A method for safe charging of an intelligent data cable

Abstract

The present invention discloses a method for safe charging of an intelligent data cable, which relates to the technical field of data cables. In the present invention, through current fluctuation detection, abnormal temperature and humidity detection, in the fast charging scenario, the current sensor can accurately capture instantaneous fluctuations and timely trigger the power adjustment or current limiting mechanism to prevent device damage caused by surges; in addition, the temperature sensor starts the trickle charging mode when overheated, while the humidity sensor prevents short-circuit risks through power-off protection; secondly, by working in coordination with the mobile phone through the wireless communication module, the data cable can dynamically adjust the charging power and feedback the real-time risk score and abnormal information to the mobile phone to remind the user to take measures, which not only enhances the interaction ability between the user and the charging device and helps the user identify potential hidden dangers in the charging environment at an early stage; in addition, by analyzing the user's charging time, usage load and risk trigger data, it is possible to predict future abnormal scenarios and provide targeted suggestions to improve the user experience.

Description

Technical Field

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

[0002] With the wide 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; currently, the data cables on the market mainly have data transmission and power transmission functions, but in a high-power fast charging environment, the performance often decreases due to heat generation problems, and there are even potential safety hazards; in addition, factors such as humidity and current fluctuations in the charging environment will also affect the charging stability, and when users often discover problems, the device may have been damaged; therefore, how to make the data cable become the "first line of defense" for charging safety and achieve intelligent collaboration with mobile phones is one of the problems that current technologies urgently need to overcome.

[0003] Traditional data cable charging solutions mostly address the above problems through physical structure design. Some solutions integrate an overcurrent protection chip and an overheat protection module in the data cable, which can automatically disconnect the circuit when abnormal current or too high temperature occurs; some high-end data cables add a current stability control function to reduce the risk of device 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; overheat protection is mostly passive triggering. When users usually perceive that the device is hot, the battery life may have been damaged and it is difficult to take timely measures to intervene. Therefore, an intelligent data cable safe charging method 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 intelligent data cables to solve the problems that the charging solutions of traditional data cables lack the collaborative ability with terminal devices such as mobile phones and cannot remind users to take measures in the early stage.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] An embodiment of the present invention provides a method for safe charging of intelligent data cables. 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 operation data, evaluate abnormal situations, and activate the response mechanism;

[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 is responsible for supplying power to the sensor, processor, and communication modules;

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

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

[0014] Step S1: Continuously collect the operation data of the charging interface during charging to generate a charging data stream;

[0015] Step S2: Perform time series analysis and feature extraction on the charging data stream to evaluate whether there are abnormal situations and generate a risk score, including: normal, slightly abnormal, and high risk;

[0016] Step S3: When the risk score reaches slightly abnormal or high risk, trigger the response mechanism;

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

[0018] As a preferred solution of the method for intelligent data cable security charging described in the present invention, wherein: in step S1, noise filtering and burst anomaly detection are 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 intelligent data cable security charging described in the present invention, wherein: the step of performing noise filtering and burst anomaly detection on the operation data to generate a charging data stream is as follows:

[0021] The sensor module real-time collects current, temperature, and humidity signals, denoted 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] Among them, 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 multiplied by the sampling time,

[0024] Smooth the signal through 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] where I filtered (t) is the current value at time t after filtering, I raw (t) is the originally collected current value, and α ∈ (0, 1) is the smoothing factor that controls the filtering response speed,

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

[0028] Use the sliding window method to extract the short-term mean and standard deviation of the operating data, mark the abnormal points. Define the window size as w, then the mean and standard deviation of the window within time t are:

[0029]

[0030] where μ I (t) is the mean current within the window, σ I (t) is the standard deviation of the current within the window, and 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 sampled value deviates from the window mean by more than k times the standard deviation, it is marked as an abnormal point. k is the abnormal threshold coefficient. Detect abnormalities in temperature and humidity. If |T filtered (t) - μ T (t)| > k·σ T (t) or |H filtered (t) - μ H (t)| > k·σ H (t), then it is marked as a temperature or humidity abnormal point;

[0033] Organize the filtered and abnormal data points removed into the charging data stream Dcleaned (t):

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

[0035] As a preferred solution of the method for safe charging of an intelligent data cable according to the present invention, wherein: the abnormal conditions include power surges, current fluctuations, overheating, or moisture short circuits.

