization parameters, performs joint calculations on the loss characteristics of the data line in the working state, and obtains personalized coupling impedance data; Line loss calculation and voltage regulation module: Based on the personalized coupling impedance data and the operating status parameters, calculate the system output voltage, dynamically adjust the system output voltage, compensate for the line loss voltage drop of the data line, and record the adjustment results; Parameter Iteration Calibration Module: Iteratively updates the characterization parameters and the model parameters formed in the construction of the high-frequency loss model, and constructs a data line aging factor model based on the incremental correction algorithm; Anomaly identification and hierarchical protection module: It is used to monitor the operating status parameters, characterization parameters and adjustment results generated during system operation, trigger corresponding protection strategies based on the monitoring results, and re-trigger the dynamic line loss compensation process after the anomaly is resolved.

[0007] Preferably, the step of forming effective physical quantity data includes: Perform a sampling integrity check on the operating status parameters and remove data with missing samples; Perform smoothing filtering on the retained data to reduce sampling noise interference; Based on the physical correlation between the operating status parameters, the filtered data is checked for consistency, and the data that meets the consistency conditions is retained to form valid physical quantity data.

[0008] Preferably, the step of obtaining the characterization parameters includes: Based on effective physical quantity data, calculate the impedance value of the data line under different operating temperature conditions; The impedance variation relationship under different temperature conditions is fitted and calculated; Based on the fitting calculation results, the manufacturing-related characteristics of the data line are deduced, and after reasonableness verification, characterization parameters are formed.

[0009] Preferably, the step of constructing the high-frequency loss model includes: Using characterization parameters as model input variables, a parameterized model structure is established to describe the loss variation of data lines under high-frequency operating conditions. The characterization parameters are introduced into the parameterized model structure to form a high-frequency loss model.

[0010] Preferably, the step of obtaining personalized coupling impedance data includes: Substitute the operating state parameters and characterization parameters into the high-frequency loss model to calculate the equivalent loss value of the data line under the current operating state. Based on the correspondence between the equivalent loss value and the equivalent impedance, the coupling impedance of the data line is calculated; The calculated coupling impedance is used as personalized coupling impedance data.

[0011] Preferably, the step of compensating for data line loss voltage drop includes: Based on the aforementioned operating status parameters and personalized coupling impedance data, a line loss estimation algorithm is used to calculate the line loss voltage drop corresponding to the data line in the current operating state. Using the line loss voltage drop as the compensation object, it is introduced into the voltage regulation process, and a voltage compensation algorithm is used to generate an output voltage regulation command to offset the line loss voltage drop. Based on the output voltage adjustment command, a closed-loop control algorithm is used to dynamically adjust the power supply output, so that the voltage deviation at the load end is compensated for the voltage drop due to the data line loss.

[0012] Preferably, the step of constructing the data line aging factor model includes: Record a historical sequence of characterization parameters and coupling impedance data over multiple working cycles; Calculate the parameter changes between adjacent work cycles, and construct an incremental update function based on the parameter changes; By iteratively calculating the incremental update function, an aging factor model is formed to reflect the performance evolution trend of the data line.

[0013] Preferably, the step of triggering the corresponding protection strategy includes: The system continuously samples the operating status parameters, characterization parameters, and adjustment results generated during system operation, and constructs a parameter status set for anomaly detection. Based on the parameter state set, an anomaly identification algorithm is used to analyze the parameter change trend to determine the anomaly occurrence state and the severity level of the anomaly; Based on the severity level of the anomaly, the protection control logic corresponding to the severity level of the anomaly is invoked to generate a protection strategy execution instruction; The protection strategy execution instructions are executed to restrict, adjust, or interrupt the system's adjustment behavior, and the line loss dynamic compensation process is retried after the abnormal state is cleared.

