Liquid crystal screen data optimization management system and method based on big data analysis

Abstract

The application discloses a liquid crystal screen data optimization management system and method based on big data analysis, and relates to the technical field of electric digital data processing. The liquid crystal screen data optimization management system based on big data analysis comprises a standby power consumption data acquisition management module, a sleep power consumption data acquisition management module and a power consumption data storage management module. The standby power consumption data acquisition management module is used for judging whether a sleep mode switching instruction is sent according to the standby power consumption data acquisition influence result, if yes, the sleep power consumption data acquisition influence result is obtained, otherwise, the standby power consumption data acquisition optimization management is executed, and finally, whether the power consumption data uploading storage management is executed is judged according to the obtained sleep power consumption data acquisition influence result, so that the liquid crystal screen power consumption data management is more accurate, and the problem that the influence of the switch from the standby mode to the sleep mode is neglected when the power consumption data of the liquid crystal screen is managed in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of electronic digital data processing technology, and in particular to a data optimization management system and method for LCD screens based on big data analysis. Background Technology

[0002] In modern display technology, liquid crystal displays (LCDs) are widely used in televisions, monitors, mobile phones, tablets, and many other fields. With increasing focus on energy efficiency, the power consumption of LCDs has become a key performance indicator. Especially in standby and sleep modes, the power consumption of LCDs directly affects their energy efficiency and environmental performance. Therefore, collecting and managing power consumption data in standby and sleep modes is of great significance during the factory quality inspection of LCDs.

[0003] In the production process of LCD screens, rigorously testing power consumption in standby and sleep modes and acquiring power consumption data is a crucial component of quality control. Existing technology manages power consumption testing in standby mode by sending a standby command to the LCD screen via a test script to switch to standby mode. Then, a power consumption testing instrument is connected to the LCD screen's power input to ensure real-time measurement of current, voltage, and power consumption. The instrument is then activated and begins recording the power consumption of the LCD screen in standby mode. Based on the power consumption data obtained, the performance of the LCD screen in standby mode is checked to see if it meets predetermined power consumption requirements, thus achieving power consumption management in standby mode. In contrast to standby mode, existing technology sends specific sleep commands to the LCD screen via control scripts or commands to switch it from standby to sleep mode. After the LCD screen enters sleep mode, the power consumption testing instrument is activated to record the power consumption of the LCD screen in sleep mode. The power consumption data recorded by the testing instrument is acquired, and based on this data, the power consumption performance of the LCD screen in sleep mode is compared with industry standards or product design requirements to check whether the power consumption of the LCD screen meets relevant energy efficiency standards, thus achieving power consumption management in sleep mode.

[0004] For example, Chinese invention patent CN116306010B discloses a method and system for analyzing the power consumption data characteristics of an amplifier, including: determining the power consumption factor; obtaining the resistance value of the load network; generating an amplifier circuit with low power consumption characteristics; simulating the amplifier using multiple amplifier parameter evaluation tools based on the low power consumption characteristics amplifier circuit to obtain amplifier simulation data; evaluating the feasibility of the amplifier power consumption data characteristics based on the amplifier simulation data; analyzing the amplifier power consumption data characteristics according to the feasibility to obtain characteristic analysis results; and optimizing the amplifier power consumption based on the characteristic analysis results.

[0005] For example, Chinese invention patent CN118917084B discloses a data optimization management system and method for LCD screens based on big data analysis, including: analyzing the correlation between the operating parameters of the LCD screen and the target LCD color deviation parameters to obtain relevant data on the operating parameters of the LCD screen; estimating the model parameters of the LCD screen in the ARIMA model using the least squares method, and constructing an LCD screen color deviation optimization model based on the obtained target LCD screen performance reference data; predicting the operating conditions of the LCD screen to obtain LCD screen color deviation prediction data, calibrating and optimizing the LCD screen color deviation prediction data to obtain target LCD screen color deviation prediction data; obtaining the target LCD screen color deviation prediction data, obtaining the color calibration data of the LCD screen, optimizing the color display of the LCD screen based on the color calibration data, and uploading the relevant data to a cloud server.

[0006] The above-mentioned technology has at least the following technical problems:

[0007] Many LCD screens are equipped with automatic brightness adjustment, which adjusts the backlight brightness automatically based on ambient light levels. In this case, changes in ambient light cause real-time changes in the LCD screen's backlight brightness, resulting in fluctuations in power consumption and affecting the stability of power consumption data. Because of this automatic backlight brightness adjustment, the LCD screen's power consumption fluctuates with changes in ambient light.

