Safe charging method for intelligent data line

Abstract

The invention discloses a safe charging method for an intelligent data line, and relates to the technical field of data lines. Through current fluctuation detection and temperature and humidity anomaly detection, a current sensor can accurately capture instantaneous fluctuation, timely trigger a power adjustment or current limiting mechanism and prevent equipment damage caused by surge in a rapid charging scene; in addition, the temperature sensor starts a trickle charging mode when being overheated, and the humidity sensor prevents the risk of short circuit through power-off protection. Secondly, through cooperative work of a wireless communication module and a mobile phone terminal, a data line can dynamically adjust the charging power, and feed back a real-time risk score and abnormal information to a mobile phone to remind a user to take measures, so that the interaction capability between the user and charging equipment is enhanced, and the user is helped to identify potential hidden dangers in a charging environment in an early stage; besides, by analyzing the charging time, the use load and the risk triggering data of the user, future abnormal scenes can be predicted, targeted suggestions can be provided, and the user experience is improved.

Description

Technical Field

[0001] The invention relates to the technical field of data cables, and in particular to a method for safely charging an intelligent data cable. Background Art

[0002] With the widespread application of fast charging technology and the increasing demand of users for efficient and safe charging, the role of data cables in the charging process has changed from a simple power transmission medium to a key safety node in the entire charging process. The data cables on the market currently mainly have data transmission and power transmission functions, but in high-power fast charging environments, they often suffer from heat problems, which leads to performance degradation and even safety hazards. In addition, factors such as humidity and current fluctuations in the charging environment will also affect charging stability, and users may often find that their devices have been damaged by the time they discover problems. Therefore, how to make data cables the "first line of defense" for charging safety and achieve intelligent collaboration with mobile phones is one of the topics that current technology urgently needs to overcome.

[0003] Traditional data cable charging solutions mostly address the above-mentioned problems through physical structure design. Some solutions integrate overcurrent protection chips and overheating protection modules in the data cable, which can automatically disconnect the circuit when abnormal current or excessive temperature occurs; some high-end data cables have added current stabilization control functions to reduce the risk of equipment damage caused by current fluctuations through hardware adjustment; however, these methods have many limitations. Overcurrent protection is usually triggered based on a single threshold and is difficult to adapt to the needs of different charging devices; and overheating protection is mostly triggered passively. When users usually sense that the device is heating up, the battery life may have been damaged, and it is difficult to take timely measures to intervene. Therefore, a method for safe charging of smart data cables is urgently needed to solve such problems. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] The present invention provides a method for safe charging of an intelligent data cable to solve the problems that the charging scheme of a traditional data cable lacks the ability to cooperate with terminal devices such as mobile phones and cannot remind users to take measures in an early stage.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] The embodiment of the present invention provides a method for safe charging of an intelligent data cable, wherein the intelligent data cable is embedded with a sensor module, a processor module, a communication module, a circuit and power management module, and a storage module:

[0008] The sensor module is used to monitor the operating data of the charging interface in real time, including current, temperature and humidity data;

[0009] The processor module is used to collect operating data, evaluate abnormal conditions and initiate response mechanisms;

[0010] The communication module is used to transmit the monitoring results of the sensor module and the analysis results of the processor module, and interact with the mobile phone;

[0011] The circuit and power management module are responsible for supplying power to the sensor, processor and communication module;

[0012] The storage module is used to collect and record historical charging behavior data;

[0013] The method steps of intelligent data safe charging are as follows:

[0014] Step S1, continuously collecting operation data of the charging interface during charging to generate a charging data stream;

[0015] Step S2, performing time series analysis and feature extraction on the charging data stream, evaluating whether there is an abnormality, and generating a risk score, including: normal, slightly abnormal, and high risk;

[0016] Step S3, when the risk score reaches a slight abnormality or high risk, a response mechanism is triggered;

[0017] Step S4, while triggering the response mechanism, the data cable sends charging risk score information to the mobile phone through the communication module, and the mobile phone reminds the user.

[0018] As a preferred solution of the method for safe charging of a smart data cable described in the present invention, wherein: in the step S1, noise is filtered out and sudden abnormality detection is performed on the operation data to generate a charging data stream;

[0019] The charging data stream includes: current fluctuation curve, temperature trend and humidity level.

[0020] As a preferred solution of the method for safe charging of a smart data cable described in the present invention, the step of filtering out noise and detecting sudden abnormalities of the operating data to generate a charging data stream is as follows:

[0021] The sensor module collects current, temperature and humidity signals in real time, which are recorded as I(t), T(t) and H(t) respectively, where t represents the time and the sampling frequency is f s , after preliminary data formatting, a time series data set D(t) is formed:

[0022] D(t)={I(t),T(t),H(t)},t=1,2,…,N,

[0023] Where I(t) is the current value at time t, T(t) is the temperature value at time t, H(t) is the humidity value at time t, and N is the total number of sampling points, which is equal to the sampling frequency f s Multiply by the sampling time,

[0024] The signal is smoothed by a first-order low-pass filter to remove high-frequency noise interference. The filtering formula is:

[0025] I filtered (t) = α·I raw (t)+(1-α)·I filtered (t-1),

[0026] Among them, I filtered (t) is the current value at time t after filtering, I raw (t) is the original collected current value, α∈(0,1) is the smoothing factor, which controls the filter response speed.

