Anti-outage power supply control method based on capacitive liquid crystal screen

Abstract

The invention provides an anti-outage power supply control method based on a capacitive liquid crystal screen, and the method comprises the steps: obtaining the temperature and humidity data of the current environment of the liquid crystal screen, and determining the type selection of a target capacitor material under the current environment condition through the combined analysis of the current environment parameters and a pre-established capacitor material type database; a prediction model of environment temperature and humidity and capacitor charging and discharging times and time intervals is established by analyzing capacitor charging and discharging historical data, the charging and discharging behavior of a capacitor is predicted in real time according to the current environment condition, if the deviation between a predicted value and a measured value exceeds a preset threshold value, an abnormal condition is judged, and a power-off information storage strategy is dynamically adjusted; working current and voltage parameters of the liquid crystal screen are obtained, the charging and discharging states of a capacitor are evaluated in combination with current environment temperature and humidity data, if the voltage of the capacitor exceeds a preset rated value, it is considered that the over-charging and over-discharging risk exists, and according to the risk, a power-off information storage strategy is dynamically optimized, the charging current is reduced or charging is terminated in advance.

Description

Technical Field

[0001] The invention relates to the field of information technology, and in particular to a power supply control method for preventing power failure based on a capacitive liquid crystal screen. Background Art

[0002] In the scenario of power-off information preservation on LCD screens, the charge and discharge characteristics of capacitors are significantly affected by ambient temperature and humidity, which makes it difficult for traditional fixed-parameter charge and discharge control strategies to adapt to complex and changing environments. When the ambient temperature rises, the equivalent series resistance of the capacitor decreases, the leakage current increases, the charging speed increases but the retention time shortens; when the temperature decreases, the equivalent series resistance increases and the charging speed slows down, affecting the timeliness of power-off information preservation. At the same time, changes in ambient humidity will affect the dielectric properties of capacitor materials, thereby changing their capacitance and leakage characteristics, which will affect the reliability of the power-off preservation strategy. Furthermore, if the impact of environmental changes on capacitor characteristics cannot be accurately estimated, it may lead to improper setting of charge and discharge control parameters, resulting in overcharge and overdischarge, and then insufficient time for power-off information preservation. In addition, the ambient temperature conditions are correlated with the capacitor characteristic parameters. How to calculate the capacitor discharge characteristics based on the correlation between the two is a step that needs to be solved, and the deviation between the calculated value and the actual situation is a problem that needs to be considered. Therefore, it is necessary to establish an adaptive system that can adapt to environmental changes in real time and dynamically optimize control parameters, and ensure the reliability of power-off information preservation through continuous parameter estimation and model calibration. Summary of the invention

[0003] The present invention provides a power supply control method for preventing power failure based on a capacitive liquid crystal screen, which mainly includes:

[0004] Obtain the temperature and humidity data of the current environment of the LCD screen, and determine the target capacitor material selection for the current environmental conditions by combining the current environmental parameters with the pre-established capacitor material type database for analysis;

[0005] Obtain the key parameters of the target capacitor material selection, such as rated voltage, rated capacity, equivalent series resistance and leakage current, and optimize the capacitor charging and discharging process through the preset capacitor compensation algorithm according to the current ambient temperature, and dynamically adjust the charging and discharging current and cut-off voltage to compensate for the capacitor characteristic deviation caused by temperature changes;

[0006] By analyzing the historical data of capacitor charging and discharging, a prediction model of environmental temperature and humidity and the number and time interval of capacitor charging and discharging is established. The charging and discharging behavior of the capacitor is predicted in real time according to the current environmental conditions. If the deviation between the predicted value and the measured value exceeds the preset threshold, it is judged as an abnormal situation and the power-off information storage strategy is dynamically adjusted.

[0007] Obtain the working current and voltage parameters of the LCD screen, and evaluate the charge and discharge status of the capacitor in combination with the current ambient temperature and humidity data. If the capacitor voltage exceeds the preset rated value, it is considered that there is a risk of overcharge and overdischarge, and the power-off information storage strategy is optimized dynamically according to the risk, reducing the charging current or terminating charging in advance;

[0008] According to the equivalent series resistance and leakage current parameters of the target capacitor material, a correlation model between the parameters and the ambient temperature and humidity is established, the capacitor charging and discharging characteristics under the current environmental conditions are calculated in real time, the estimated value is compared with the actual measured value, the deviation is calculated and the correlation model between the parameters and the ambient temperature and humidity is dynamically calibrated, and the power-off information storage strategy is continuously optimized according to the results of the correlation model;

[0009] The optimized power-off information preservation strategy is applied to the power-off information preservation process. By continuously monitoring the change rate of the ambient temperature and humidity, the change in the capacitor characteristics is judged based on the change rate data. If the temperature and humidity change rate exceeds the preset threshold, the power-off information preservation strategy is adjusted in time, and the capacitor charging and discharging control parameters are optimized based on the change in the capacitor characteristics of the judgment result.

[0010] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0011] The present invention discloses a power supply control method for preventing power failure based on a capacitive liquid crystal screen. The present invention obtains the temperature and humidity data of the current environment of the liquid crystal screen, combines with a pre-established capacitor material type database, determines the target capacitor material selection for the current environmental conditions, and obtains its key parameters. According to the current environmental temperature, the charging and discharging process of the capacitor is controlled and optimized by a preset capacitor compensation algorithm, and the charging and discharging current and cut-off voltage are dynamically adjusted to compensate for the capacitor characteristic deviation caused by temperature change. At the same time, the present invention analyzes the historical data of capacitor charging and discharging, establishes a prediction model of environmental temperature and humidity and the number and time interval of capacitor charging and discharging, predicts the charging and discharging behavior of the capacitor in real time, and dynamically adjusts the power failure information saving strategy. In addition, the present invention also establishes a correlation model of parameters and environmental temperature and humidity according to the equivalent series resistance and leakage current parameters of the target capacitor material, calculates the capacitor charging and discharging characteristics under the current environmental conditions in real time, compares the estimated value with the actual measured value, calculates the deviation and dynamically calibrates the correlation model, and continuously optimizes the power failure information saving strategy. Finally, the optimized power failure information saving strategy is applied to the power failure information saving process, and the power failure information saving strategy and capacitor charging and discharging control parameters are adjusted in time by continuously monitoring the rate of change of environmental temperature and humidity. The present invention can effectively improve the performance and reliability of the liquid crystal screen capacitor and extend its service life. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 The present invention is a flow chart of a power supply control method for preventing power failure based on a capacitive liquid crystal screen.