[0008] During the transition from standby mode to sleep mode, the operating state of the LCD screen changes, particularly the power supply voltage and power consumption mode, which experience significant fluctuations, thus affecting the accuracy of power consumption data acquisition. Current technology overlooks the impact of the LCD screen's transition from standby to sleep mode when managing and testing its power consumption data. Summary of the Invention

[0009] To address the technical problem in existing technologies that neglect the impact of switching from standby mode to sleep mode when managing and testing the power consumption data of LCD screens, this invention provides an LCD screen data optimization management system and method based on big data analysis. The technical solution is as follows:

[0010] On one hand, a data optimization and management system for LCD screens based on big data analysis is provided, including: a standby power consumption data acquisition and management module, a sleep power consumption data acquisition and management module, and a power consumption data storage and management module; wherein, the standby power consumption data acquisition and management module is used to perform standby power consumption data acquisition and management on a preset batch of LCD screens in standby mode, and obtain the impact results of standby power consumption data acquisition to determine whether to send a sleep mode switching command to the sleep power consumption data acquisition and management module; the sleep power consumption data acquisition and management module is used to perform sleep power consumption data acquisition and management on the preset batch of LCD screens after switching to sleep mode if a sleep mode switching command is received, and obtain sleep power consumption data to characterize the impact of voltage conversion fluctuation interference during the sleep power consumption data acquisition process. The data acquisition impacts the results. If a standby power consumption data acquisition optimization management instruction is received, then standby power consumption data acquisition optimization management is executed. This means optimizing the backlight brightness adjustment interference during standby power consumption data acquisition, as well as filtering and compressing the acquired standby power consumption data. The power consumption data storage management module is used to determine whether to execute power consumption data upload and storage management based on the obtained sleep power consumption data acquisition impact results. If yes, power consumption data upload and storage management is executed; otherwise, sleep power consumption data acquisition optimization management is executed. This means optimizing the voltage conversion fluctuation interference during sleep power consumption data acquisition, as well as storing and managing the acquired sleep power consumption data.

[0011] On the other hand, a data optimization management method for LCD screens based on big data analysis is provided. This method includes the following steps: In standby mode, standby power consumption data collection and management is performed on a preset batch of LCD screens to obtain the impact results of standby power consumption data collection to determine whether to send a sleep mode switching command; if a sleep mode switching command is received, sleep power consumption data collection and management is performed on the preset batch of LCD screens after switching to sleep mode to obtain sleep power consumption data collection impact results characterizing the impact of voltage conversion fluctuations on the sleep power consumption data collection process; if a standby power consumption data collection optimization management command is received, standby power consumption data collection optimization management is executed, whereby the standby power consumption data collection optimization management optimizes the backlight brightness adjustment interference during standby power consumption data collection, and performs filtering and compression processing on the collected standby power consumption data; based on the obtained sleep power consumption data collection impact results, it is determined whether to execute power consumption data upload and storage management. If yes, power consumption data upload and storage management is executed; otherwise, sleep power consumption data collection optimization management is executed, whereby the sleep power consumption data collection optimization management optimizes the voltage conversion fluctuation interference during sleep power consumption data collection, and performs storage management on the collected sleep power consumption data.

[0012] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0013] 1. During the power consumption test of the LCD screen to manage the collected power consumption data, voltage fluctuations during the switch from standby mode to sleep mode cause low reliability of power consumption data acquisition. This application determines whether to send a sleep mode switching command based on the impact results of standby power consumption data acquisition. If sent, the impact results of sleep power consumption data acquisition are obtained; otherwise, standby power consumption data acquisition optimization management is performed. Standby power consumption data acquisition optimization management helps reduce the interference of backlight brightness adjustment during standby power consumption data acquisition. Finally, based on the obtained impact results of sleep power consumption data acquisition, it determines whether to perform power consumption data upload and storage management. Power consumption data upload and storage management helps improve the accuracy of storing and managing the collected sleep power consumption data, achieving more accurate management of LCD screen power consumption data and solving the problem in the prior art of ignoring the impact of switching from standby mode to sleep mode when managing and testing the power consumption data of the LCD screen.

[0014] 2. By determining whether the standby power consumption acquisition impact score is not greater than the acquisition impact limit score, the standby power consumption data acquisition impact result is obtained. This helps to more accurately determine whether the standby power consumption data acquisition process is affected by backlight brightness adjustment interference. When the standby power consumption data acquisition impact result is abnormal, the sleep mode switching command is not sent, and standby power consumption data acquisition optimization management is executed. Standby power consumption data acquisition optimization management effectively suppresses the impact of backlight brightness adjustment interference. At the same time, the acquired standby power consumption data is filtered and compressed, which helps to further improve the reliability of the acquired power consumption data storage and management, and realizes more accurate management of LCD screen power consumption data in standby mode.

[0015] 3. By determining whether the impact score of sleep power consumption acquisition is within the preset switching impact limit, if so, the corresponding sleep power consumption data acquisition impact result is recorded as controllable switching impact, and power consumption data upload and storage management is executed. This helps to more accurately judge the interference caused by voltage fluctuations during the switching process. Otherwise, the corresponding sleep power consumption data acquisition impact result is recorded as abnormal switching impact, and sleep power consumption data acquisition optimization management is executed. This helps to improve the reliability of power consumption data acquisition during the switching of the LCD screen from standby mode to sleep mode, and achieves more accurate management of power consumption data during the switching of the LCD screen from standby mode to sleep mode. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the structure of a liquid crystal screen data optimization management system based on big data analysis, provided in an embodiment of the present invention.