[0014] The beneficial effects of this invention are: By continuously collecting and processing the voltage, current, temperature, and operating status changes of smart terminal data cables during power supply and data transmission, an effective physical quantity data system reflecting the true operating characteristics of the data cables is constructed. Based on this, the manufacturing-related characteristics of the data cables are reverse-engineered to obtain key parameters for characterizing the individual differences of different data cables. Furthermore, a loss characteristic model matching the high-frequency operating state is established to achieve a refined description of the line loss behavior of the data cables in actual use scenarios. Based on the calculation results of the model, the system can adaptively and dynamically adjust the output voltage and compensate for the line loss voltage drop caused by changes in line resistance, loss, and load fluctuations in real time, thereby effectively improving the stability and consistency of the power supply voltage at the load end. Through comprehensive analysis of operating status parameters, model parameters, and adjustment results, abnormal operating conditions can be identified and corresponding graded protection strategies can be triggered to reduce the power supply risks caused by data cable aging, performance degradation, and abnormal use, and improve the safety, reliability, and adaptability of the smart terminal power supply system under complex environments and long-term use conditions. Attached Figure Description

[0015] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a structural diagram of the intelligent terminal data line voltage dynamic adjustment control system provided by the present invention. Detailed Implementation

[0016] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0017] Intelligent terminal data line voltage dynamic adjustment control system, such as Figure 1 As shown, it includes: Operation status acquisition module: detects the data cable connection status and identifies the cable specification, collects data reflecting the operation status of the data cable during operation, obtains operation status parameters, filters and verifies the consistency of the collected data, and forms valid physical quantity data; Furthermore, the step of forming effective physical quantity data includes: Perform a sampling integrity check on the operating status parameters and remove data with missing samples; Perform smoothing filtering on the retained data to reduce sampling noise interference; Based on the physical correlation between the operating status parameters, the filtered data is checked for consistency, and the data that meets the consistency conditions is retained to form valid physical quantity data.

[0018] Specifically, the operation status acquisition module monitors the level changes of the charging interface of the smart terminal in real time to detect whether a data cable is connected. When a data cable connection signal is detected, it immediately performs a fast charging protocol handshake with the data cable through the data communication pin of the charging interface. Through protocol interaction, it identifies the basic specifications of the data cable, including basic information such as the wire diameter, number of conductor strands, and shielding type, providing a basic reference for subsequent parameter calibration and model adaptation.

[0019] After the data cable is successfully connected and enters the working state, the module synchronously starts multi-channel sampling to continuously collect four types of core parameters reflecting the operating status of the data cable, ensuring the comprehensiveness and real-time nature of parameter collection. The specific parameters and methods of collection are as follows: The terminal uses a built-in current sampling resistor to collect the load current flowing through the data line in real time. The sampling frequency is adapted to high-frequency fast charging conditions (not less than 1MHz) to ensure that dynamic changes in current are captured. Differential sampling is used to collect the real-time voltage difference between the two ends of the data line, accurately reflecting the voltage loss of the data line. The protocol parsing module extracts the operating frequency of the current fast charging mode. If the protocol does not explicitly specify the operating frequency, the voltage ripple of the data line is collected and the ripple frequency is analyzed to determine the fast charging operating frequency. The thermistor built into the charging interface collects the temperature at the connection between the data line and the terminal interface in real time, indirectly reflecting the overall temperature rise of the data line and preventing excessive temperature from affecting parameter accuracy and system safety.

[0020] Due to electromagnetic interference and sampling errors in high-frequency fast charging scenarios, the collected raw data may be distorted or incomplete, requiring three-step preprocessing: Each set of raw data (load current, voltage drop, frequency, temperature) is verified, and incomplete data caused by missing or interrupted sampling is removed to ensure that each set of data contains four core parameters. The moving average filtering algorithm is used to filter the retained complete data to reduce the impact of high-frequency electromagnetic interference and sampling noise on data accuracy. The core logic of the filtering is to offset the random error of a single sampling point by taking the average of multiple consecutive sampling points. Based on the laws of electrical physics (voltage drop across the data line = load current × data line impedance), and combined with the basic specifications of the data line identified in the first step, the reference impedance of the same specification data line is determined. Each set of filtered data is verified to determine the matching between voltage drop, current, and impedance. Data with deviations exceeding the preset threshold is removed, and finally, effective physical quantity data is formed to provide accurate input for the subsequent manufacturing characteristic reverse modeling module.

[0021] Manufacturing characteristic inverse modeling module: Based on the effective physical quantity data, the manufacturing-related characteristics of the data line are inversely deduced through multi-temperature point impedance calculation, and the characterization parameters are obtained after validity verification; Furthermore, the step of obtaining the characterization parameters includes: Based on effective physical quantity data, calculate the impedance value of the data line under different operating temperature conditions; The impedance variation relationship under different temperature conditions is fitted and calculated; Based on the fitting calculation results, the manufacturing-related characteristics of the data line are deduced, and after reasonableness verification, characterization parameters are formed.