[0027] Apply the filtering formula to the temperature and humidity signals to obtain the filtered temperature and humidity values ​​T respectively. filtered (t) and H filtered (t);

[0028] The sliding window method is used to extract the short-term mean and standard deviation of the running data, mark the abnormal points, and define the window size as w. Then the mean and standard deviation of the window at time t are:

[0029]

[0030] Among them, μ I (t) is the current mean value in the window, σ I (t) is the current standard deviation in the window, w is the size of the sliding window,

[0031] For temperature and humidity, calculate μ T (t),σ T (t) and μ H (t),σ H (t);

[0032] If the sample value deviates from the window mean by more than k times the standard deviation, it is marked as an abnormal point, where k is the abnormal threshold coefficient. For abnormal temperature and humidity detection, if |T filtered (t)-μ T (t)|>k·σ T (t) or |H filtered (t)-μ H (t)|>k·σ H (t), it is marked as a temperature or humidity abnormal point;

[0033] The filtered and abnormal data points are sorted into charging data stream Dcleaned (t):

[0034] D cleaned (t) = {I filtered (t),T filtered (t),H filtered (t)}, t=1,2,…,N, the data stream serves as the input for timing analysis and feature extraction.

[0035] As a preferred solution of the method for safe charging of a smart data cable described in the present invention, the abnormal conditions include power surge, current fluctuation, overheating or moisture short circuit.

[0036] As a preferred solution of the method for safe charging of a smart data cable described in the present invention, the steps of performing timing analysis and feature extraction on the charging data stream, evaluating abnormal conditions and generating risk scores are as follows:

[0037] Charging data flow D cleaned (t) = {I filtered (t),T filtered (t),H filtered (t)} is divided into sliding windows according to time, with a window length of w and a step size of s. Statistical features are extracted in each window.

[0038] For each window [t, t+w-1], the following features are extracted:

[0039]

[0040] Δ x (t) = x(t+w-1)-x(t),

[0041] Among them, μ x (t) is the mean value in the window, reflecting the average level of the signal, σ x (t) is the standard deviation within the window, describing the amplitude of signal fluctuation, Δ x (t) is the signal increment, which is used to capture trend changes, ρ x (t) is the autocorrelation coefficient, reflecting the short-term dependence of the signal, and x(i) represents I filtered ,T filtered ,H filtered ;

[0042] Arrange the features of each window into a feature vector F t :

[0043] F t ={μ I (t),σ I (t),Δ I (t),ρ I(t),μ T (t),σ T (t),Δ T (t),ρ T (t),μ H (t),σ H (t),Δ H (t),ρ H (t)},

[0044] Define the normal range for each characteristic, the definition formula is:

[0045] L x ≤x(t)≤U x , where L x ,U x are the lower and upper limits of feature x, indicating the normal range,

[0046] For the eigenvector F t , calculate the degree of deviation of each feature, the calculation formula is:

[0047] If L x ≤x(t)≤U x , then s x (t) = 0,

[0048] If x(t)>U x ,but

[0049] If x(t) <L x ,but

[0050] Among them, s x (t) is the abnormal deviation score of feature x, ranging from [0, 1]. For each feature, the more serious the abnormality, the higher the deviation score;

[0051] Combine the abnormal scores of all features to calculate the comprehensive risk score R(t), and the calculation formula is:

[0052]

[0053] Where n is the total number of features, w x is the feature weight;

[0054] According to the comprehensive risk score R(t), the risk level is divided into the following ways:

[0055] If R(t)<τ1, then the risk level = normal,

[0056] If τ1≤R(t)<τ2, then the risk level = slight abnormality,

[0057] If R(t)≥τ2, then the risk level = high risk,

[0058] Among them, τ1, τ2 are risk score thresholds.

[0059] As a preferred solution of the method for safe charging of a smart data cable described in the present invention, the response mechanism is: when it is detected that the temperature is too high, the wireless communication between the data cable and the mobile phone dynamically adjusts the charging power, switches to trickle mode or suspends fast charging; when the humidity is too high, the charging is actively interrupted.

[0060] As a preferred solution of the method for safe charging of a smart data cable described in the present invention, the trigger response mechanism is:

[0061] According to the feature extraction and risk score calculation results F t and R(t), determine the type of anomaly;

[0062] The judgment conditions for temperature abnormality are:

[0063]

[0064] Among them, T filtered (t) is the temperature value at time t, T max is the preset temperature safety threshold,

[0065] The judgment conditions for abnormal humidity are:

[0066]

[0067] Among them, H filtered (t) is the humidity value at time t, H max is the humidity safety threshold,

[0068] The risk status is:

[0069]

[0070] Among them, τ1 is the slight abnormal threshold. When τ1≤R(t)<τ2, the trickle mode is triggered, and when R(t)≥τ2, the strong response is triggered.

[0071] As a preferred solution of the method for safe charging of a smart data cable described in the present invention, the adjustment method after triggering the response mechanism is:

[0072] When T filtered (t)>T max The data line cooperates with the mobile phone through the communication module, switches to trickle mode, and reduces the power output to a lower range P target , the adjustment formula is:

[0073] Ptarget =η·P current ,η∈(0,1),

[0074] Among them, P target is the adjusted target power, P current is the current power, η is the power adjustment factor, which is dynamically calculated according to the degree of temperature anomaly. The calculation formula is:

[0075] If T filtered (t)>T max ,

[0076] When T filtered (t) When the temperature just exceeds the threshold, the power reduction is small. When the temperature is seriously abnormal, the power is significantly reduced.