[0036] As a preferred solution of the method for safe charging of an intelligent data cable according to the present invention, wherein: the steps of performing time series analysis and feature extraction on the charging data stream, evaluating abnormal conditions, and generating a risk score are as follows.

[0037] For the charging data stream D cleaned (t) = {I filtered (t), T filtered (t), H filtered (t)}, it is divided into sliding windows according to time, the window length is w, and the step size is s. Statistical features are extracted within 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] where μ x (t) is the mean value within the window, reflecting the average level of the signal, σ x (t) is the standard deviation within the window, describing the amplitude of signal fluctuations, Δ x (t) is the signal increment, 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] The features of each window are organized 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 feature, and the defining formula is:

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

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

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

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

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

[0050] where s x (t) is the abnormal deviation score of feature x, with a range of [0, 1]. For each feature, the more severe 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, and w x is the feature weight;

[0054] According to the comprehensive risk score R(t), divide the risk level, and the division method is:

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

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

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

[0058] where τ1 and τ2 are risk score thresholds.

[0059] As a preferred solution of the method for safe charging of an intelligent data cable according to the present invention, wherein: the response mechanism is: when the detected temperature is too high, the wireless communication between the data cable and the mobile phone side dynamically adjusts the charging power, switches to the trickle mode or pauses fast charging; when the humidity is too high, charging is actively interrupted.

[0060] As a preferred solution of the method for safe charging of an intelligent data cable according to the present invention, wherein: the way to trigger the response mechanism is,

[0061] According to the feature extraction and the risk score calculation result F t and R(t), judge the type of abnormality;

[0062] The determination condition for temperature abnormality is:

[0063]

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

[0065] The determination condition for humidity abnormality is:

[0066]

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

[0068] The risk state is:

[0069]

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

[0071] As a preferred solution of the method for safe charging of an intelligent data cable according to the present invention, wherein: the adjustment method after triggering the response mechanism is,

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

[0073] Ptarget = η·P current , where η ∈ (0, 1),

[0074] where P target is the adjusted target power, P current is the current power, and 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) just exceeds the threshold, the power reduction amplitude is small. When the temperature anomaly is severe, the power is significantly reduced.

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

[0078]

[0079] where T critical is the temperature safety critical value,

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

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

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

[0083] where H safe is the safe humidity value. After it returns to the normal range, the charging function is restored;

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

[0085] If both the temperature and humidity are abnormal, the charging is interrupted first. The priority of the humidity anomaly is greater than that of the temperature anomaly;

[0086] While triggering the response mechanism, the data cable sends feedback information to the mobile phone through the communication module, including the type of anomaly and the current status.

[0087] As a preferred embodiment of the method for safe charging of an intelligent data cable according to the present invention, 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.

[0088] As a preferred embodiment of the method for safe charging of an intelligent data cable according to the present invention, in the step where the data cable sends charging risk score information to the mobile phone through the communication module and the mobile phone terminal reminds the user:

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

[0090] M = {t, R(t), T filtered (t), H filtered (t), anomaly 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 anomaly type is, for example, too high temperature or too high humidity;

[0092] After receiving the information, the mobile phone terminal generates user prompt content according to the risk score and the anomaly type;

[0093] The storage module of the data cable 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] where t i is the timestamp of the charging behavior, R(t i ) is the risk score at the corresponding time, 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] By analyzing the user's charging behavior through a time series model and using an autoregressive model to model the change trend of the risk score, the model formula is:

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

[0098] where φ i is the model coefficient, fitted from historical data, and ∈ t is the random error, p is the model order, and the model output predicts the risk score R pred (t) at a future time;

[0099] Combining historical charging behavior and the predicted risk score, possible abnormal scenarios are identified.