[0013] Figure 2 The present invention is a schematic diagram of a power supply control method for preventing power failure based on a capacitive liquid crystal screen.

[0014] Figure 3 It is another schematic diagram of a power supply control method for preventing power failure based on a capacitive liquid crystal screen according to the present invention. DETAILED DESCRIPTION

[0015] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] like Figure 1-3 In this embodiment, a power supply control method based on a capacitive liquid crystal screen to prevent power failure may specifically include:

[0017] S101, obtaining temperature and humidity data of the current environment of the LCD screen, and determining the target capacitor material selection for the current environmental conditions by combining and analyzing the current environmental parameters with a pre-established capacitor material type database.

[0018] Receive environmental parameter data collected by the temperature and humidity sensor array, and store the environmental parameter data into an environmental parameter monitoring table through a serial data bus, wherein the environmental parameter monitoring table includes a sampling timestamp and temperature and humidity values ​​at the sampling point; generate temperature time series data according to the environmental parameter monitoring table, and obtain a set of capacitor material numbers that meet the temperature time series data interval from a pre-established capacitor material database; for the capacitor material number set, calculate the humidity adaptability score using the overlap between the material moisture resistance interval and the environmental humidity, and if the temperature fluctuation standard deviation exceeds a preset threshold, perform a weighted correction on the temperature resistance interval; calculate the temperature adaptability score and humidity adaptability score of each material in the capacitor material number set through cosine similarity, generate a comprehensive score according to the weights of the temperature adaptability score and the humidity adaptability score, and obtain the capacitor material number with the highest score and write it into the capacitor material recommendation table.

[0019] Specifically, a temperature and humidity sensor array is used to arrange multi-point sampling nodes at the four corners and the center of the liquid crystal screen, and the ambient temperature and humidity values ​​are obtained according to the sampling frequency per minute. The sampling data is stored in the environmental parameter monitoring table through the serial data bus. The monitoring table record contains four fields: sampling timestamp, sampling point number, temperature value, and humidity value. The temperature time series data and humidity time series data are generated according to the environmental parameter monitoring table, and the capacitor parameter table is read from the pre-established capacitor material database. The capacitor parameter table contains the material number, the upper temperature resistance limit, the lower temperature resistance limit, the upper humidity resistance limit, the lower humidity resistance limit, and the dielectric constant field. The capacitor material number set that meets the current ambient temperature range is screened from the capacitor parameter table through the interval judgment operation. The humidity mean is calculated according to the humidity time series data in the environmental parameter monitoring table, and the humidity adaptability of the capacitor material number set is calculated by the humidity mean. The humidity adaptability calculation uses the percentage of overlap between the material humidity resistance range and the ambient humidity as the humidity adaptability score, and the temperature fluctuation standard deviation is calculated in combination with the temperature time series data. If the temperature fluctuation standard deviation exceeds the preset threshold, the temperature resistance range is weighted and corrected. The temperature adaptability score and humidity adaptability score of each material in the capacitor material number set are compared by cosine similarity calculation, and a comprehensive score is generated according to the weight of 0.6 for the temperature adaptability score and 0.4 for the humidity adaptability score. After sorting the comprehensive score, the capacitor material number with the highest score is obtained as the selection result, and the selection result is written into the capacitor material recommendation table. The temperature and humidity collection on the surface of the LCD screen needs to ensure the representativeness and accuracy of the data. The arrangement of temperature and humidity sensors at the four corners and the center point to form a sensor array can fully cover the screen area and avoid data deviation caused by single-point sampling. The temperature and humidity sensors transmit data through the serial data bus. When the sampling frequency is once per minute, it can meet the monitoring needs of environmental parameter changes. The temperature sampling range is -20 to +70 degrees Celsius, and the humidity sampling range is 0% to 95%. The temperature and humidity monitoring data recording format includes a 20-bit timestamp, a three-bit sampling point number, a four-bit temperature value, and a three-bit humidity value. The time series data is written into the environmental parameter monitoring table by appending. The performance of capacitor materials is significantly affected by the temperature and humidity environment. When selecting capacitor materials, it is necessary to comprehensively consider the temperature resistance and moisture resistance of the materials. The performance parameters of various capacitor materials are pre-stored in the capacitor material database. Each material contains a ten-digit material number, a four-digit upper temperature resistance limit, a four-digit lower temperature resistance limit, a three-digit upper humidity resistance limit, a three-digit lower humidity resistance limit, and a three-digit dielectric constant. The material performance parameters are obtained through experimental testing. The selection of capacitor materials is first preliminarily screened based on the temperature range. The screening conditions require that the upper and lower temperature resistance limits of the material completely cover the range of ambient temperature changes.The influence of environmental humidity on the performance of capacitor materials is that the dielectric constant changes with humidity. The humidity adaptability evaluation adopts the overlap calculation method. The overlap calculation is based on the overlap between the material's humidity resistance range and the environmental humidity. The overlap is highest when the environmental humidity mean is at the center of the material's humidity resistance range. When the environmental humidity mean deviates from the center of the range, the overlap decreases with the degree of deviation. When the temperature fluctuation exceeds five degrees Celsius, the temperature resistance range needs to be shrunk. The shrinkage amplitude is positively correlated with the standard deviation of the temperature fluctuation. Cosine similarity calculation is used to evaluate the matching degree of capacitor materials in terms of temperature and humidity comprehensive performance. By constructing the temperature adaptability score and humidity adaptability score of the material into a two-dimensional vector, the cosine value of the angle between the vector and the ideal state vector is calculated. The temperature adaptability score weight of 0.6 corresponds to the dominant position of the temperature factor in the LCD screen application scenario, and the humidity adaptability score weight of 0.4 reflects the secondary influence of the humidity factor. Finally, the material with the highest cosine similarity is selected as the recommended selection result. The capacitor material recommendation table record contains five fields: material number, temperature adaptability score, humidity adaptability score, comprehensive score, and recommended timestamp, which is convenient for subsequent query and evaluation.