[0022] Specifically, based on the aforementioned effective physical quantity data, parameter groups under different temperature conditions are selected, and three typical temperature points (usually 25℃, 40℃, and 55℃, covering the typical temperature rise range of data lines in fast charging scenarios) are chosen. The actual impedance value of the data line at each temperature point is calculated. The core logic of impedance calculation is "impedance = voltage / current", that is, the actual impedance at each temperature point is obtained by dividing the effective voltage drop across the data line by the corresponding effective load current, ensuring the accuracy of impedance calculation and providing basic data for subsequent fitting calculations.

[0023] Since manufacturing process variations in data cables (such as conductor stranding density, insulation layer thickness, and conductor resistivity variations) directly affect their impedance characteristics, and impedance changes with temperature, it is necessary to establish a correlation between "impedance variation - manufacturing process variation - temperature" through fitting calculations, thereby inferring the manufacturing process variation. The specific operation is as follows: Calculate the difference between the actual impedance and the reference impedance (i.e., impedance deviation) at each temperature point. The reference impedance is the impedance value of a standard data line of the same specification and without process deviation at the corresponding temperature. A multivariate linear fitting calculation was performed on the impedance deviation and temperature difference (the difference between the current temperature and the reference temperature of 25°C) at all temperature points to construct the inverse modeling formula for manufacturing characteristics: ; in, For a certain temperature Below, the difference between the actual impedance of the data line and the reference impedance (i.e., impedance deviation). The conductor stranding density deviation reflects the process error during the stranding of the data cable conductors. A positive value indicates that the stranding density is higher than the standard value, and a negative value indicates that it is lower than the standard value. This represents the insulation layer thickness deviation, reflecting the process error during the production of the data cable insulation layer. A positive value indicates that the insulation layer thickness is higher than the standard value, while a negative value indicates that it is lower than the standard value. This represents the resistivity deviation of the conductor, reflecting the resistivity deviation of the data cable conductor material. A positive value indicates that the resistivity is higher than the standard value, and a negative value indicates that the resistivity is lower than the standard value. The fitting coefficients were calibrated through simulation experiments using a large number of standard data lines, and each corresponds to the weight of the influence of the three characterization parameters on the impedance deviation. The temperature fitting coefficient is used to calibrate the weighting of the effect of temperature changes on impedance deviation. The reference temperature is fixed at 25℃ (normal room temperature, for easy and uniform calibration). The inverse modeling formula for manufacturing characteristics is solved by the least squares method to obtain the specific values ​​of the three characterization parameters (conductor strand density deviation, insulation layer thickness deviation, and conductor resistivity deviation). Subsequently, the validity of the three characterization parameters is verified. In combination with the process standards of the data cable manufacturing industry, a reasonable range for each characterization parameter is preset, and outliers exceeding the reasonable range are eliminated. Finally, the valid characterization parameters are output.

[0024] High-frequency loss characteristic modeling module: Receives the characterization parameters, constructs a high-frequency loss model based on the characterization parameters, performs joint calculations on the loss characteristics of the data line in the working state, and obtains personalized coupling impedance data; Furthermore, the step of constructing the high-frequency loss model includes: Using characterization parameters as model input variables, a parameterized model structure is established to describe the loss variation of data lines under high-frequency operating conditions. The characterization parameters are introduced into the parameterized model structure to form a high-frequency loss model.

[0025] Specifically, the model identifies three core types of losses in data lines under high-frequency operating conditions: skin effect loss, proximity effect loss, and dielectric loss. The core input variables of the model are fast charging operating frequency, data line operating temperature, and characterization parameters (conductor strand density deviation, insulation layer thickness deviation, and conductor resistivity deviation). The output variable is the total high-frequency loss of the data line under the current operating conditions. A parameterized framework for a high-frequency loss characteristic model is established, incorporating all core input variables into the framework, clarifying the logic of the relationship between model input and output, abandoning the traditional separate modeling approach, realizing the integrated calculation of three types of losses, and laying the foundation for loss quantification in subsequent impedance calculations.