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

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

[0102] According to the prediction results and historical patterns, targeted suggestions are provided in the following ways:

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

[0104] If humidity anomalies occur frequently, it is recommended to keep the interface dry.

[0105] If fast - charging anomalies are triggered frequently, it is recommended to reduce the fast - charging frequency.

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

[0107] In the present invention, through current fluctuation detection, temperature and humidity anomaly detection, in a fast - charging scenario, the current sensor can accurately capture instantaneous fluctuations, timely trigger the power adjustment or current - limiting mechanism, and prevent device damage caused by surges. In addition, the temperature sensor starts the trickle - charging mode when overheating, while the humidity sensor prevents short - circuit risks through power - off protection, making up for the defect of the slow response of traditional data cables in over - current or over - heat protection. Secondly, by working in coordination with the mobile phone through the wireless communication module, the data cable can dynamically adjust the charging power, and feedback the real - time risk score and anomaly information to the mobile phone to remind the user to take measures, which not only enhances the interaction ability between the user and the charging device, helping the user identify potential hidden dangers in the charging environment at an early stage. In addition, the recording and prediction functions of historical charging behavior fill the shortcoming of the lack of adaptability of traditional data cables to the user's charging habits. By analyzing the user's charging time, load used, and risk - triggering data, it is possible to predict future abnormal scenarios and provide targeted suggestions, improving the user experience. Description of the Drawings

[0108] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0109] Figure 1 It is a flowchart of a method for safe charging of an intelligent data cable in Embodiment 1. Specific embodiments

[0110] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the specific embodiments of the present invention in detail with reference to the accompanying drawings of the specification.

[0111] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0112] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The phrase "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an individual or alternative embodiment that excludes other embodiments.

[0113] Embodiment 1

[0114] Refer to Figure 1 , this embodiment provides a method for safe charging of an intelligent data cable. The intelligent data cable used is embedded with the following modules:

[0115] A sensor module for real-time monitoring of current, temperature, and humidity parameters;

[0116] The current sensor uses the Hall effect current sensor Allegro ACS712ELCTR05B, with a current detection range of ±5A, an analog signal output mode, and an accuracy of ±1.5%;

[0117] The temperature sensor uses Texas Instruments TMP117, with an operating temperature range of 55°C to 150°C and an I2C interface;

[0118] The humidity sensor uses Sensirion SHT31, with a relative humidity detection range of 0% to 100% RH, an I2C interface, supports fast sampling, integrates temperature and humidity measurement, and can optimize the number of sensors;

[0119] A processor module for data acquisition, anomaly analysis, and preliminary intelligent response control;

[0120] The processor uses Nordic Semiconductor nRF52840, which integrates an ARM Cortex-M4 processor with a running frequency of 64 MHz. It can meet the requirements of timing analysis and rule engines. It needs to have a built-in Bluetooth 5.0 and low-power Bluetooth BLE communication module. A storage space of 1MB Flash + 256KB RAM is sufficient, and it needs to support the low-power mode;

[0121] A communication module for transmitting the monitoring and analysis results of the data line and interacting with the mobile phone;

[0122] The Bluetooth communication protocol selects Bluetooth 5.0 and is integrated into the nRF52840 chip;

[0123] And Espressif Systems ESP32 WiFi communication chip;

[0124] A circuit and power management module responsible for powering the sensors, processor, and communication module;

[0125] The power management chip uses Texas Instruments BQ25895, supports 5V / 9V / 12V fast charging protocols, integrates input current limit and dynamic power path management, and can avoid sensor interference during the charging process;

[0126] The voltage regulator uses AMS1117 3.3;