[0018] Figure 2 A flowchart illustrating the data acquisition impact suppression optimization process of a big data analysis-based LCD screen data optimization management system is provided in this embodiment of the invention.

[0019] Figure 3 A flowchart for obtaining the impact results of sleep power consumption data collection in an LCD screen data optimization management system based on big data analysis is provided for an embodiment of the present invention.

[0020] Figure 4 This is one of the standby power consumption data management interface diagrams of a liquid crystal screen data optimization management system based on big data analysis provided in an embodiment of the present invention;

[0021] Figure 5 This is one of the sleep power consumption data management interface diagrams of a liquid crystal screen data optimization management system based on big data analysis provided in an embodiment of the present invention;

[0022] Figure 6 A flowchart illustrating a data optimization and management method for LCD screens based on big data analysis, provided as an embodiment of the present invention. Detailed Implementation

[0023] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0024] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0025] This invention provides a data optimization management system and method for LCD screens based on big data analysis. This solves the problem in existing technologies where the impact of switching from standby mode to sleep mode is ignored when managing and testing the power consumption data of LCD screens. By performing standby power consumption data collection and management on a preset batch of LCD screens in standby mode, the impact of standby power consumption data collection is obtained to determine whether to send a sleep mode switching command. If a sleep mode switching command is received, sleep power consumption data collection and management is performed on the preset batch of LCD screens after switching to sleep mode, and the impact of sleep power consumption data collection on voltage conversion fluctuations is obtained. Otherwise, standby power consumption data collection optimization management is performed. Finally, based on the obtained sleep power consumption data collection impact results, it is determined whether to perform power consumption data upload and storage management. If yes, power consumption data upload and storage management is performed; otherwise, sleep power consumption data collection optimization management is performed, achieving more accurate management of LCD screen power consumption data.

[0026] The technical solution in this embodiment of the invention addresses the problem of neglecting the impact of switching from standby mode to sleep mode when managing and testing the power consumption data of the LCD screen. The overall approach is as follows:

[0027] The system determines whether to send a sleep mode switching command by collecting standby power consumption data. If so, it obtains the sleep power consumption data collection results; otherwise, it performs standby power consumption data collection optimization management. Finally, it combines the obtained sleep power consumption data collection results to determine whether to perform power consumption data upload and storage management, thus achieving more accurate management of LCD screen power consumption data.

[0028] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0029] like Figure 1 The diagram shown is a structural schematic of a big data analysis-based LCD screen data optimization management system provided in an embodiment of the present invention. The big data analysis-based LCD screen data optimization management system provided in an embodiment of the present invention includes: a standby power consumption data acquisition and management module, a sleep power consumption data acquisition and management module, and a power consumption data storage and management module.

[0030] Specifically, the first module of this application is a standby power consumption data acquisition and management module. This module is used to collect and manage standby power consumption data of a preset batch of LCD screens in standby mode. By collecting standby power consumption data during the LCD screen production and testing phase, the power consumption performance of the LCD screen in standby mode is effectively revealed. Based on the results of the standby power consumption data acquisition, it is determined whether to send a sleep mode switching command to the sleep power consumption data acquisition and management module. The power consumption data corresponding to standby mode and sleep mode can provide a comprehensive view of the LCD screen power consumption data, which helps to more clearly understand the power consumption difference between standby mode and sleep mode, as well as the power consumption level of the LCD screen in the corresponding mode.

[0031] It should be noted that when designing a big data analytics-based LCD screen data optimization management system, a dedicated database for storing core configuration information was first created. This database contains various reference values ​​necessary for system operation, such as backlight brightness and backlight current reference values. These initial settings are not arbitrarily assigned but calculated using a summation and averaging method based on a large amount of accumulated measured data in the database, making the initial settings more objective and reflecting general conditions. Of course, considering the complex and ever-changing real-world application environment and potential new problems during system debugging, these values ​​in the database are not fixed. Technicians can manually set, adjust, or fine-tune them at any time based on the system's performance in actual testing, ensuring continuous system optimization to achieve optimal operating conditions.