[0026] For the three types of loss items, corresponding characterization parameter correction factors are embedded respectively, so that the model can adapt to data lines with different manufacturing process deviations and realize personalized modeling. The calculation results of each loss item are precisely matched with the actual manufacturing characteristics of the data cable to ensure that the loss data output by the model is personalized and practical. Multiple sets of simulation and measured data from standard data cables of the same specification were used to calibrate the benchmark parameters (such as DC resistance, benchmark capacitance, and proximity effect coefficient) in the model. The calibrated model was then substituted into data cable loss calculations under different process deviations and high-frequency operating conditions to verify the deviation between the model's calculation results and the measured results, ensuring that the deviation was within a preset accuracy threshold. This resulted in a final, directly usable high-frequency loss characteristic model. The formula for the high-frequency loss characteristic model is as follows: ; Total high-frequency loss; For skin effect loss, This is due to proximity effect loss. Dielectric loss; For fast charging operating frequency, The operating temperature of the data cable. For conductor strand density deviation, This is due to the thickness deviation of the insulation layer. For conductor resistivity deviation, For the set of characterization parameters { , , }; Furthermore, the step of obtaining personalized coupling impedance data includes: Substitute the operating state parameters and characterization parameters into the high-frequency loss model to calculate the equivalent loss value of the data line under the current operating state. Based on the correspondence between the equivalent loss value and the equivalent impedance, the coupling impedance of the data line is calculated; The calculated coupling impedance is used as personalized coupling impedance data.

[0027] Specifically, extract the total high-frequency loss. By combining the load current collected in real time by the operating status acquisition module, and based on the physical formula "loss = current squared × resistance", the resistive component of the coupling impedance is deduced. Simultaneously, based on the aforementioned characterization parameter correction factor, conductor strand density deviation and conductor resistivity deviation are embedded into the calculation process. The DC resistance is individually corrected, and additional resistance due to skin and proximity effects under high-frequency operating conditions is superimposed to obtain a personalized coupling resistance component that fits the actual manufacturing characteristics and operating conditions of the data cable, ensuring that the resistance component accurately corresponds to the model loss data.

[0028] Based on the aforementioned characterization parameter correction factor, characterization parameter correction factors are introduced for the reference inductance and reference capacitance of the data line respectively; combined with the fast charging operating frequency, the corrected inductive reactance and capacitive reactance are calculated respectively, and then the personalized coupling reactance component is obtained by subtracting the capacitive reactance from the inductive reactance, ensuring that the reactance component matches the actual manufacturing characteristics of the data line. The calculated personalized coupling resistance component is taken as the real part of the complex number, and the personalized coupling reactance component is taken as the imaginary part of the complex number. The two are then fused in complex form to obtain the complete personalized coupling impedance data: ; in, For personalized coupling impedance data, The reference DC resistor for the data line. This is a conductor strand density correction factor. The permeability of free space, The number of strands in the conductor, For the diameter of a single conductor, The twisting pitch, This is the proximity effect coefficient. The conductor's reference resistivity, For reference inductor, As a reference capacitor, It is the imaginary unit.

[0029] Line loss calculation and voltage regulation module: Based on the personalized coupling impedance data and the operating status parameters, calculate the system output voltage, dynamically adjust the system output voltage, compensate for the line loss voltage drop of the data line, and record the adjustment results; Furthermore, the step of compensating for the voltage drop of the data line loss includes: Based on the aforementioned operating status parameters and personalized coupling impedance data, a line loss estimation algorithm is used to calculate the line loss voltage drop corresponding to the data line in the current operating state. Using the line loss voltage drop as the compensation object, it is introduced into the voltage regulation process, and a voltage compensation algorithm is used to generate an output voltage regulation command to offset the line loss voltage drop. Based on the output voltage adjustment command, a closed-loop control algorithm is used to dynamically adjust the power supply output, so that the voltage deviation at the load end is compensated for the voltage drop due to the data line loss.

[0030] Specifically, the system acquires the personalized coupling impedance data and the operating status parameters, and calculates the line loss voltage drop generated by the data line under the current operating state based on the personalized coupling impedance data and the operating status parameters. In this calculation process, the equivalent impedance characteristics of the data line are coupled with the operating status parameters so that the calculation result of the line loss voltage drop can be dynamically adjusted according to the individual differences of the data line and the changes in the operating state, thereby avoiding the use of fixed cable parameters or empirical compensation values ​​for line loss estimation. After calculating the line loss voltage drop, the system generates a voltage regulation amount to offset the line loss voltage drop based on the calculation results. The system then dynamically adjusts the output voltage based on this voltage regulation amount, compensating for voltage variations after data transmission and thus improving the stability of the power supply voltage at the load end. The system records the regulation amount and results generated during the voltage regulation process to form recorded data. This recorded data supports subsequent parameter iteration calibration and operational status analysis, enabling the system to maintain regulation accuracy under long-term data line use and changing operating conditions.