[0127] In this embodiment, a small heat dissipation module is additionally built into the data line. It uses a Peltier thermoelectric cooler TEC112706, which is activated when an anomaly is detected at high temperatures to assist in heat dissipation in the interface area and is powered by the data line power management chip;

[0128] A storage module for recording historical charging behavior data to provide a basis for user behavior prediction;

[0129] It uses EEPROM Atmel AT24C32, and the interface is also I2C;

[0130] The method steps for safe charging of the intelligent data line are as follows:

[0131] Step S1: Continuously collect the operation data of the charging interface during charging to generate a charging data stream;

[0132] In step S1, filter the noise and detect sudden anomalies in the operation data to generate a charging data stream;

[0133] The charging data stream includes: current fluctuation curve, temperature trend, and humidity level;

[0134] The steps for filtering noise and detecting sudden anomalies in the operating data to generate a charging data stream are as follows:

[0135] The sensor module collects current, temperature, and humidity signals in real time, denoted 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, equal to the sampling frequency f s multiplied 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] where I filtered (t) is the current value at time t after filtering, I raw (t) is the originally collected current value, and α ∈ (0, 1) is the smoothing factor that controls the filtering response speed,

[0141] Applying the filtering formula to the temperature and humidity signals, the filtered temperature and humidity values T filtered (t) and H filtered (t) are obtained respectively;

[0142] The short-term mean and standard deviation of the operating data are extracted using the sliding window method, and the abnormal points are marked. Defining the window size as w, the mean and standard deviation of the window within time t are:

[0143]

[0144] where μ I (t) is the mean current within the window, σ I (t) is the standard deviation of the current within the window, and 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 sampled value deviates from the window mean by more than k times the standard deviation, it is marked as an outlier. k is the outlier threshold coefficient. For temperature and humidity detection anomalies, if filtered (t) - μ T (t)| > k·σ T (t) or |H filtered (t) - μ H (t)| > k·σ H (t), then it is marked as a temperature or humidity outlier point;

[0147] The filtered and anomaly - removed data points are organized into a 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 time - series analysis and feature extraction;

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

[0150] Step S2: Perform time - series analysis and feature extraction on the charging data stream, evaluate whether there are abnormal situations, and generate a risk score, including: normal, slightly abnormal, and high - risk;

[0151] Abnormal situations include surges, current fluctuations, overheating, or wet short - circuits;

[0152] The steps of performing time - series analysis and feature extraction on the charging data stream, evaluating abnormal situations, and generating a risk score are as follows.

[0153] For the charging data stream D cleaned (t) = {I filtered (t), T filtered (t), H filtered (t)} is divided into sliding windows by time. The window length is w and the step size is s. Statistical features are extracted within 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] where μ x(t) is the mean value within the window, reflecting the average level of the signal, and σ x (t) is the standard deviation within the window, describing the amplitude of signal fluctuations, and Δ x (t) is the signal increment, used to capture trend changes, and 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] Organize 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 feature, and the defining formula is:

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

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

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

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

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

[0166] where s x (t) is the abnormal deviation score of the feature x, and the range is [0,1]. For each feature, the more abnormal, the higher the deviation score;

[0167] Calculate the comprehensive risk score R(t) by combining the anomaly scores of all features. The calculation formula is as follows:

[0168]

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

[0170] According to the comprehensive risk score R(t), divide the risk levels. The division method is as follows:

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

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

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

[0174] where τ1 and τ2 are risk score thresholds;

[0175] Specifically, through sliding window segmentation and feature extraction, combined with the anomaly deviation score and the comprehensive risk assessment model, dynamically evaluate the abnormal situation from the charging data stream and generate a risk score. This result is used as the trigger condition for the subsequent response mechanism to provide data support for safe charging.

[0176] Step S3, when the risk score reaches slightly abnormal or high risk, trigger the response mechanism;

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

[0178] The way to trigger the response mechanism is

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

[0180] The determination condition for temperature anomaly is:

[0181]

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

[0183] The determination condition for humidity anomaly is:

[0184]

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

[0186] The risk status is:

[0187]

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

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

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

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

[0192] Among them, P target is the target power after adjustment, P current is the current power, and 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) just exceeds the threshold, the power reduction amplitude is small. When the temperature anomaly is serious, the power is significantly reduced.