[0032] As a further solution, the standby power consumption data acquisition impact results are used to measure the degree of influence of backlight brightness adjustment interference on the standby power consumption data acquisition process, including whether the acquisition impact limit is qualified and whether the acquisition impact limit is abnormal. The specific acquisition steps are as follows:

[0033] First, the data collection impact quantification indicators are obtained during the standby power consumption data collection and management process of a preset batch of LCD screens. These indicators include a backlight adjustment brightness indicator, which characterizes the difference between the backlight brightness and the backlight brightness reference value, and a backlight adjustment current indicator, which characterizes the difference between the backlight current and the backlight current reference value. The backlight adjustment brightness indicator represents the result of relative deviation processing of the backlight brightness and the backlight brightness reference value, i.e., the absolute value of the difference between the backlight brightness and the backlight brightness reference value is used to calculate the ratio with the backlight brightness reference value. The backlight brightness is obtained through the API (Application Programming Interface) provided by the LCD screen. The backlight adjustment current indicator represents the result of relative deviation processing of the backlight current and the backlight current reference value, i.e., the absolute value of the difference between the backlight current and the backlight current reference value is used to calculate the ratio with the backlight current reference value. The backlight current is obtained through a current sensor deployed at the output end of the backlight power line.

[0034] Next, the results of weighting the collected impact quantification index and the adjustment interference impact compensation value obtained from the preset database are harmonic averaged to obtain the backlight adjustment impact factor, which is T in the numerical expression of the standby power consumption collection impact score. The adjustment interference impact compensation value is used to describe the degree of influence of the collected impact quantification index on the backlight adjustment impact factor, including the adjustment brightness impact compensation value and the adjustment current impact compensation value.

[0035] Secondly, the maximum and minimum standby power consumption data are processed to obtain the standby power consumption data deviation ratio, which is the numerical expression of the influence score of standby power consumption acquisition. In this section, the deviation percentage of the obtained standby power consumption data and the backlight adjustment influence factor are interactively processed (i.e., the two independent variables are multiplied to represent their mutual influence) to obtain the standby power consumption acquisition influence score, which quantifies the degree of influence of backlight brightness adjustment interference on the standby power consumption data acquisition process; the numerical expression of the standby power consumption acquisition influence score is as follows:

[0036] ;

[0037] In the formula, D represents the standby power consumption sampling impact score, T represents the backlight adjustment impact factor, and P... max P represents the maximum standby power consumption data. min P represents the minimum standby power consumption data. y This indicates the reference fluctuation value for standby power consumption.

[0038] It's important to understand that the standby power consumption sampling impact score incorporates multiple parameters, which are interconnected and influence each other. Specifically, as the backlight adjustment impact factor increases, the fluctuation amplitude of the standby power consumption data deviates more from the reference fluctuation value, and the percentage of deviation in the standby power consumption data increases accordingly. This indicates a greater degree of interference from backlight brightness adjustment during the standby power consumption data sampling process, thus increasing the standby power consumption sampling impact score. By quantifying the mutual influence between these parameters, it's important to understand that while the backlight current is typically stable in standby mode, fluctuations in the backlight current will increase the fluctuation amplitude of the power consumption data, further increasing the standby power consumption sampling impact score. Furthermore, the backlight current refers to the current used to drive the backlight. A higher backlight current results in higher power consumption for the LCD screen, and changes in the backlight current directly affect the screen's power consumption level. In standby mode, fluctuations in the backlight current lead to fluctuations in the power consumption data. For example, if the backlight current has a certain range of variation, the LCD screen's power consumption may fluctuate over a period of time, increasing the fluctuation amplitude of the power consumption data, thus enhancing backlight brightness adjustment interference during standby power consumption data sampling.

[0039] The obtained standby power consumption sampling impact score is compared with the preset sampling impact limit score obtained from the preset database; it is determined whether the standby power consumption sampling impact score is not greater than the sampling impact limit score. If so, the corresponding standby power consumption data sampling impact result is recorded as sampling impact limit qualified, and a sleep mode switching command is sent to switch the LCD screen from standby mode to sleep mode, and the sleep power consumption data sampling impact result is obtained.

[0040] Otherwise, the corresponding standby power consumption data acquisition impact result will be recorded as acquisition impact limitation abnormality, the sleep mode switching command will not be sent, the standby power consumption data acquisition optimization management command will be sent and the standby power consumption data acquisition optimization management will be executed. The standby power consumption data acquisition optimization management includes acquisition impact suppression optimization and data acquisition optimization management.

[0041] like Figure 2 The diagram shows a flowchart of the data acquisition impact suppression optimization process of a big data analysis-based LCD screen data optimization management system provided in an embodiment of the present invention. The corresponding logic is as follows: obtain the PWM optimization frequency and screen optimization refresh rate based on the standby power consumption acquisition impact score, update the initial PWM frequency to the PWM optimization frequency and the initial screen refresh rate to the screen optimization refresh rate, determine whether the re-acquired standby power consumption data acquisition impact result is within the acquisition impact limit, if so, send the acquisition impact suppression optimization end command and switch to sleep mode, otherwise, restart from obtaining the standby power consumption acquisition impact score until the data acquisition optimization management command sending condition is triggered.