[0031] Parameter Iteration Calibration Module: Iteratively updates the characterization parameters and the model parameters formed in the construction of the high-frequency loss model, and constructs a data line aging factor model based on the incremental correction algorithm; Furthermore, the step of constructing the data line aging factor model includes: Record a historical sequence of characterization parameters and coupling impedance data over multiple working cycles; Calculate the parameter changes between adjacent work cycles, and construct an incremental update function based on the parameter changes; By iteratively calculating the incremental update function, an aging factor model is formed to reflect the performance evolution trend of the data line.

[0032] Specifically, during normal system operation, the parameter iteration calibration module continuously collects and stores historical data of relevant parameters according to a preset iteration cycle, forming a complete historical parameter sequence; at the same time, it records aging-related data such as the cumulative usage time and number of insertions and removals of the data line, providing data support for the construction of the aging factor evolution model.

[0033] Based on the historical parameter sequence, the parameter changes within two adjacent iteration cycles are calculated, including changes in the characterizing parameters and changes in the personalized coupling impedance. The correlation between parameter changes and data line aging-related data is analyzed, and an incremental update function is constructed. Through multiple iterations of the incremental update function, an aging factor evolution model is constructed. This model quantifies the degree of aging of the data line at different times, transforming the aging degree into a calculable aging factor. This factor is then embedded with a high-frequency loss model and a personalized coupling impedance formula to compensate for aging deviations. The aging factor model is as follows: ; in, for The aging factor of the data cable is used to characterize the degree of aging of the data cable. The larger the aging factor, the more serious the aging of the data cable. This refers to the cumulative usage time of the data cable. , This is the aging rate coefficient. To characterize the number of parameters, The aging weights for each characterization parameter, for The first moment Each characterization parameter value.

[0034] The constructed aging factor model is embedded into the high-frequency loss characteristic model and the personalized coupling impedance fusion formula, according to... Aging factors over time The system updates the fitting coefficients in the high-frequency loss model and the baseline parameters in the coupling impedance formula. At the same time, based on the incremental update function, it updates the values ​​of the characterization parameters to offset the parameter drift caused by the aging of the data line. After each iteration, the updated parameters are stored in the system as the basis for the next modeling and adjustment, ensuring that the system adjustment accuracy can adapt to the aging process of the data line. Even if the data line has been used for a long time, the adjustment accuracy can still be maintained within the preset range.

[0035] Anomaly identification and hierarchical protection module: It is used to monitor the operating status parameters, characterization parameters and adjustment results generated during system operation, trigger corresponding protection strategies based on the monitoring results, and re-trigger the dynamic line loss compensation process after the anomaly is resolved.

[0036] Furthermore, the step of triggering the corresponding protection strategy includes: The system continuously samples the operating status parameters, characterization parameters, and adjustment results generated during system operation, and constructs a parameter status set for anomaly detection. Based on the parameter state set, an anomaly identification algorithm is used to analyze the parameter change trend to determine the anomaly occurrence state and the severity level of the anomaly; Based on the severity level of the anomaly, the protection control logic corresponding to the severity level of the anomaly is invoked to generate a protection strategy execution instruction; The protection strategy execution instructions are executed to restrict, adjust, or interrupt the system's adjustment behavior, and the line loss dynamic compensation process is retried after the abnormal state is cleared.