[0020] S102. Obtain the key parameters of the target capacitor material selection, including the rated voltage, rated capacity, equivalent series resistance, and leakage current, and control and optimize the capacitor charging and discharging process through a preset capacitor compensation algorithm according to the current ambient temperature, and dynamically adjust the charging and discharging current and cut-off voltage to compensate for the capacitor characteristic deviation caused by temperature changes.

[0021] The rated voltage value, rated capacity value, rated charge and discharge current value, equivalent series resistance value, and temperature coefficient value are obtained from the capacitor parameter data table, and are written into the environmental parameter table according to the ambient temperature value and the leakage current value sampled by the data acquisition unit; the temperature compensation coefficient mapping table is queried according to the average temperature value in the environmental parameter table, and the upper limit value of the charging voltage after compensation is obtained by multiplying the temperature coefficient value by the temperature compensation coefficient; the leakage current value is predicted by using a Kalman filter, and the discharge voltage compensation coefficient is searched from the temperature compensation parameter table according to the predicted leakage current value to obtain the discharge cut-off voltage value after compensation; during the capacitor charging and discharging process, if the charging voltage reaches the constant current charging segment threshold, the charging current is reduced to the corresponding proportion of the rated value, and if the discharge voltage is reduced to the discharge cut-off voltage value after compensation, the discharge current is adjusted according to the capacitance drift.

[0022] Specifically, the target capacitance parameters are obtained from the capacitance parameter data table, including five parameter fields: rated voltage value, rated capacity value, rated charge and discharge current value, equivalent series resistance value, and temperature coefficient value. The current ambient temperature value and leakage current value are sampled once a second by the data acquisition unit. The sampled data is written into the environmental parameter table according to the three field structures of timestamp, temperature value, and leakage current value. The temperature average value is calculated every ten seconds as the temperature compensation calculation reference value. The temperature compensation coefficient mapping table is queried according to the temperature compensation calculation reference value. The temperature compensation coefficient changes piecewise linearly in the temperature range of -20 degrees to +80 degrees. Every 10 degrees of temperature corresponds to a fixed coefficient value. The upper limit value of the charging voltage after compensation is obtained by multiplying the temperature coefficient value in the capacitance parameter table with the temperature compensation coefficient. The charging process is divided into constant current charging section, current reduction charging section, and trickle charging section according to the charging voltage upper limit value of 20%, 50%, and 83% voltage points. The Kalman filter is used to predict the continuously sampled leakage current data, with the leakage current value as the state quantity and the temperature value as the observation quantity. The prediction interval is fixed to 30 seconds. According to the predicted leakage current change trend, the corresponding discharge voltage compensation coefficient is found from the temperature compensation parameter table. The compensation coefficient is multiplied by the rated discharge voltage value of the capacitor to obtain the discharge cut-off voltage value after compensation. At the same time, the capacitance drift is calculated by the leakage current prediction value as a reference for discharge process control. During the capacitor charging and discharging process, the sampling circuit records the voltage and current data at a frequency of 10 times per second. When the charging voltage reaches the constant current charging threshold, the charging current drops to 50% of the rated value, when it reaches the threshold of the current reduction charging threshold, the charging current drops to 20% of the rated value, and when it reaches the threshold of the trickle charging threshold, the charging current drops to 5% of the rated value. When the discharge voltage drops to the discharge cut-off voltage value after compensation, the discharge current is dynamically adjusted according to the capacitance drift, and the adjusted value is written into the control parameter table. The five basic parameters in the capacitor parameter data table constitute the basic description of the capacitor characteristics. Taking aluminum electrolytic capacitors as an example, the rated voltage value is usually in the range of 10 to 450 volts, the rated capacity value ranges from 0.1 microfarad to 47,000 microfarads, the rated charge and discharge current value is proportional to the capacity, the equivalent series resistance value decreases with the increase of capacity, and the temperature coefficient value represents the sensitivity of the capacitor parameters to temperature changes. The data acquisition unit ensures real-time monitoring of environmental parameter changes by sampling every second. The temperature average calculation period is set to 10 seconds to effectively filter out the impact of transient temperature fluctuations. The temperature compensation coefficient mapping table is established based on the capacitor temperature characteristic curve. The compensation coefficient is 1.4 at minus 20 degrees, and gradually decreases with increasing temperature. At 25 degrees, the compensation coefficient is 1. When the temperature continues to rise to 80 degrees, the compensation coefficient drops to 0.8. The compensation coefficient is multiplied by the temperature coefficient to obtain the actual compensation value, and then the upper limit of the charging voltage is determined. The charging process adopts three-stage control. Before the voltage reaches 20% of the upper limit, constant current charging is used to quickly increase the voltage. When it reaches 50%, it enters the current reduction stage to slow down the charging rate. After reaching 80%, trickle charging is used for fine adjustment.The Kalman filter predicts the leakage current change trend by fusing the leakage current measurement value and the temperature observation value. The leakage current is an important indicator for evaluating the performance of the capacitor. At an ambient temperature of 25 degrees, the leakage current usually does not exceed the rated value. The leakage current doubles for every 20 degrees increase in temperature. The leakage current trend calculated based on the 30-second prediction interval is used to dynamically adjust the discharge cut-off voltage. When the leakage current is predicted to rise, the cut-off voltage is appropriately increased to compensate for the polarization effect. When the leakage current is predicted to decrease, the cut-off voltage is correspondingly reduced. The 10 Hz sampling frequency of the sampling circuit meets the monitoring requirements of the charging and discharging process. When the aluminum electrolytic capacitor is charged, the rated charging current is applied in the constant current stage to quickly store energy. When the voltage reaches 20% of the upper limit, the current is reduced to half of the rated value to slow down the voltage rise rate. When it reaches 50%, the current is halved again to enter the fine adjustment stage. Finally, the charging is completed with a small current of 5% of the rated current. During the discharge process, the cut-off voltage is adjusted with the capacitance drift. When the drift increases by 1%, the cut-off voltage increases by 0.02 volts to avoid over-discharge affecting the capacitor life. The control parameter table records the voltage and current thresholds of each stage of charging and discharging, which is convenient for subsequent optimization of the control strategy.