[0042] Specifically, the impact suppression optimization refers to updating the PWM (Pulse Width Modulation) adjustment frequency and screen refresh rate during the standby power consumption data acquisition management process of the LCD screen based on the standby power consumption data acquisition results, in order to suppress the interference of backlight brightness adjustment during the standby power consumption data acquisition process; (Reference) Figure 2 The specific process for optimizing the suppression of data collection impact is as follows:

[0043] A1 updates the initial PWM frequency during the standby power consumption data acquisition and management process of the LCD screen to the optimized PWM frequency. The optimized PWM frequency represents the result of mapping the backlight adjustment influence factor in the standby power consumption acquisition influence score to a pre-built mapping set of backlight adjustment influence factors and PWM optimized frequencies in a preset database. Specifically, as the backlight adjustment influence factor increases, the optimized PWM frequency decreases. Under high-frequency PWM, the backlight brightness changes frequently, easily generating noise, which can cause resonance or mutual interference during the standby power consumption data acquisition process, affecting the stability and accuracy of the standby power consumption data. Lowering the initial PWM frequency helps to smooth out the backlight adjustment, reducing frequent power consumption fluctuations in a short period, thereby reducing interference from backlight adjustment during data acquisition. By reducing this high-frequency interference, more accurate and stable power consumption data can be obtained, avoiding the impact of backlight adjustment noise on the data acquisition results.

[0044] A2 inputs the standby power consumption impact score into a pre-built mapping set between the standby power consumption impact score and the screen optimized refresh rate in a preset database to obtain the screen optimized refresh rate. The initial screen refresh rate is then iteratively updated based on the obtained screen optimized refresh rate; specifically, as the standby power consumption impact score increases, the screen optimized refresh rate decreases. In standby mode, if the screen refresh rate is low, the image update frequency decreases, and the resources required by the LCD screen's processor and backlight system are relatively less.

[0045] A3: Re-perform standby power consumption data acquisition and management for the preset batch of LCD screens. Determine whether the newly acquired standby power consumption data acquisition impact result is within the acceptable range. If so, send an acquisition impact suppression optimization end command and switch to sleep mode. Otherwise, repeat A1, A2, and A3 until the data acquisition optimization management command sending condition is triggered. The data acquisition optimization management command sending condition includes PWM adjustment frequency limit condition and screen refresh rate limit condition. The PWM adjustment frequency limit condition means that the PWM adjustment optimization frequency is not less than the preset PWM frequency adjustment limit value. The screen refresh rate limit condition means that the screen optimization refresh rate is not less than the preset screen refresh rate limit value.

[0046] Among them, data acquisition optimization management refers to filtering and compressing the standby power consumption data during the standby power consumption data acquisition and management process of the LCD screen, based on the impact results of standby power consumption data acquisition, in order to improve the accuracy of standby power consumption data during the standby power consumption data acquisition and management process of the LCD screen; the specific process of data acquisition optimization management is as follows:

[0047] B1. The standby power consumption impact score is input into the corresponding linear regression models for adjusting the acquisition window and the acquisition frequency, respectively, which output the acquisition window optimization strength and acquisition frequency optimization strength. Based on the obtained acquisition window optimization strength and acquisition frequency optimization strength, the initial acquisition window length and initial acquisition frequency are updated to obtain the acquisition optimization window length and acquisition optimization frequency, respectively. The acquisition window adjustment linear regression model is a pre-trained linear regression model used to fit the mapping relationship between the standby power consumption impact score and the acquisition window optimization strength. This is achieved by using the standby power consumption impact score from historical time periods and the preset staff's assessment of the standby power consumption impact score. The set acquisition window optimization intensity is input into the linear regression model, and the corresponding acquisition window adjustment linear regression model is obtained by training it using the scikit-learn framework based on the least squares method. The acquisition frequency adjustment linear regression model is a pre-trained linear regression model used to fit the mapping relationship between the standby power consumption acquisition influence score and the acquisition frequency optimization intensity. The standby power consumption acquisition influence score in the historical time period and the acquisition frequency optimization intensity set by the preset staff based on the standby power consumption acquisition influence score are input into the linear regression model, and the corresponding acquisition frequency adjustment linear regression model is obtained by training it using the scikit-learn framework based on the least squares method.

[0048] B2. Based on the obtained acquisition optimization window length and acquisition optimization frequency, the standby power consumption data of the preset batch of LCD screens is re-acquired a preset number of times to obtain standby detection power consumption data and obtain the corresponding standby power consumption data acquisition impact results.

[0049] B3. Determine whether the results of the reacquisition of the corresponding standby power consumption data acquisition are all within the acceptable range. If so, acquire the results of the sleep power consumption data acquisition; otherwise, proceed to B4.