[0077] If T filtered (t)>>T max , then the fast charge is suspended directly and switched to trickle charge mode. The triggering conditions are:

[0078]

[0079] Among them, T critical is the temperature safety critical value,

[0080] When H filtered (t)>H max , directly triggering the charging interruption mechanism, the built-in MOSFET control switch of the data line cuts off the charging path,

[0081] After the humidity anomaly is triggered, the data line checks whether the humidity status has returned to the normal range. The checking method is:

[0082] H filtered (t) <H safe ,

[0083] Among them, H safe It is a safe humidity value. The charging function will resume after it returns to the normal range.

[0084] The response execution logic of the response mechanism is:

[0085] If both temperature and humidity are abnormal, charging will be interrupted first, and humidity abnormality has a higher priority than temperature abnormality.

[0086] While the response mechanism is triggered, the data line sends feedback information to the mobile phone through the communication module, including the abnormality type and current status.

[0087] As a preferred solution of the method for safe charging of a smart data cable described in the present invention, in step S4, possible minor abnormalities and high-risk scenarios are predicted based on the user's historical charging behavior, and charging suggestions are provided.

[0088] As a preferred solution of the method for safe charging of a smart data cable described in the present invention, wherein: the data cable sends charging risk score information to the mobile phone through the communication module, and the mobile phone reminds the user in the following steps:

[0089] The data cable transmits the risk score information to the mobile phone through the communication module. The transmission content M includes:

[0090] M={t,R(t),T filtered (t),H filtered (t), exception type},

[0091] Where t is the time, R(t) is the risk score, T filtered (t),H filtered (t) is the current temperature and humidity, and the abnormal type is such as too high temperature or too high humidity;

[0092] After receiving the information, the mobile phone generates user prompts based on the risk score and abnormality type;

[0093] The storage module of the data line records the historical charging behavior to form a data set H:

[0094] H={t i ,R(t i ),T filtered (t i ),H filtered (t i ),P output (t i ), response type},i=1,2,…,N,

[0095] Among them, t i is the timestamp of the charging behavior, R(t i ) is the risk score at the corresponding moment, T filtered (t i ),H filtered (t i ) is the temperature and humidity data at that time, P output (t i ) is the output power at that time, and the response type is the type of protection mechanism triggered;

[0096] The user charging behavior is analyzed through the time series model, and the risk score change trend is modeled using the autoregressive model. The model formula is:

[0097] R(t)=φ1R(t-1)+φ2R(t-2)+…+φ p R(tp)+∈ t ,

[0098] Among them, φ i is the model coefficient, fitted by historical data, ∈ t is the random error, p is the model order, and the model output predicts the risk score R at the future moment pred (t);

[0099] Combine historical charging behavior and predictive risk scores to identify possible abnormal scenarios.

[0100] If the predicted risk score satisfies R pred (t)≥τ1, it is determined that there is a slight abnormal scene.

[0101] If the predicted risk score satisfies R pred (t)≥τ2, it is determined that there is a high-risk scenario and the user is reminded in advance;

[0102] Based on forecast results and historical patterns, targeted recommendations are provided in the following ways:

[0103] If temperature abnormalities occur frequently, it is recommended to avoid using the device while charging for a long time.

[0104] If humidity is abnormally high, it is recommended to keep the interface dry.

[0105] If fast charging is triggered abnormally frequently, it is recommended to reduce the frequency of fast charging.

[0106] The beneficial effects of the present invention are:

[0107] The present invention, through current fluctuation detection, temperature and humidity anomaly detection, in the fast charging scenario, the current sensor can accurately capture instantaneous fluctuations, timely trigger power adjustment or current limiting mechanism, and prevent equipment damage due to surge; in addition, the temperature sensor starts the trickle charging mode when overheated, and the humidity sensor prevents short circuit risks through power-off protection, making up for the defect of delayed response of overcurrent or overheating protection of traditional data lines; secondly, through the wireless communication module working in coordination with the mobile phone end, the data line can dynamically adjust the charging power, and feed back real-time risk scores and abnormal information to the mobile phone, reminding the user to take measures, which not only enhances the user's ability to interact with the charging device, but also helps the user to identify potential hidden dangers in the charging environment at an early stage; in addition, the recording and prediction function of historical charging behavior fills the shortcoming of the traditional data line's lack of adaptability to user charging habits; by analyzing the user's charging time, usage load and risk triggering data, it can predict future abnormal scenarios and provide targeted suggestions to improve user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0108] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0109] Figure 1 This is a flow chart of a method for safely charging a smart data cable in Example 1. DETAILED DESCRIPTION

[0110] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0111] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0112] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0113] Example 1

[0114] Reference Figure 1 This embodiment provides a method for safe charging of a smart data cable, wherein the smart data cable used has the following modules embedded therein:

[0115] Sensor modules for real-time monitoring of current, temperature and humidity parameters;