[0023] S103. By analyzing the historical data of capacitor charging and discharging, a prediction model of ambient temperature and humidity and the number and time interval of capacitor charging and discharging is established. The charging and discharging behavior of the capacitor is predicted in real time according to the current environmental conditions. If the deviation between the predicted value and the measured value exceeds a preset threshold, it is judged as an abnormal situation and the power-off information storage strategy is dynamically adjusted.

[0024] A time series record containing a charge and discharge state mark is obtained from a historical database, a standard deviation of the temperature and humidity values ​​is calculated according to the time series record, and abnormal data points are eliminated by a threshold three times the standard deviation to obtain a first data set; a sliding time window is used to segment the first data set, and temperature and humidity statistics are calculated for the data segments to obtain a feature vector, wherein the feature vector includes a temperature mean, a humidity mean, and a charge and discharge ratio; a decision tree regressor is trained according to the feature vector to generate a prediction model, and the prediction model outputs a charge and discharge behavior prediction result after receiving a real-time temperature and humidity value input; a relative deviation value is calculated for the prediction result and the measured charge and discharge behavior, and an abnormality level is determined according to the relative deviation value, and a power-off information storage period is selected according to the abnormality level, and the power-off information is compressed and stored using Huffman coding.

[0025] Specifically, the capacitor charge and discharge records are read from the historical database, including six fields: timestamp, charge and discharge state mark, charge and discharge duration, ambient temperature value, ambient humidity value, and power-off time point. The temperature and humidity abnormal values ​​are eliminated by the triple standard deviation principle. The charge and discharge duration data is denoised based on wavelet transform. The continuous time series data is divided into fixed-length segments using a 15-minute sliding time window. Each segment generates a feature vector containing nine eigenvalues: temperature mean, temperature variance, humidity mean, humidity variance, number of charges, number of discharges, charge and discharge ratio, average charge duration, and average discharge duration. The decision tree regressor is trained based on the cleaned historical data. The input layer contains three features: temperature mean, humidity mean, and charge and discharge ratio. The output layer contains four values: predicted number of charges, predicted number of discharges, predicted charge duration, and predicted discharge duration. The decision tree depth is set to eight layers, the number of leaf nodes is set to 32, and the root mean square error is used as the splitting criterion. The training data is divided into a training set and a validation set in chronological order, with a ratio of four to one. The model is retrained every 42 hours. The current temperature and humidity values ​​are acquired every five minutes by a data collector, and the trained decision tree regressor is input to obtain the prediction results of the charge and discharge behavior. At the same time, the actual charge and discharge behavior data is recorded, and the abnormality is graded according to the relative deviation between the predicted value and the measured value. The deviation exceeds 10% and is recorded as a mild abnormality, exceeds 20% and is recorded as a moderate abnormality, and exceeds 30% and is recorded as a severe abnormality. The abnormal level and the corresponding timestamp, predicted value, measured value, and deviation value are written into the abnormality record table. The power-off information saving strategy is dynamically adjusted according to the abnormal record level. The power-off information is saved every 30 minutes in the mild abnormal state, every 15 minutes in the moderate abnormal state, and every 5 minutes in the severe abnormal state. The power-off information table contains three data blocks: system status mark, operating parameters, and key data. Huffman coding is used to compress the data, and the compression ratio is not less than 50%. The compressed power-off information package is stored in a fixed storage location in chronological order. The temperature and humidity records in the historical data of capacitor charging and discharging reflect the impact of environmental conditions on capacitor behavior. The standard deviation screening is based on the temperature fluctuations in the range of -20 to +80 degrees Celsius and the humidity changes in the range of 20% to 90%. Data outside this range is marked as outliers and removed. The wavelet transform denoises the charging and discharging time data, eliminates high-frequency noise through the fourth-order wavelet coefficients, and retains the key features of the original data. The 15-minute sliding window corresponds to the typical cycle of capacitor charging and discharging. The 9 eigenvalues ​​extracted in the window fully describe the working state of the capacitor.The decision tree regressor adopts a hierarchical prediction structure. The depth of the tree is set to eight layers, which reflects the fine division of the capacitor behavior characteristics. The number of leaf nodes is 32, which corresponds to the diversity of capacitor behavior under complex environmental conditions. The root mean square error is used as a splitting criterion to measure the degree of deviation between the predicted value and the actual value. The training data is divided in chronological order to ensure the adaptability of the prediction model to the latest data characteristics. The 24-hour retraining cycle matches the daily change law of the capacitor working environment. The five-minute sampling interval of the data collector corresponds to the minimum time scale of the capacitor charge and discharge state change. The deviation between the measured data and the predicted results reflects the stability of the capacitor working state. Mild anomalies are manifested as small fluctuations in the number or duration of charge and discharge. Moderate anomalies correspond to obvious changes in the charge and discharge mode. Severe anomalies are usually accompanied by significant degradation of capacitor performance. The abnormal record table associates the abnormal relationship between environmental conditions and capacitor behavior through timestamps. The power-off information preservation strategy adopts a graded response mechanism according to the abnormality level. The thirty-minute preservation interval under mild abnormality is suitable for the situation that the working state of the capacitor is basically stable. The fifteen-minute preservation interval under moderate abnormality encrypts the status monitoring frequency. The five-minute preservation interval under severe abnormality realizes real-time tracking of capacitor behavior. The system status mark contains information such as operating mode and alarm status. The operating parameters record environmental conditions and control variables. Key data saves capacitor working indicators. Huffman coding constructs the optimal coding tree based on the frequency of data occurrence, and uses short coding for high-frequency data to achieve a compression ratio of more than 50%. The compressed data packets are stored in timestamp order to facilitate fault backtracking and status recovery.

[0026] S104, obtaining the working current and voltage parameters of the LCD screen, combining the current environmental temperature and humidity data, evaluating the charge and discharge status of the capacitor, if the capacitor voltage exceeds the preset rated value, it is considered that there is a risk of overcharge and overdischarge, and dynamically optimizing the power-off information storage strategy according to the risk, reducing the charging current or terminating the charging early.