[0037] Specifically, the operating status parameters, effective characterization parameters, personalized coupling impedance data, and voltage regulation results are monitored and integrated to construct an anomaly discrimination parameter status set; Preset safety thresholds for each monitoring parameter (such as cable interface temperature safety threshold ≤ 85℃, characterization parameter reasonable threshold ± 10%, load current safety threshold ≤ 10A, etc.). If a parameter exceeds the preset safety threshold, it is judged as abnormal. Monitor the rate of change of parameters. If a parameter changes drastically in a short period of time (such as a sudden rise in temperature or a sudden change in current), it is judged as abnormal even if it does not exceed the safety threshold. Based on the scope and severity of the anomalies, they are classified into three levels, as follows: Minor anomalies: slight distortion of a single parameter (such as small fluctuations in sampled data) does not affect the normal adjustment of the system and poses no safety risks; Moderate abnormality: A single parameter exceeds the safety threshold, or multiple parameters show slight abnormalities, affecting the adjustment accuracy, but there is no direct safety hazard; Serious anomaly: Multiple parameters exceed safety thresholds, or extreme conditions such as short circuits or overheating occur, posing a safety hazard and potentially causing damage to the system or terminal; If a minor anomaly is detected, immediately switch to the backup sampling channel to recollect data, remodel and adjust without interrupting fast charging, thus ensuring user experience. If the problem is determined to be a moderate anomaly, immediately reduce the fast charging power to a safe range, adjust the voltage regulation coefficient to reduce line loss, and send a prompt signal to the smart terminal to remind the user to check the data cable. If a serious abnormality is detected, the charging circuit will be immediately cut off, fast charging output will be stopped, and a protection prompt will be triggered on the smart terminal to avoid safety hazards. The system continuously monitors changes in abnormal operating conditions. If all abnormal parameters are found to have returned to the safe threshold range and the parameter change rate tends to stabilize, the abnormality is determined to be resolved. Subsequently, the entire voltage dynamic adjustment process is automatically restarted to restore the system's normal fast charging voltage regulation function and ensure that the system can recover automatically.

[0038] Example 2 This embodiment aims to improve the accuracy of data line loss compensation and ensure the stability of load voltage during the fast charging process of smart terminals. Through the smart terminal data line voltage dynamic adjustment and control system proposed in this invention, precise line loss calculation and dynamic closed-loop adjustment of power supply voltage are achieved.

[0039] In a winter outdoor environment at -5℃, the user connects their phone to one end of a data cable and the other end to a power bank's fast charging port. Then, the power bank is turned on and the phone is in fast charging mode. The system automatically activates and enters working mode, automatically initiating data acquisition to capture in real-time the load current flowing through the data cable, the data cable surface temperature, the outdoor ambient temperature, and the fast charging operating frequency. Simultaneously, it accurately captures the impact of low temperatures on the data cable. Based on the collected low-temperature data, the system automatically adapts to the changes in data cable performance caused by the low-temperature environment, specifically compensating for transmission loss deviations caused by low temperatures. It quickly completes relevant data calculations and generates customized adaptation data for the current outdoor low-temperature conditions. Based on this customized adaptation data, the system automatically and accurately calculates the actual line loss voltage drop generated during data cable transmission under the current low-temperature environment, ensuring that the calculation results accurately reflect the current operating state of the data cable and avoiding deviations in line loss calculations due to low temperatures.

[0040] The system combines the phone's own fast charging requirements to determine the target voltage that the phone can achieve stable fast charging. Then, it adds the calculated line loss voltage drop to the target voltage to accurately calculate the target voltage value that the power bank needs to output, ensuring that the fast charging power is transmitted in full. The system automatically adjusts the power bank's fast charging output voltage to accurately offset the additional line loss caused by low temperature environment. At the same time, it monitors the actual charging voltage of the phone in real time and continuously compares the actual voltage with the preset target voltage to check for any deviations.

[0041] If the system detects that the actual charging voltage on the phone deviates from the target voltage beyond the preset range, it will automatically recalculate the relevant data, fine-tune the output voltage of the power bank, and repeat the monitoring, calculation, and adjustment actions until the charging voltage on the phone stabilizes within the target range. After adjustment, the system will maintain a stable output state to ensure that the phone can achieve normal fast charging in an outdoor environment of -5℃, without any issues such as no response or sudden drop in speed.

[0042] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A dynamic voltage adjustment and control system for intelligent terminal data lines, characterized in that, include: Operation status acquisition module: detects the data cable connection status and identifies the cable specification, collects data reflecting the operation status of the data cable during operation, obtains operation status parameters, filters and verifies the consistency of the collected data, and forms valid physical quantity data; Manufacturing characteristic inverse modeling module: Based on the effective physical quantity data, the manufacturing-related characteristics of the data line are inversely deduced through multi-temperature point impedance calculation, and the characterization parameters are obtained after validity verification; High-frequency loss characteristic modeling module: Receives the characterization parameters, constructs a high-frequency loss model based on the characterization parameters, performs joint calculations on the loss characteristics of the data line in the working state, and obtains personalized coupling impedance data; Line loss calculation and voltage regulation module: Based on the personalized coupling impedance data and the operating status parameters, calculate the system output voltage, dynamically adjust the system output voltage, compensate for the line loss voltage drop of the data line, and record the adjustment results; Parameter Iteration Calibration Module: Iteratively updates the characterization parameters and the model parameters formed in the construction of the high-frequency loss model, and constructs a data line aging factor model based on the incremental correction algorithm; Anomaly identification and hierarchical protection module: It is used to monitor the operating status parameters, characterization parameters and adjustment results generated during system operation, trigger corresponding protection strategies based on the monitoring results, and re-trigger the dynamic line loss compensation process after the anomaly is resolved.