[0116] The current sensor uses the Hall effect current sensor AllegroACS712ELCTR05B, the current detection range is ±5A, the output mode is analog signal, and the accuracy is ±1.5%;

[0117] Temperature sensor, using Texas Instruments TMP117, operating temperature range 55 ° C to 150 ° C, I2C interface;

[0118] Humidity sensor, using Sensirion SHT31, with a relative humidity detection range of 0% to 100% RH, an I2C interface, fast sampling support, integrated temperature and humidity measurement, and optimized sensor quantity;

[0119] Processor module for data collection, abnormality analysis and preliminary intelligent response control;

[0120] The processor uses Nordic Semiconductor RF52840, which integrates an ARM Cortex M4 processor and runs at a main frequency of 64 MHz. It can meet the requirements of timing analysis and rule engines. It needs to have built-in Bluetooth 5.0 and low-power Bluetooth BLE communication modules. The storage space can be 1MB Flash + 256KB RAM, and it needs to support low-power mode.

[0121] Communication module, used to transmit the monitoring and analysis results of the data line and interact with the mobile phone;

[0122] The Bluetooth communication protocol is Bluetooth 5.0, which is integrated into the nRF52840 chip;

[0123] and Espressif Systems ESP32 WiFi communication chip;

[0124] Circuit and power management module, responsible for powering sensors, processors and communication modules;

[0125] The power management chip uses Texas Instruments BQ25895, supports 5V / 9V / 12V fast charging protocols, integrates input current limitation and dynamic power path management, and can prevent sensors from interfering with the charging process.

[0126] The voltage regulator uses AMS11173.3;

[0127] In this embodiment, an additional small heat dissipation module is built into the data line, using a Peltier thermoelectric cooling sheet TEC112706, which is activated when an abnormality is detected at high temperature to assist the interface area in dissipating heat, and the data line power management chip provides power;

[0128] Storage module, used to record historical charging behavior data and provide a basis for user behavior prediction;

[0129] Adopt EEPROM Atmel AT24C32, the interface is also I2C;

[0130] The steps for safe charging of smart data cables are as follows:

[0131] Step S1, continuously collecting operation data of the charging interface during charging to generate a charging data stream;

[0132] In step S1, the operation data is filtered to remove noise and detect sudden abnormalities to generate a charging data stream;

[0133] Charging data streams include: current fluctuation curves, temperature trends, and humidity levels;

[0134] The steps of filtering out noise and detecting sudden anomalies in the operating data and generating the charging data stream are as follows:

[0135] The sensor module collects current, temperature and humidity signals in real time, which are recorded as I(t), T(t) and H(t) respectively, where t represents the time and the sampling frequency is f s , after preliminary data formatting, a time series data set D(t) is formed:

[0136] D(t)={I(t),T(t),H(t)},t=1,2,…,N,

[0137] Where I(t) is the current value at time t, T(t) is the temperature value at time t, H(t) is the humidity value at time t, and N is the total number of sampling points, which is equal to the sampling frequency f s Multiply by the sampling time,

[0138] The signal is smoothed by a first-order low-pass filter to remove high-frequency noise interference. The filtering formula is:

[0139] I filtered (t) = α·I raw (t)+(1-α)·I filtered (t-1),

[0140] Among them, I filtered (t) is the current value at time t after filtering, I raw (t) is the original collected current value, α∈(0,1) is the smoothing factor, which controls the filter response speed.

[0141] Apply the filtering formula to the temperature and humidity signals to obtain the filtered temperature and humidity values ​​T respectively. filtered (t) and H filtered (t);

[0142] The sliding window method is used to extract the short-term mean and standard deviation of the running data, mark the abnormal points, and define the window size as w. Then the mean and standard deviation of the window at time t are:

[0143]

[0144] Among them, μ I (t) is the current mean value in the window, σ I (t) is the current standard deviation in the window, w is the size of the sliding window,

[0145] For temperature and humidity, calculate μ T (t),σ T (t) and μ H (t),σ H (t);

[0146] If the sampling value deviates from the window mean by more than k times the standard deviation, it is marked as an abnormal point, where k is the abnormal threshold coefficient. For abnormal temperature and humidity detection, if |T filtered (t)-μ T (t)|>k·σ T (t) or |H filtered (t)-μ H (t)|>k·σ H (t), it is marked as a temperature or humidity abnormal point;

[0147] The filtered and abnormal data points are sorted into charging data stream D cleaned (t):

[0148] D cleaned (t) = {I filtered (t),T filtered (t),H filtered (t)}, t=1,2,…,N, the data stream is used as the input for timing analysis and feature extraction;

[0149] Specifically, a clean charging data stream is generated by filtering out noise, removing high-frequency interference, and combining the sliding window method to detect anomalies.

[0150] Step S2, performing time series analysis and feature extraction on the charging data stream, evaluating whether there is an abnormality, and generating a risk score, including: normal, slightly abnormal, and high risk;

[0151] Abnormal conditions include power surges, current fluctuations, overheating or moisture short circuits;

[0152] The steps of performing timing analysis and feature extraction on the charging data stream, evaluating abnormal conditions and generating risk scores are as follows:

[0153] Charging data flow D cleaned (t) = {I filtered (t),T filtered (t),H filtered (t)} is divided into sliding windows according to time, with a window length of w and a step size of s. Statistical features are extracted in each window.