[0027] A current and voltage acquisition circuit is used to obtain real-time working current value and working voltage value from the power supply terminal of the liquid crystal screen, and the ambient temperature value and humidity value are obtained through a temperature and humidity sensor, and the timestamp and working parameters are recorded in an operation status record table; a sliding time window is constructed according to the voltage data in the operation status record table, and the voltage change trend in the future time period is obtained through exponential smoothing prediction, and the exponential smoothing prediction adopts a smoothing coefficient read from a temperature compensation coefficient table; the voltage change rate is calculated for the predicted voltage value and the measured voltage value, and the risk level is determined according to the voltage change rate, and the current limit correction coefficient is read from the temperature compensation coefficient table according to the risk level; the power-off information storage period is set according to the risk level, the charge and discharge control parameters are read from the control register, and the correction coefficient and the power-off protection parameter are written into the corresponding field of the control register to execute the power-off protection instruction.

[0028] Specifically, the current and voltage acquisition circuit is used to obtain the real-time working current value and working voltage value from the power supply end of the LCD screen at a sampling frequency of one thousand times per second. The current ambient temperature value and humidity value are collected once a second through the temperature and humidity sensor. The millisecond timestamp, working current value, working voltage value, temperature value, humidity value, and LCD screen switch state are written into the operation status record table. The operation status record table uses a circular storage structure to store the data of the last hour. A 30-second sliding time window is constructed based on the voltage data in the operation status record table. The voltage change trend within the next 10 seconds is calculated by exponential smoothing prediction. The smoothing coefficient in the calculation is read from the temperature compensation coefficient table according to the temperature value. The temperature compensation coefficient table records the temperature range and the corresponding compensation coefficient. If the predicted voltage value exceeds 110% of the rated voltage value, it is determined to be an overcharge risk. If it is lower than 90% of the rated voltage value, it is determined to be an over-discharge risk. Five fields, risk type, occurrence time, predicted voltage value, measured voltage value, and temperature value, are written into the risk record table. The voltage change rate is calculated by the predicted voltage value and the measured voltage value in the risk record table. The risk is divided into three levels according to the voltage change rate: mild risk, moderate risk, and severe risk. A change rate exceeding 1% is recorded as mild risk, exceeding 3% is recorded as moderate risk, and exceeding 5% is recorded as severe risk. The corresponding current limit correction coefficient is read from the temperature compensation coefficient table according to the risk level. The charge and discharge current limit is reduced to 70% of the original value when the risk is mild, 50% when the risk is moderate, and 30% when the risk is severe. The current charge and discharge control parameters are read from the control register address, and the power-off information saving cycle is set according to the risk level. The mild risk is saved once every minute, the moderate risk is saved once every thirty seconds, and the severe risk is saved once every ten seconds. The power-off information contains three data blocks: system status, operating parameters, and protection parameters. The size of each data block is fixed to one thousand bytes. The corrected charge and discharge limit is written into the control field with an address offset of zero through the control register, and the power-off protection parameter is written into the protection field with an offset of sixteen at the same time, and the power-off protection instruction is executed. The current and voltage acquisition performs high-frequency sampling and monitoring on the power supply end of the LCD screen. The sampling frequency of one thousand hertz ensures that the instantaneous fluctuations of voltage and current are captured. The temperature and humidity are sampled once a second to reflect the slowly changing characteristics of environmental parameters. The operation status record table uses a circular storage structure to record the data of the last hour to support the continuous monitoring of the charging and discharging status of the capacitor. The circular storage covers the earliest record in a fixed space to achieve dynamic updates. The voltage trend prediction uses a thirty-second observation window and a ten-second prediction interval. In actual applications, when the operating voltage of the LCD screen is 5 volts, the overcharge voltage threshold is set at 5.5 volts, and the over-discharge voltage threshold is set at 4.5 volts. The temperature compensation coefficient is adjusted with temperature changes. The compensation coefficient is 1.2 at minus 20 degrees, 1.0 at normal temperature 25 degrees, and drops to 0.8 at high temperature 80 degrees. The compensation coefficient corrects the prediction results to improve the prediction accuracy.The risk level classification is based on the voltage change rate. Under standard working conditions, the voltage change rate is maintained within 0.5%. When the external load suddenly changes or the capacitor performance degrades, the voltage change rate increases. Mild risk corresponds to slow voltage drift. The charge and discharge current limit is 70% of the rated value and still maintains normal operation. The moderate risk is characterized by rapid voltage changes. The current limit is 50% to significantly reduce the charge and discharge rate. The voltage fluctuates violently at severe risk, and the current limit is 30% to achieve quasi-shutdown protection. The control register accesses the hardware interface through address mapping. The charge and discharge control parameters are stored in the zero address segment, including three control words: current limit, voltage threshold, and protection delay. The power-off protection parameters are located in the sixteen address segments, recording the protection trigger conditions and response measures. Mild risk saves power-off information once a minute to meet basic backup requirements. Under moderate risk, the key status is encrypted and recorded in a thirty-second storage cycle. Under severe risk, the ten-second storage cycle achieves quasi-real-time status tracking. The system status data block records the operation mode and alarm information. The operation parameter data block stores environmental conditions and performance indicators. The protection parameter data block contains threshold settings and control strategies.

[0029] S105. According to the equivalent series resistance and leakage current parameters of the target capacitor material, a correlation model between the parameters and the ambient temperature and humidity is established, the capacitor charging and discharging characteristics under the current environmental conditions are calculated in real time, the estimated value is compared with the actual measured value, the deviation is calculated and the correlation model between the parameters and the ambient temperature and humidity is dynamically calibrated, and the power-off information storage strategy is continuously optimized according to the results of the correlation model.

[0030] A self-test circuit is used to obtain the equivalent series resistance value and leakage current value of the capacitor, and the temperature value and humidity value are synchronously collected through an environmental sensor; the temperature and humidity intervals are divided according to the temperature and humidity values, and the capacitor parameter estimation value is obtained through a parameter estimation model; a support vector regressor is used to train the data in the deviation record table to obtain the correction coefficient of the temperature and humidity interval; if the capacitor parameter offset exceeds a preset threshold, the data storage time interval is adjusted according to the temperature and humidity influence coefficient, and the temperature and humidity influence coefficient is determined by the temperature interval value and the humidity interval value.