[0050] B4. The standby power consumption impact scores re-acquired during a preset number of acquisitions are harmonicly averaged to obtain an impact score adjustment coefficient. This coefficient is then input into the corresponding outputs of the data filtering and compression linear regression models: the data filtering optimization strength and the data compression optimization strength. Based on the obtained data filtering optimization strength, the standby power consumption data is filtered, and then compressed using the data compression optimization strength before being uploaded to the cloud server. The data filtering linear regression model is a pre-trained model used to fit the mapping relationship between the impact score adjustment coefficient and the data filtering optimization strength. This model is established by adjusting the impact score within historical time periods and using a preset adjustment system based on the impact score. The set data filtering optimization intensity is input into the linear regression model, and the corresponding data filtering linear regression model is obtained by training it using the scikit-learn framework based on the least squares method. The input is the collection impact score adjustment coefficient within the historical time period. The data compression processing linear regression model is a pre-trained linear regression model used to fit the mapping relationship between the collection impact score adjustment coefficient and the data compression optimization intensity. Here, the input is the collection impact score adjustment coefficient within the historical time period. By inputting the collection impact score adjustment coefficient within the historical time period and the data compression optimization intensity set by the preset staff based on the collection impact score adjustment coefficient into the linear regression model, the corresponding data compression processing linear regression model is obtained by training it using the scikit-learn framework based on the least squares method.

[0051] As a further option, such as Figure 3 The diagram shown is a flowchart of the process for obtaining the impact result of sleep power consumption data acquisition in a big data-based LCD screen data optimization management system according to an embodiment of the present invention. The corresponding logic is as follows: Based on the switching impact management data, the influence of voltage conversion fluctuation interference on the sleep power consumption data acquisition process is quantitatively judged to obtain the sleep power consumption acquisition impact score. It is determined whether the sleep power consumption acquisition impact score is within the preset switching impact limit range. If so, the corresponding sleep power consumption data acquisition impact result is recorded as controllable acquisition switching impact, and power consumption data upload and storage management is executed. Otherwise, the corresponding sleep power consumption data acquisition impact result is recorded as abnormal acquisition switching impact, and sleep power consumption data acquisition optimization management is executed.

[0052] refer to Figure 3 The specific process for obtaining the impact of sleep power consumption data collection results is as follows:

[0053] Q1. Obtain the switching impact management data of the LCD screen during the process of switching from standby mode to sleep mode. The switching impact management data includes voltage fluctuation amplitude, voltage fluctuation frequency, maximum sleep power consumption data and minimum sleep power consumption data. Obtain the voltage fluctuation amplitude and voltage fluctuation frequency by using an oscilloscope deployed at the power input terminal of the LCD screen, and obtain the maximum sleep power consumption data and minimum sleep power consumption data by using a power meter deployed at the power input terminal of the LCD screen.

[0054] Q2, based on the handover impact management data, quantitatively determine the degree of influence of voltage conversion fluctuations on the sleep power consumption data acquisition process, and obtain the sleep power consumption acquisition impact score; specifically, the steps to obtain the sleep power consumption acquisition impact score are as follows:

[0055] Q21. The voltage fluctuation amplitude and voltage fluctuation frequency are respectively proportionally processed by dividing the voltage fluctuation amplitude threshold and voltage fluctuation frequency threshold obtained from the preset database to obtain the corresponding voltage fluctuation amplitude index and voltage fluctuation frequency index.

[0056] Q22, the voltage conversion fluctuation weighting results are harmonic averaged to obtain the voltage conversion influence factor; the voltage conversion fluctuation weighting results represent the coupling results after the voltage fluctuation amplitude index, voltage fluctuation frequency index and the conversion fluctuation compensation value obtained from the preset database are weighted respectively. The conversion fluctuation compensation value includes fluctuation amplitude compensation value and fluctuation frequency compensation value, which are used to describe the degree of influence of the voltage fluctuation amplitude index and voltage fluctuation frequency index on the voltage conversion influence factor.

[0057] Q23, the acquired maximum and minimum sleep power consumption data are subjected to deviation ratio processing to obtain the sleep power consumption data deviation ratio result. The sleep power consumption data deviation ratio result is the numerical expression of the sleep power consumption acquisition influence score. In this section, the deviation percentage of the obtained sleep power consumption data and the voltage conversion influence factor are interactively processed to obtain the sleep power consumption acquisition influence score, which quantifies the degree of influence of voltage conversion fluctuations on the sleep power consumption data acquisition process. The numerical expression of the sleep power consumption acquisition influence score is as follows:

[0058] ;

[0059] In the formula, S represents the impact fraction of sleep power consumption acquisition, H represents the voltage conversion impact factor, and G... max G represents the maximum sleep power consumption data. min G represents the minimum sleep power consumption data. y This indicates the reference fluctuation value of sleep power consumption.