[0154] For each window [t, t+w-1], the following features are extracted:

[0155]

[0156] Δ x (t) = x(t+w-1)-x(t),

[0157] Among them, μ x(t) is the mean value in the window, reflecting the average level of the signal, σ x (t) is the standard deviation within the window, describing the amplitude of signal fluctuation, Δ x (t) is the signal increment, which is used to capture trend changes, ρ x (t) is the autocorrelation coefficient, reflecting the short-term dependence of the signal, and x(i) represents I filtered ,T filtered ,H filtered ;

[0158] Arrange the features of each window into a feature vector F t :

[0159] F t ={μ I (t),σ I (t),Δ I (t),ρ I (t),μ T (t),σ T (t),Δ T (t),ρ T (t),μ H (t),σ H (t),Δ H (t),ρ H (t)},

[0160] Define the normal range for each characteristic, the definition formula is:

[0161] L x ≤x(t)≤U x , where L x ,U x are the lower and upper limits of feature x, indicating the normal range,

[0162] For the eigenvector F t , calculate the degree of deviation of each feature, the calculation formula is:

[0163] If L x ≤x(t)≤U x , then s x (t) = 0,

[0164] If x(t)>U x ,but

[0165] If x(t) <L x ,but

[0166] Among them, s x (t) is the abnormal deviation score of feature x, ranging from [0, 1]. For each feature, the more serious the abnormality, the higher the deviation score;

[0167] Combine the abnormal scores of all features to calculate the comprehensive risk score R(t), and the calculation formula is:

[0168]

[0169] Where n is the total number of features, w x is the feature weight;

[0170] According to the comprehensive risk score R(t), the risk level is divided into the following ways:

[0171] If R(t)<τ1, then the risk level = normal,

[0172] If τ1≤R(t)<τ2, then the risk level = slight abnormality,

[0173] If R(t)≥τ2, then the risk level = high risk,

[0174] Among them, τ1, τ2 are risk score thresholds;

[0175] Specifically, through sliding window segmentation and feature extraction, combined with abnormal deviation scoring and comprehensive risk assessment model, abnormal situations are dynamically evaluated from the charging data stream and a risk score is generated. This result serves as the trigger condition for the subsequent response mechanism, providing data support for safe charging.

[0176] Step S3, when the risk score reaches a slight abnormality or high risk, a response mechanism is triggered;

[0177] The response mechanism is as follows: when the temperature is detected to be too high, the wireless communication between the data line and the mobile phone dynamically adjusts the charging power, switches to trickle mode or suspends fast charging; when the humidity is too high, the charging is actively interrupted;

[0178] The response mechanism is triggered by:

[0179] According to the feature extraction and risk score calculation results F t and R(t), determine the type of anomaly;

[0180] The judgment conditions for temperature abnormality are:

[0181]

[0182] Among them, T filtered (t) is the temperature value at time t, T max is the preset temperature safety threshold,

[0183] The judgment conditions for abnormal humidity are:

[0184]

[0185] Among them, H filtered (t) is the humidity value at time t, H max is the humidity safety threshold,

[0186] The risk status is:

[0187]

[0188] Among them, τ1 is the slight abnormal threshold. When τ1≤R(t)<τ2, the trickle mode is triggered, and when R(t)≥τ2, the strong response is triggered;

[0189] The adjustment method after triggering the response mechanism is:

[0190] When T filtered (t)>T max The data line cooperates with the mobile phone through the communication module, switches to trickle mode, and reduces the power output to a lower range P target , the adjustment formula is:

[0191] P target =η·P current ,η∈(0,1),

[0192] Among them, P target is the adjusted target power, P current is the current power, η is the power adjustment factor, which is dynamically calculated according to the degree of temperature anomaly. The calculation formula is:

[0193] If T filtered (t)>T max ,

[0194] When T filtered (t) When the temperature just exceeds the threshold, the power reduction is small. When the temperature is seriously abnormal, the power is significantly reduced.

[0195] If T filtered (t)>>T max , then the fast charge is suspended directly and switched to trickle charge mode. The triggering conditions are:

[0196]

[0197] Among them, T critical is the temperature safety critical value,

[0198] When H filtered (t)>H max , directly triggering the charging interruption mechanism, the built-in MOSFET control switch of the data line cuts off the charging path,

[0199] After the humidity anomaly is triggered, the data line checks whether the humidity status has returned to the normal range. The checking method is:

[0200] H filtered (t) <H safe ,

[0201] Among them, H safe It is a safe humidity value. The charging function will resume after it returns to the normal range.

[0202] The response execution logic of the response mechanism is:

[0203] If both temperature and humidity are abnormal, charging will be interrupted first, and humidity abnormality has a higher priority than temperature abnormality.

[0204] While the response mechanism is triggered, the data line sends feedback information to the mobile phone through the communication module, including the abnormality type and current status;

[0205] Specifically, through dynamic judgment and graded response of abnormal types, power adjustment or fast charging suspension is triggered when the temperature is too high, and charging is actively interrupted and status detection is performed when the humidity is too high, ultimately achieving efficient abnormal protection. The response results are synchronously fed back to the user through the communication module, improving charging safety and user experience.