[0031] Specifically, a self-check circuit is used to measure the equivalent series resistance value and leakage current value of the capacitor once every second, and the environmental temperature and humidity sensor synchronously collects the current temperature value and humidity value. A mapping relationship between environmental parameters and capacitor parameters is established through a linear regression model. The environmental parameters are divided into temperature intervals according to ten-degree intervals and humidity intervals according to 10% intervals. The parameter estimation model is trained based on historical collected data, and the mean and variance of the capacitor parameters in each temperature and humidity combination interval are recorded. The capacitor parameter estimation value corresponding to the current environmental temperature and humidity combination is calculated from the parameter estimation model, including two parameters: the equivalent series resistance estimation value and the leakage current estimation value. The actual capacitor parameters are measured through the acquisition circuit, and the relative error between the calculated estimated value and the measured value is compared. The six fields of timestamp, environmental temperature value, environmental humidity value, parameter estimation value, parameter measured value, and relative error value are written into the deviation record table, and the data of the deviation record table is cleaned once every hour. The support vector regressor is used to train the data in the deviation record table, and the corresponding relationship between the deviation and the environmental parameters is constructed. The training data selects the records within the last 24 hours, and the training data set is updated every four hours. The deviation correction table is generated to record the correction coefficient of each temperature and humidity interval. The prediction results of the parameter estimation model are calibrated by the correction coefficient to achieve dynamic optimization of the capacitance parameter estimation. The offset of the capacitance charge and discharge characteristics is calculated based on the calibrated parameter estimation value. The offset is calculated based on the percentage change of the equivalent series resistance value and the leakage current value. When the offset exceeds 5%, the power-off information is saved every 30 seconds, when it exceeds 10%, it is saved every 15 seconds, and when it exceeds 20%, it is saved every 5 seconds. The saving time interval is corrected by the temperature and humidity environmental impact coefficient. The saving interval is shortened by 20% for every ten-degree increase in temperature and 10% for every 10% increase in humidity. The self-check process of capacitor parameters needs to maintain a fixed measurement interval of one second, reflecting the temperature response characteristics of capacitor characteristics. Taking aluminum electrolytic capacitors as an example, the equivalent series resistance value is maintained in the range of 0.1 ohms to 1 ohm at room temperature of 25 degrees. The resistance value decreases by 5% to 8% for every 10 degrees increase in temperature, and increases by 6% to 10% for every 10 degrees decrease in temperature. The leakage current value is maintained within the rated value at room temperature, and the leakage current doubles for every 20 degrees increase in temperature. The linear regression model establishes a mapping relationship by recording the parameter change law under different temperature and humidity conditions. The parameter estimation model establishes parameter prediction rules for different temperature and humidity combination intervals. In actual applications, the temperature interval is divided into ten intervals from -20 degrees to +80 degrees, and the humidity interval is divided into seven intervals from 20% to 90%, forming 70 temperature and humidity combination intervals. Each interval records the statistical characteristics of capacitor parameters. The relative error between the estimated value and the measured value of the equivalent series resistance in the normal temperature and humidity interval is usually kept within 5%, and the error may rise to 15% under extreme temperature and humidity conditions. The deviation record table maintains the timeliness of the data through regular cleaning.The support vector regressor constructs a deviation correction model based on 24-hour historical data. The deviation correction focuses on the intervals where the temperature and humidity change dramatically. When the temperature rises or falls rapidly, the change of the capacitor parameters will lag behind, resulting in a large deviation between the estimated value and the measured value. The correction coefficient is obtained through statistical analysis of the historical deviation data. Different correction weights are used for different temperature and humidity intervals. The weight of the impact of temperature change on the capacitor parameters is usually greater than the weight of the impact of humidity change. The offset of the capacitor charge and discharge characteristics reflects the dynamic characteristics of the capacitor performance with environmental changes. Under standard working conditions, the changes in equivalent series resistance and leakage current are kept within 5%. The power-off information is saved every 30 seconds to meet the basic backup requirements. When the parameter offset exceeds 10%, the capacitor performance fluctuates significantly. The save interval is shortened to 15 seconds to encrypt and record the state change. When the offset reaches 20%, the capacitor is in a state of performance deterioration. The 5-second save interval ensures that key state changes are captured. The impact of ambient temperature and humidity is superimposed by the correction coefficient, and the save frequency is further increased under high temperature and high humidity conditions.

[0032] S106. Apply the optimized power-off information preservation strategy to the power-off information preservation process, and judge the change of capacitor characteristics according to the change rate data by continuously monitoring the change rate of ambient temperature and humidity. If the temperature and humidity change rate exceeds the preset threshold, adjust the power-off information preservation strategy in time, and optimize the capacitor charging and discharging control parameters according to the change of capacitor characteristics of the judgment result.

[0033] The temperature and humidity sensor data are collected and processed through five-point moving average filtering to obtain the temperature and humidity change rate, which is calculated based on the data at adjacent moments; a linear regression prediction is performed on the future temperature and humidity trends based on the temperature and humidity change rate, and if the temperature change rate exceeds a preset temperature threshold, or the humidity change rate exceeds a preset humidity threshold, an early warning record is generated; the power-off information saving parameters are adjusted according to the temperature and humidity change rates in the early warning record, and the power-off information saving parameters include a saving period and a storage capacity; the charge and discharge control parameter modification amount is obtained from the power-off information saving table through the power-off information saving parameter, and the charge and discharge control parameter modification amount is written into the control register address interval.