2. The intelligent terminal data line voltage dynamic adjustment control system according to claim 1, characterized in that, In the operational status acquisition module, the steps for generating valid physical quantity data include: Perform a sampling integrity check on the operating status parameters and remove data with missing samples; Perform smoothing filtering on the retained data to reduce sampling noise interference; Based on the physical correlation between the operating status parameters, the filtered data is checked for consistency, and the data that meets the consistency conditions is retained to form valid physical quantity data.

3. The intelligent terminal data line voltage dynamic adjustment control system according to claim 1, characterized in that, In the manufacturing characteristic inverse modeling module, the step of obtaining the characterization parameters includes: Based on effective physical quantity data, calculate the impedance value of the data line under different operating temperature conditions; The impedance variation relationship under different temperature conditions is fitted and calculated; Based on the fitting calculation results, the manufacturing-related characteristics of the data line are deduced, and after reasonableness verification, characterization parameters are formed.

4. The intelligent terminal data line voltage dynamic adjustment control system according to claim 1, characterized in that, In the high-frequency loss characteristic modeling module, the steps for constructing the high-frequency loss model include: Using characterization parameters as model input variables, a parameterized model structure is established to describe the loss variation of data lines under high-frequency operating conditions. The characterization parameters are introduced into the parameterized model structure to form a high-frequency loss model.

5. The intelligent terminal data line voltage dynamic adjustment control system according to claim 1, characterized in that, In the high-frequency loss characteristic modeling module, the step of obtaining personalized coupling impedance data includes: Substitute the operating state parameters and characterization parameters into the high-frequency loss model to calculate the equivalent loss value of the data line under the current operating state. Based on the correspondence between the equivalent loss value and the equivalent impedance, the coupling impedance of the data line is calculated; The calculated coupling impedance is used as personalized coupling impedance data.

6. The intelligent terminal data line voltage dynamic adjustment control system according to claim 1, characterized in that, In the line loss calculation and voltage regulation module, the step of compensating for the line loss voltage drop of the data line includes: Based on the aforementioned operating status parameters and personalized coupling impedance data, a line loss estimation algorithm is used to calculate the line loss voltage drop corresponding to the data line in the current operating state. Using the line loss voltage drop as the compensation object, it is introduced into the voltage regulation process, and a voltage compensation algorithm is used to generate an output voltage regulation command to offset the line loss voltage drop. Based on the output voltage adjustment command, a closed-loop control algorithm is used to dynamically adjust the power supply output, so that the voltage deviation at the load end is compensated for the voltage drop due to the data line loss.

7. The intelligent terminal data line voltage dynamic adjustment control system according to claim 1, characterized in that, In the parameter iteration calibration module, the step of constructing the data line aging factor model includes: Record a historical sequence of characterization parameters and coupling impedance data over multiple working cycles; Calculate the parameter changes between adjacent work cycles, and construct an incremental update function based on the parameter changes; By iteratively calculating the incremental update function, an aging factor model is formed to reflect the performance evolution trend of the data line.

8. The intelligent terminal data line voltage dynamic adjustment control system according to claim 1, characterized in that, In the anomaly identification and graded protection module, the step of triggering the corresponding protection strategy includes: The system continuously samples the operating status parameters, characterization parameters, and adjustment results generated during system operation, and constructs a parameter status set for anomaly detection. Based on the parameter state set, an anomaly identification algorithm is used to analyze the parameter change trend to determine the anomaly occurrence state and the severity level of the anomaly; Based on the severity level of the anomaly, the protection control logic corresponding to the severity level of the anomaly is invoked to generate a protection strategy execution instruction; The protection strategy execution instructions are executed to restrict, adjust, or interrupt the system's adjustment behavior, and the line loss dynamic compensation process is retried after the abnormal state is cleared.

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