[0206] Step S4, when the response mechanism is triggered, the data cable sends charging risk score information to the mobile phone through the communication module, and the mobile phone reminds the user;

[0207] In step S4, based on the user's historical charging behavior, possible minor anomalies and high-risk scenarios are predicted, and charging suggestions are provided;

[0208] The data cable sends charging risk score information to the mobile phone through the communication module, and the mobile phone reminds the user in the following steps:

[0209] The data cable transmits the risk score information to the mobile phone through the communication module. The transmission content M includes:

[0210] M={t,R(t),T filtered (t),H filtered (t), exception type},

[0211] Where t is the time, R(t) is the risk score, T filtered (t),H filtered (t) is the current temperature and humidity, and the abnormal type is such as too high temperature or too high humidity;

[0212] After receiving the information, the mobile phone generates user prompts based on the risk score and abnormality type;

[0213] The storage module of the data line records the historical charging behavior to form a data set H:

[0214] H={t i ,R(t i ),T filtered (t i ),H filtered (t i ),P output (t i ), response type},i=1,2,…,N,

[0215] Among them, t i is the timestamp of the charging behavior, R(t i ) is the risk score at the corresponding moment, T filtered (t i ),H filtered (t i ) is the temperature and humidity data at that time, P output (t i ) is the output power at that time, and the response type is the type of protection mechanism triggered;

[0216] The user charging behavior is analyzed through the time series model, and the risk score change trend is modeled using the autoregressive model. The model formula is:

[0217] R(t)=φ1R(t-1)+φ2R(t-2)+…+φ p R(tp)+∈ t ,

[0218] Among them, φ i is the model coefficient, fitted by historical data, ∈ t is the random error, p is the model order, and the model output predicts the risk score R at the future moment pred (t);

[0219] Combine historical charging behavior and predictive risk scores to identify possible abnormal scenarios.

[0220] If the predicted risk score satisfies R pred (t)≥τ1, it is determined that there is a slight abnormal scene.

[0221] If the predicted risk score satisfies R pred (t)≥τ2, it is determined that there is a high-risk scenario and the user is reminded in advance;

[0222] Based on forecast results and historical patterns, targeted recommendations are provided in the following ways:

[0223] If temperature abnormalities occur frequently, it is recommended to avoid using the device while charging for a long time.

[0224] If humidity is abnormally high, it is recommended to keep the interface dry.

[0225] If fast charging is triggered abnormally frequently, it is recommended to reduce the frequency of fast charging;

[0226] Specifically, risk scores and abnormal information are transmitted through the communication module to remind users of their current status in real time. By combining historical charging behavior data and predictive models, potential abnormal scenarios are identified and personalized charging recommendations are generated, effectively improving charging safety and user experience.

[0227] Experimental Example 1

[0228] The experimental equipment includes:

[0229] The smart data cable prototype in Example 1, the smart phone: equipped with an experimental APP, is used to receive information transmitted by the data cable and display user prompts,

[0230] Adjustable power supply: supports 5V / 9V / 12V output, simulates fast charging and current fluctuations,

[0231] Adjustable load: used to simulate the operating load of the mobile phone in different power modes.

[0232] Temperature control box: can control the temperature and humidity of the charging environment;

[0233] Experimental parameters:

[0234] Current safety range: 0A~3A,

[0235] Temperature threshold: 45-60℃,

[0236] Humidity threshold: 60%-80%RH,

[0237] Sliding window size: 10 seconds, sampling frequency: 1Hz,

[0238] Communication delay requirement: less than 100ms;

[0239] Experimental procedures

[0240] Step 1: Current Fluctuation Detection Experiment

[0241] 1. Use an adjustable power supply to set a stable current of 5V / 2A and verify the detection accuracy of the data line for normal current (record the deviation between the actual value and the collected value).

[0242] 2. During the charging process, simulate instantaneous current fluctuations (such as 2A switching to 3.5A and falling back), and observe the following:

[0243] Whether the data line detects current fluctuations (determine the difference between the detection time and the fluctuation trigger time),

[0244] Whether the risk score calculation logic is triggered correctly, whether the mobile phone receives the "current abnormality" prompt in time, and records the delay time;

[0245] Step 2: Temperature anomaly detection experiment

[0246] 1. Place the smart data cable in a normal temperature environment of 25°C to verify its temperature measurement accuracy.

[0247] 2. Gradually increase the ambient temperature to 50°C in the temperature control box and observe.

[0248] Is the data line detected? filtered (t)>T max ,

[0249] Whether the communication between the data cable and the mobile phone triggers "fast charging switching to trickle mode" and records the response delay time.

[0250] 3. Continue to increase the temperature to 65°C to verify whether the "fast charge pause" response is triggered;

[0251] Step 3: Humidity anomaly detection experiment

[0252] 1. Use a normal humidity environment of 50% RH to verify the basic performance of the humidity sensor.

[0253] 2. Increase the humidity gradually to 85% RH and observe:

[0254] Is the data line detected H filtered (t)>H max ,

[0255] Whether to trigger the charging interruption response and record the disconnection delay time,

[0256] 3. Restore the humidity to the safe range of 60% RH and verify whether charging can be automatically resumed;

[0257] Experimental Results

[0258] 1. The data line can accurately collect current, temperature and humidity data, and the detection accuracy error is less than 5%.