[0034] Specifically, the environmental data is collected once a second through the temperature and humidity sensor, and the temperature and humidity data are processed using a five-point moving average filter. The temperature change rate and humidity change rate are calculated based on the data at adjacent moments. The change rate is calculated by the temperature and humidity difference within a unit time. The processed data is written into the environmental change table according to the five fields of timestamp, temperature change value, humidity change value, temperature original value, and humidity original value. The environmental change table saves the data records of the last six hours. The change rate data of the last 30 minutes is read from the environmental change table, and the temperature and humidity change trend in the next five minutes is predicted by linear regression. If the temperature change rate exceeds 0.5 degrees per minute, or the humidity change rate exceeds 1% per minute, an early warning record is triggered, and the five fields of timestamp, change type, rate value, temperature prediction value, and humidity prediction value are written in the early warning record table. At the same time, the influence of the combined change of temperature and humidity on the capacitance parameters is calculated. According to the combined influence of the change rate and temperature and humidity in the early warning record table, the power-off information storage period is adjusted in stages. For every temperature change rate exceeding 0.1 degree per minute, the storage period is shortened by 10%, and for every humidity change rate exceeding 0.5% per minute, the storage period is shortened by 5%. The power-off information storage capacity is dynamically allocated according to the temperature and humidity compensation coefficient. For every 10 degree increase in temperature, the storage capacity is increased by 20%, and for every 10% increase in humidity, the storage capacity is increased by 10%. Read the storage strategy parameters from the power-off information storage table, and obtain the corresponding charge and discharge control parameter modification amount by table lookup. When the temperature changes drastically, the charging current limit is reduced to 60% of the rated value, and the discharge current limit is reduced to 70% of the rated value. When the humidity changes drastically, the charging current limit is reduced to 80% of the rated value, and the discharge current limit is reduced to 85% of the rated value. Write the modified control parameters to the control register address 0 to address 15 to perform charge and discharge process optimization. The acquisition and processing of temperature and humidity data directly affects the monitoring accuracy of capacitor parameter changes. The sampling frequency per second can capture environmental changes in a timely manner. The moving average filter eliminates random fluctuations by taking the average of five consecutive sampling points. In actual applications, when the air conditioner is started, the temperature may drop at a rate of 0.3 degrees per minute, and when the doors and windows are opened, the humidity may rise at a rate of 0.8% per minute. The environmental change table realizes continuous tracking of environmental change trends by saving six hours of data. The prediction of temperature and humidity change trends uses a thirty-minute observation window and a five-minute prediction interval. Linear regression fits the law of environmental parameter changes based on historical data. The threshold setting reflects the sensitivity of capacitor parameters to environmental changes. A temperature change of 0.5 degrees per minute corresponds to a rapid heating or cooling process, and a humidity change of 1% per minute corresponds to a sharp fluctuation in air humidity. The combined changes in temperature and humidity take into account extreme conditions such as high temperature and high humidity, low temperature and low humidity. The changes in capacitor parameters under these conditions are more significant.The dynamic adjustment of the power-off information preservation strategy reflects the graded response to environmental changes. The impact weight of temperature changes is greater than that of humidity changes, which is reflected in the adjustment ratio of the preservation period. A temperature change of 0.1 degrees per minute shortens the preservation period by 10%, while a humidity change of 0.5 degrees only shortens the preservation period by 5%. The dynamic allocation of storage capacity also follows a similar principle. Increasing storage capacity under high temperature and high humidity conditions ensures the complete preservation of key information. The adjustment of the charge and discharge control parameters adopts differentiated strategies for different environmental changes. When the temperature changes drastically, the current limit decreases more, reflecting the dominant influence of temperature on capacitor performance. The adjustment range of the charge current limit is greater than that of the discharge current limit, reflecting the focus on preventing overcharging risks. The control parameters are written to the register using a fixed address mapping method. The charge control parameters are stored in the range from address 0 to address 7, and the discharge control parameters are stored in the range from address 8 to address 15. Real-time adjustment of the capacitor charge and discharge process is achieved through the hardware interface.

[0035] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A power supply control method for preventing power failure based on a capacitive liquid crystal screen, characterized in that: The method comprises: Obtain the temperature and humidity data of the current environment of the LCD screen, and determine the target capacitor material selection for the current environmental conditions by combining the current environmental parameters with the pre-established capacitor material type database for analysis; Obtain the key parameters of the target capacitor material selection, such as rated voltage, rated capacity, equivalent series resistance and leakage current, and optimize the capacitor charging and discharging process through the preset capacitor compensation algorithm according to the current ambient temperature, and dynamically adjust the charging and discharging current and cut-off voltage to compensate for the capacitor characteristic deviation caused by temperature changes; By analyzing the historical data of capacitor charging and discharging, a prediction model of environmental temperature and humidity and the number and time interval of capacitor charging and discharging is established. The charging and discharging behavior of the capacitor is predicted in real time according to the current environmental conditions. If the deviation between the predicted value and the measured value exceeds the preset threshold, it is judged as an abnormal situation and the power-off information storage strategy is dynamically adjusted. Obtain the working current and voltage parameters of the LCD screen, and evaluate the charge and discharge status of the capacitor in combination with the current ambient temperature and humidity data. If the capacitor voltage exceeds the preset rated value, it is considered that there is a risk of overcharge and overdischarge, and the power-off information storage strategy is optimized dynamically according to the risk, reducing the charging current or terminating charging in advance; According to the equivalent series resistance and leakage current parameters of the target capacitor material, a correlation model between the parameters and the ambient temperature and humidity is established, the capacitor charging and discharging characteristics under the current environmental conditions are calculated in real time, the estimated value is compared with the actual measured value, the deviation is calculated and the correlation model between the parameters and the ambient temperature and humidity is dynamically calibrated, and the power-off information storage strategy is continuously optimized according to the results of the correlation model; The optimized power-off information preservation strategy is applied to the power-off information preservation process. By continuously monitoring the change rate of the ambient temperature and humidity, the change in the capacitor characteristics is judged based on the change rate data. If the temperature and humidity change rate exceeds the preset threshold, the power-off information preservation strategy is adjusted in time, and the capacitor charging and discharging control parameters are optimized based on the change in the capacitor characteristics of the judgment result.

2. The method according to claim 1, characterized in that: The method of obtaining the temperature and humidity data of the current environment of the LCD screen and determining the target capacitor material selection under the current environmental conditions by combining the current environmental parameters with a pre-established capacitor material type database for analysis includes: Receive environmental parameter data collected by the temperature and humidity sensor array, and store the environmental parameter data into an environmental parameter monitoring table through a serial data bus, wherein the environmental parameter monitoring table includes a sampling timestamp and temperature and humidity values ​​at the sampling point; Generate temperature time series data according to the environmental parameter monitoring table, and obtain a set of capacitor material numbers that meet the temperature time series data interval from a pre-established capacitor material database; For the capacitor material number set, the humidity adaptability score is calculated by using the overlap between the material humidity resistance range and the ambient humidity. If the temperature fluctuation standard deviation exceeds a preset threshold, a weighted correction is performed on the temperature resistance range. The temperature adaptability score and humidity adaptability score of each material in the capacitor material number set are calculated by cosine similarity, a comprehensive score is generated according to the weights of the temperature adaptability score and the humidity adaptability score, and the capacitor material number with the highest score is obtained and written into the capacitor material recommendation table.