[0060] It's important to understand that the sleep power consumption acquisition impact score is used to quantify the degree of influence of voltage conversion fluctuations on the sleep power consumption data acquisition process. Specifically, as the voltage conversion impact factor increases, the deviation of the sleep power consumption data fluctuation amplitude from the sleep power consumption reference fluctuation value increases, and the deviation ratio of the sleep power consumption data increases. This indicates that the LCD screen is more susceptible to voltage conversion fluctuation interference during the transition from standby mode to sleep mode, i.e., the sleep power consumption acquisition impact score increases. Furthermore, as the voltage fluctuation amplitude increases, i.e., the voltage conversion impact factor increases, the voltage stability of the LCD screen's driving circuit and power management system becomes poor, thus affecting the stability of the LCD screen's display brightness, contrast, color, and other operating states, leading to increased power consumption fluctuation amplitude, i.e., an increased deviation ratio of the sleep power consumption data. In addition, high-frequency voltage fluctuations may cause the power management system to frequently adjust, making it difficult for the LCD screen's power consumption to stabilize. This increases power consumption fluctuation, i.e., the frequency of voltage fluctuations, further increasing the deviation ratio of the sleep power consumption data. Through the combined effect of multiple parameters and the analysis of their interrelationships, it is possible to more accurately determine the degree of influence of voltage conversion fluctuations during mode transitions, thereby effectively improving the reliability of the power consumption data acquired by the LCD screen during the transition from standby mode to sleep mode.

[0061] Q3. Determine whether the impact score of sleep power consumption acquisition is within the preset switching impact limit. If so, record the corresponding sleep power consumption data acquisition impact result as controllable acquisition switching impact and perform power consumption data upload and storage management. Otherwise, record the corresponding sleep power consumption data acquisition impact result as abnormal acquisition switching impact and perform sleep power consumption data acquisition optimization management.

[0062] The specific process for managing the upload and storage of power consumption data is as follows:

[0063] C1 compresses power consumption data with power consumption data storage compression intensity. Power consumption data storage compression intensity represents the result of mapping the power consumption data storage compression harmonic average result to the pre-built mapping set of power consumption data storage compression harmonic average result and power consumption data storage compression intensity in the preset database. As the power consumption data storage compression harmonic average result increases, the power consumption data storage compression intensity decreases accordingly. The power consumption data storage compression harmonic average result represents the result of harmonic averaging the standby power consumption acquisition influence score and the sleep power consumption acquisition influence score. The power consumption data includes standby power consumption data and sleep power consumption data.

[0064] C2 updates the fragmented transmission data volume with the compressed power consumption data according to the fragmented upload update intensity to obtain the fragmented transmission data volume. The fragmented transmission data volume is then used to upload fragments to the cloud server. The fragmented upload update intensity represents the result of mapping the power consumption storage compression harmonic average result into the pre-built mapping set of power consumption storage compression harmonic average result and fragmented upload update intensity in the preset database. As the power consumption storage compression harmonic average result increases, the fragmented upload update intensity also increases.

[0065] Specifically, the process for performing sleep power consumption data acquisition optimization management is as follows:

[0066] D1 determines whether the deviation between the sleep power consumption sampling impact score and the switching impact limit score is within the preset abnormal impact limit range. If yes, proceed to D2; otherwise, update the soft-start time during the LCD screen's transition from standby mode to sleep mode to the soft-start optimization time. The soft-start optimization time represents the result of mapping the sleep power consumption sampling impact score deviation into the pre-built mapping set of sleep power consumption sampling impact score deviation and soft-start optimization time in the preset database. As the sleep power consumption sampling impact score deviation increases, the soft-start optimization time decreases accordingly. The deviation between the sleep power consumption sampling impact score and the switching impact limit score is the result of calculating the difference between them. If the soft-start time is too long, the LCD screen may generate higher instantaneous power consumption during mode switching. Reducing the soft-start time to the soft-start optimization time during the sleep mode transition helps slow down the rate of voltage or current change, reducing the impact of instantaneous voltage fluctuations on the device, ensuring power stability, and smoothly completing the state transition. Due to the delay during the soft-start process, the LCD screen may generate unnecessary high power consumption during startup, thereby increasing the instantaneous power consumption of the entire system.

[0067] D2 inputs the sleep power consumption acquisition impact score into the corresponding output of the switching acquisition cycle adjustment strength of the switching impact acquisition optimization linear regression model. Based on the obtained switching acquisition cycle adjustment strength, the initial switching acquisition cycle is updated to obtain the switching optimization management cycle. The switching impact acquisition optimization linear regression model is a pre-trained linear regression model used to fit the mapping relationship between the sleep power consumption acquisition impact score and the corresponding switching acquisition cycle adjustment strength. The input of the linear regression model is the sleep power consumption acquisition impact score within the historical time period.

[0068] D3. During the switching optimization management cycle, the sleep power consumption data of the preset batch of LCD screens is collected a preset number of times to obtain sleep detection power consumption data. The sleep detection power consumption data is compressed using the switching impact compression adjustment strength and uploaded to the cloud server. The switching impact compression adjustment strength represents the result of mapping the sleep power consumption collection impact score into the mapping set of sleep power consumption collection impact score and switching impact compression adjustment strength that has been constructed in the preset database. Specifically, as the sleep power consumption collection impact score increases, the switching impact compression adjustment strength decreases accordingly.