[0259] 2. After the exception is triggered, the response time is controlled within 100ms.

[0260] 3. The mobile phone can receive accurate abnormal prompt information with a delay of less than 100ms.

[0261] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for safe charging of a smart data cable, characterized in that: The intelligent data cable has built-in sensor module, processor module, communication module, circuit and power management module and storage module: The sensor module is used to monitor the operating data of the charging interface in real time, including current, temperature and humidity data; The processor module is used to collect operating data, evaluate abnormal conditions and initiate response mechanisms; The communication module is used to transmit the monitoring results of the sensor module and the analysis results of the processor module, and interact with the mobile phone; The circuit and power management module are responsible for supplying power to the sensor, processor and communication module; The storage module is used to collect and record historical charging behavior data; The method steps of intelligent data safe charging are as follows: Step S1, continuously collecting operation data of the charging interface during charging to generate a charging data stream; Step S2, performing time series analysis and feature extraction on the charging data stream, evaluating whether there is an abnormality, and generating a risk score, including: normal, slightly abnormal, and high risk; Step S3, when the risk score reaches a slight abnormality or high risk, a response mechanism is triggered; Step S4, while triggering the response mechanism, the data cable sends charging risk score information to the mobile phone through the communication module, and the mobile phone reminds the user.

2. A method for safe charging of a smart data cable as claimed in claim 1, characterized in that: In the step S1, the operation data is filtered to remove noise and detect sudden abnormalities to generate a charging data stream; The charging data stream includes: current fluctuation curve, temperature trend and humidity level.

3. A method for safe charging of a smart data cable as claimed in claim 2, characterized in that: The step of filtering out noise and detecting sudden abnormalities on the operating data to generate a charging data stream is: The sensor module collects current, temperature and humidity signals in real time, which are recorded as I(t), T(t) and H(t) respectively, where t represents the time and the sampling frequency is f s , after preliminary data formatting, a time series data set D(t) is formed: D(t)={I(t),T(t),H(t)},t=1,2,…,N, Where I(t) is the current value at time t, T(t) is the temperature value at time t, H(t) is the humidity value at time t, and N is the total number of sampling points, which is equal to the sampling frequency f s Multiply by the sampling time, The signal is smoothed by a first-order low-pass filter to remove high-frequency noise interference. The filtering formula is: I filtered (t)=α·I raw (t)+(1-α)·I filtered (t-1), Among them, I filtered (t) is the current value at time t after filtering, I raw (t) is the original collected current value, α∈(0,1) is the smoothing factor, which controls the filter response speed. Apply the filtering formula to the temperature and humidity signals to obtain the filtered temperature and humidity values ​​T respectively. filtered (t) and H filtered (t); The sliding window method is used to extract the short-term mean and standard deviation of the running data, mark the abnormal points, and define the window size as w. Then the mean and standard deviation of the window at time t are: Among them, μ I (t) is the current mean value in the window, σ I (t) is the current standard deviation in the window, w is the size of the sliding window, For temperature and humidity, calculate μ T (t),σ T (t) and μ H (t),σ H (t); If the sampling value deviates from the window mean by more than k times the standard deviation, it is marked as an abnormal point, where k is the abnormal threshold coefficient. For abnormal temperature and humidity detection, if |T filtered (t)-μ T (t)|>k·σ T (t) or |H filtered (t)-μ H (t)|>k·σ H (t), it is marked as a temperature or humidity abnormal point; The filtered and abnormal data points are sorted into charging data stream D cleaned (t): D cleaned (t) = {I filtered (t),T filtered (t),H filtered (t)}, t=1,2,…,N, the data stream serves as the input for timing analysis and feature extraction.

4. A method for safe charging of a smart data cable as claimed in claim 3, characterized in that: Such abnormal conditions include power surges, current fluctuations, overheating or moisture short circuits.

5. A method for safe charging of a smart data cable as claimed in claim 4, characterized in that: The steps of performing timing analysis and feature extraction on the charging data stream, evaluating abnormal conditions and generating risk scores are as follows: Charging data flow D cleaned (t) = {I filtered (t),T filtered (t),H filtered (t)} is divided into sliding windows according to time, with a window length of w and a step size of s. Statistical features are extracted in each window. For each window [t, t+w-1], the following features are extracted: Δ x (t)=x(t+w-1)-x(t), Among them, μ x (t) is the mean value in the window, reflecting the average level of the signal, σ x (t) is the standard deviation within the window, describing the amplitude of signal fluctuation, Δ x (t) is the signal increment, which is used to capture trend changes, ρ x (t) is the autocorrelation coefficient, reflecting the short-term dependence of the signal, and x(i) represents I filtered ,T filtered ,H filtered ; Arrange the features of each window into a feature vector F t : F t ={μ I (t),σ I (t),D I (t),p I (t),μ T (t),σ T (t),D T (t),p T (t),μ H (t),σ H (t),D H (t),p H (t)}, Define the normal range for each characteristic, the definition formula is: L x ≤x(t)≤U x , where L x ,U x are the lower and upper limits of feature x, indicating the normal range, For the eigenvector F t , calculate the degree of deviation of each feature, the calculation formula is: If L x ≤x(t)≤U x , then s x (t) = 0, If x(t)>U x ,but If x(t) <L x ,but Among them, s x (t) is the abnormal deviation score of feature x, ranging from [0, 1]. For each feature, the more serious the abnormality, the higher the deviation score; Combine the abnormal scores of all features to calculate the comprehensive risk score R(t), and the calculation formula is: Where n is the total number of features, w x is the feature weight; According to the comprehensive risk score R(t), the risk level is divided into the following ways: If R(t)<τ1, then the risk level = normal, If τ1≤R(t)<τ2, then the risk level = slight abnormality, If R(t)≥τ2, then the risk level = high risk, Among them, τ1, τ2 are risk score thresholds.