3. The method according to claim 1, characterized in that The method of obtaining the rated voltage, rated capacity, equivalent series resistance and leakage current key parameters of the target capacitor material selection, and controlling and optimizing the charging and discharging process of the capacitor through a preset capacitor compensation algorithm according to the current ambient temperature, dynamically adjusting the charging and discharging current and cut-off voltage to compensate for the capacitor characteristic deviation caused by temperature change, includes: Obtain the rated voltage value, rated capacity value, rated charge and discharge current value, equivalent series resistance value and temperature coefficient value from the capacitor parameter data table, and write the ambient temperature value and leakage current value sampled by the data acquisition unit into the environmental parameter table; Querying a temperature compensation coefficient mapping table according to the average temperature value in the environmental parameter table, and obtaining a compensated charging voltage upper limit value by multiplying the temperature coefficient value by the temperature compensation coefficient; A Kalman filter is used to predict the leakage current value, and a discharge voltage compensation coefficient is searched from a temperature compensation parameter table according to the predicted leakage current value to obtain a discharge cut-off voltage value after compensation; During the capacitor charging and discharging process, if the charging voltage reaches the constant current charging threshold, the charging current drops to a corresponding proportion of the rated value; if the discharging voltage drops to the compensated discharge cut-off voltage value, the discharging current is adjusted according to the capacitance drift.

4. The method according to claim 1, characterized in that: The method analyzes the historical data of capacitor charge and discharge, establishes a prediction model of environmental temperature and humidity and the number and time interval of capacitor charge and discharge, and predicts the charge and discharge behavior of the capacitor in real time according to the current environmental conditions. If the deviation between the predicted value and the measured value exceeds a preset threshold, it is judged as an abnormal situation, and the power-off information storage strategy is dynamically adjusted, including: Obtaining a time series record containing a charge and discharge state mark in a historical database, calculating a standard deviation of the temperature and humidity values ​​according to the time series record, and eliminating abnormal data points using a threshold three times the standard deviation to obtain a first data set; The first data set is segmented by using a sliding time window, and temperature and humidity statistics are calculated for the data segments to obtain a feature vector, wherein the feature vector includes a temperature mean, a humidity mean and a charge-discharge ratio; A decision tree regressor is trained according to the feature vector to generate a prediction model, wherein the prediction model receives real-time temperature and humidity value input and outputs a charge and discharge behavior prediction result; A relative deviation value is calculated for the predicted result and the measured charge and discharge behavior, an abnormality level is determined according to the relative deviation value, a power-off information storage period is selected according to the abnormality level, and the power-off information is compressed and stored using Huffman coding.

5. The method according to claim 1, characterized in that: The operating current and voltage parameters of the LCD screen are obtained, and the charge and discharge state of the capacitor is evaluated in combination with the current ambient temperature and humidity data. If the capacitor voltage exceeds the preset rated value, it is considered that there is a risk of overcharge and overdischarge, and the power-off information storage strategy is dynamically optimized according to the risk, and the charging current is reduced or the charging is terminated in advance, including: The current and voltage acquisition circuit is used to obtain the real-time working current value and working voltage value from the power supply end of the LCD screen, the ambient temperature value and humidity value are obtained through the temperature and humidity sensor, and the timestamp and working parameters are recorded in the operation status record table; A sliding time window is constructed according to the voltage data in the operation status record table, and a voltage change trend in a future time period is obtained by exponential smoothing prediction, wherein the exponential smoothing prediction adopts a smoothing coefficient read from a temperature compensation coefficient table; Calculating a voltage change rate based on the predicted voltage value and the measured voltage value, determining a risk level based on the voltage change rate, and reading a current limit correction coefficient from a temperature compensation coefficient table based on the risk level; The power-off information storage period is set according to the risk level, the charge and discharge control parameters are read from the control register, and the correction coefficient and the power-off protection parameter are written into the corresponding field of the control register to execute the power-off protection instruction.

6. The method according to claim 1, characterized in that According to the equivalent series resistance and leakage current parameters of the target capacitor material, a correlation model between the parameters and the ambient temperature and humidity is established, the capacitor charge and discharge characteristics under the current environmental conditions are calculated in real time, the estimated value is compared with the actual measured value, the deviation is calculated and the correlation model between the parameters and the ambient temperature and humidity is dynamically calibrated, and the power-off information storage strategy is continuously optimized according to the result of the correlation model, including: A self-test circuit is used to obtain the equivalent series resistance and leakage current of the capacitor, and the temperature and humidity values ​​are synchronously collected through environmental sensors; Divide the temperature and humidity intervals according to the temperature value and the humidity value, and obtain the capacitance parameter estimation value through the parameter estimation model; The support vector regressor is used to train the data in the deviation record table to obtain the correction coefficient of the temperature and humidity range; If the capacitance parameter offset exceeds a preset threshold, the data storage time interval is adjusted according to the temperature and humidity influence coefficient, where the temperature and humidity influence coefficient is determined by the temperature interval value and the humidity interval value.

7. The method according to claim 1, characterized in that The optimized power-off information saving strategy is applied to the power-off information saving process, by continuously monitoring the change rate of the ambient temperature and humidity, judging the change of the capacitor characteristics according to the change rate data, and if the temperature and humidity change rate exceeds the preset threshold, timely adjusting the power-off information saving strategy, and optimizing the capacitor charging and discharging control parameters according to the change of the capacitor characteristics of the judgment result, including: The temperature and humidity sensor data is collected and processed by five-point moving average filtering to obtain the temperature and humidity change rate, which is calculated based on the data at adjacent moments; Performing linear regression prediction on future temperature and humidity trends based on the temperature and humidity change rates, and generating an early warning record if the temperature change rate exceeds a preset temperature threshold, or the humidity change rate exceeds a preset humidity threshold; Adjust the power-off information storage parameters according to the temperature and humidity change rate in the early warning record, wherein the power-off information storage parameters include a storage period and a storage capacity; The charge and discharge control parameter modification amount is obtained from the power-off information storage table through the power-off information storage parameter, and the charge and discharge control parameter modification amount is written into the control register address interval.

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