[0195] If T filtered (t) >> T max , then the fast charging is directly paused and switched to the trickle mode. Trigger conditions:

[0196]

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

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

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

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

[0201] where H safe is the safe humidity value. After it returns to the normal range, the charging function is restored;

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

[0203] if both the temperature and humidity are abnormal, charging is interrupted preferentially, and the priority of humidity anomaly is greater than that of temperature anomaly;

[0204] While triggering the response mechanism, the data cable sends feedback information to the mobile phone through the communication module, including the anomaly type and the current status;

[0205] Specifically, through the dynamic determination and hierarchical response of the anomaly type, power adjustment or fast charging pause 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, efficient anomaly protection is achieved, and the response result is synchronously fed back to the user through the communication module, improving charging safety and user experience.

[0206] 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 side 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 steps for the data cable to send charging risk score information to the mobile phone through the communication module and the mobile phone side to remind the user are as follows:

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

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

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

[0212] After receiving the information, the mobile phone side generates user prompt content according to the risk score and the anomaly type;

[0213] The storage module of the data cable 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] where 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 triggered protection mechanism;

[0216] Analyze the user's charging behavior through a time series model, and use an autoregressive model to model the change trend of the risk score. The model formula is:

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

[0218] where φ i is the model coefficient, fitted from historical data, ∈ t is the random error, p is the model order, and the model output predicts the risk score R pred (t);

[0219] Combine historical charging behavior and predicted 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 minor abnormal scenario,

[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] Provide targeted suggestions according to the prediction results and historical patterns. The way of providing is:

[0223] If temperature anomalies occur frequently, it is recommended to avoid charging and using at the same time for a long time,

[0224] If humidity anomalies occur frequently, 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, the risk score and abnormal information are transmitted through the communication module to remind the user of the current status in real time. Combining historical charging behavior data and the prediction model, potential abnormal scenarios are identified and personalized charging suggestions are generated, effectively improving charging safety and the user experience.

[0227] Experimental Example 1

[0228] The experimental equipment includes:

[0229] The intelligent data cable prototype in Example 1, a smart phone: equipped with an experimental APP for receiving the information transmitted by the data cable and displaying user prompts,

[0230] An adjustable power supply: supporting 5V / 9V / 12V output to simulate fast charging and current fluctuations,

[0231] An adjustable load: used to simulate the operating load of the mobile phone in different power modes,

[0232] A temperature control box: capable of controlling the temperature and humidity of the charging environment;

[0233] Experimental parameters:

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

[0235] Temperature threshold: 45 - 60 °C,

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

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

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

[0239] Experimental steps

[0240] Step 1: Current fluctuation detection experiment

[0241] 1. Use the adjustable power supply to set a stable current of 5V / 2A to verify the detection accuracy of the data cable 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 switching from 2A to 3.5A and then falling back), and observe the following,

[0243] Whether the data cable detects the current fluctuation (judge the difference between the detection time and the fluctuation trigger time),

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

[0245] Step 2: Temperature Abnormality Detection Experiment

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

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

[0248] T filtered (t)>T max ,

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

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

[0251] Step 3: Humidity Abnormality Detection Experiment

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

[0253] 2. Gradually increase the humidity to 85% RH and observe whether the data cable detects

[0254] H filtered (t)>H max ,

[0255] whether the charging interruption response is triggered, and record the disconnection delay time.

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

[0257] Experimental Results

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

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

[0260] 3. The mobile phone can receive accurate abnormality prompt information, and the delay time is less than 100 ms.

[0261] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by 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, 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; 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; 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.

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: 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 1, 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.

8. A method for safe charging of a smart data cable as claimed in claim 7, 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 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.

Images