6. A method for safe charging of a smart data cable as claimed in claim 5, characterized in that: The response mechanism is as follows: when the temperature is detected to be too high, the wireless communication between the data line and the mobile phone dynamically adjusts the charging power, switches to trickle mode or suspends fast charging; when the humidity is too high, the charging is actively interrupted.

7. A method for safe charging of a smart data cable as claimed in claim 6, characterized in that: The trigger response mechanism is: According to the feature extraction and risk score calculation results F t and R(t), determine the type of anomaly; The judgment conditions for temperature abnormality are: Among them, T filtered (t) is the temperature value at time t, T max is the preset temperature safety threshold, The judgment conditions for abnormal humidity are: Among them, H filtered (t) is the humidity value at time t, H max is the humidity safety threshold, The risk status is: Among them, τ1 is the slight abnormal threshold. When τ1≤R(t)<τ2, the trickle mode is triggered, and when R(t)≥τ2, the strong response is triggered.

8. A method for safe charging of a smart data cable as claimed in claim 7, characterized in that: The adjustment method after triggering the response mechanism is: When T filtered (t)>T max The data line cooperates with the mobile phone through the communication module, switches to trickle mode, and reduces the power output to a lower range P target , the adjustment formula is: P.S target Nη·P current ,η∈(0,1), Among them, P target is the adjusted target power, P current is the current power, η is the power adjustment factor, which is dynamically calculated according to the degree of temperature anomaly. The calculation formula is: If T filtered (t)>T max , When T filtered (t) When the temperature just exceeds the threshold, the power reduction is small. When the temperature is seriously abnormal, the power is significantly reduced. If T filtered (t)>>T max , then the fast charge is suspended directly and switched to trickle charge mode. The triggering conditions are: Among them, T critical is the temperature safety critical value, When H filtered (t)>H max , directly triggering the charging interruption mechanism, the built-in MOSFET control switch of the data line cuts off the charging path, After the humidity anomaly is triggered, the data line checks whether the humidity status has returned to the normal range. The checking method is: H filtered (t)<H safe , Among them, H safe It is a safe humidity value. The charging function will resume after it returns to the normal range. The response execution logic of the response mechanism is: If both temperature and humidity are abnormal, charging will be interrupted first, and humidity abnormality has a higher priority than temperature abnormality. While the response mechanism is triggered, the data line sends feedback information to the mobile phone through the communication module, including the abnormality type and current status.

9. A method for safe charging of a smart data cable as claimed in claim 8, characterized in that: In step S4, possible minor abnormalities and high-risk scenarios are predicted based on the user's historical charging behavior, and charging suggestions are provided.

10. A method for safe charging of a smart data cable as claimed in claim 9, characterized in that: The data cable sends charging risk score information to the mobile phone through the communication module, and the mobile phone reminds the user in the following steps: The data cable transmits the risk score information to the mobile phone through the communication module. The transmission content M includes: M={t,R(t),T filtered (t),H filtered (t), exception type}, Where t is the time, R(t) is the risk score, T filtered (t),H filtered (t) is the current temperature and humidity, and the abnormal type is such as too high temperature or too high humidity; After receiving the information, the mobile phone generates user prompts based on the risk score and abnormality type; The storage module of the data line records the historical charging behavior to form a data set H: H={t i ,R(t i ),T filtered (t i ),H filtered (t i ),P output (t i ), response type},i=1,2,…,N, Among them, t i is the timestamp of the charging behavior, R(t i ) is the risk score at the corresponding moment, T filtered (t i ),H filtered (t i ) is the temperature and humidity data at that time, P output (t i ) is the output power at that time, and the response type is the type of protection mechanism triggered; The user charging behavior is analyzed through the time series model, and the risk score change trend is modeled using the autoregressive model. The model formula is: R(t)=φ1R(t-1)+φ2R(t-2)+…+φ p R(tp)+∈ t , Among them, φ i is the model coefficient, fitted by historical data, ∈ t is the random error, p is the model order, and the model output predicts the risk score R at the future moment pred (t); Combine historical charging behavior and predictive risk scores to identify possible abnormal scenarios. If the predicted risk score satisfies R pred (t)≥τ1, it is determined that there is a slight abnormal scene. If the predicted risk score satisfies R pred If (t)≥τ2, it is determined that there is a high-risk scenario and the user is reminded in advance; Based on forecast results and historical patterns, targeted recommendations are provided in the following ways: If temperature abnormalities occur frequently, it is recommended to avoid using the device while charging for a long time. If humidity is abnormally high, it is recommended to keep the interface dry. If fast charging is triggered abnormally frequently, it is recommended to reduce the frequency of fast charging.

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