[0069] By compressing power consumption data, cloud storage requirements can be significantly reduced, especially when large amounts of data are accumulated, effectively saving costs and improving storage efficiency. Segmented uploading and compression optimization improve the efficiency and reliability of data transmission, reduce data loss and network congestion caused by upload failures, and ensure that data can be transmitted to the cloud faster and more stably, thereby helping to improve the accuracy of LCD screen power consumption data management.

[0070] As a further solution, the second module of this application is a sleep power consumption data acquisition and management module. This module is used to perform sleep power consumption data acquisition and management on a preset batch of LCD screens after switching to sleep mode if a sleep mode switching instruction is received, and to obtain the sleep power consumption data acquisition impact result to characterize the impact of voltage conversion fluctuation interference on the sleep power consumption data acquisition process. Otherwise, standby power consumption data acquisition optimization management is performed. Standby power consumption data acquisition optimization management means optimizing the interference of backlight brightness adjustment during standby power consumption data acquisition, and filtering and compressing the acquired standby power consumption data.

[0071] As a further solution, the third module of this application is a power consumption data storage management module. This module is used to determine whether to perform power consumption data upload and storage management based on the obtained sleep power consumption data acquisition impact results. If so, power consumption data upload and storage management is performed; otherwise, sleep power consumption data acquisition optimization management is performed. Sleep power consumption data acquisition optimization management means optimizing the interference of voltage conversion fluctuations during sleep power consumption data acquisition and storing and managing the acquired sleep power consumption data.

[0072] like Figure 4 The image shown is one of the standby power consumption data management interface diagrams of a liquid crystal screen data optimization management system based on big data analysis provided in an embodiment of the present invention; by Figure 4As can be seen, the main menu of the LCD screen test data management system provided in this application embodiment includes an optical performance test data management center, an electrical performance test data management center, a defect detection data management center, and a structural appearance test data management center. The system management includes user management, system settings, and a message center. Among them, the electrical performance test data management center includes an acquisition management center, a storage management center, and data security and backup. Specifically, the acquisition management center is divided into standby power consumption data management and sleep power consumption data management. Standby power consumption data management includes device information, standby power consumption data test data center, standby power consumption data acquisition impact results, and standby power consumption data acquisition optimization management. Standby power consumption data acquisition optimization management is used to display the update status of PWM frequency, screen refresh rate, acquisition window, and acquisition frequency.

[0073] like Figure 5 The image shown is one of the sleep power consumption data management interface diagrams of a liquid crystal screen data optimization management system based on big data analysis provided in an embodiment of the present invention; by Figure 5 As can be seen, the sleep power consumption data management of the acquisition management center provided in this application embodiment includes device information, sleep power consumption data test data center, sleep power consumption data acquisition impact results, and sleep power consumption data acquisition optimization management. The sleep power consumption data acquisition optimization management is used to display the update status of soft start time and acquisition cycle.

[0074] When managing and testing LCD screen power consumption data, it is crucial to consider the impact of switching from standby mode to sleep mode. This not only helps to more accurately assess the overall energy consumption of the LCD screen but also optimizes the energy efficiency of the device. There can be significant differences in power consumption between standby and sleep modes. In standby mode, the LCD screen may still consume some power to maintain a fast response, while in sleep mode, it typically reduces power consumption to a minimum, consuming almost no energy. If the impact of this mode switching and the interference of the switching process on power consumption data acquisition are not considered, the tested power consumption data may be inaccurate, thus affecting the optimization effect of power management. This application analyzes the interference in power consumption data collected from the LCD screen in standby mode and during the switching process to sleep mode during management testing. This helps to more accurately manage LCD screen power consumption data and further improves the reliability of power consumption data acquisition and management for the LCD screen.

[0075] like Figure 6The diagram shows a flowchart of a data optimization management method for LCD screens based on big data analysis provided by an embodiment of the present invention. This method includes the following steps: In standby mode, standby power consumption data collection and management is performed on a preset batch of LCD screens to obtain standby power consumption data collection impact results to determine whether to send a sleep mode switching command; if a sleep mode switching command is received, sleep power consumption data collection and management is performed on the preset batch of LCD screens after switching to sleep mode to obtain sleep power consumption data collection impact results characterizing the impact of voltage conversion fluctuation interference during the sleep power consumption data collection process; otherwise, standby power consumption data collection optimization management is performed. Standby power consumption data collection optimization management means optimizing the interference of backlight brightness adjustment during standby power consumption data collection, and filtering and compressing the collected standby power consumption data; combined with the obtained sleep power consumption data collection impact results, it is determined whether to perform power consumption data upload and storage management. If yes, power consumption data upload and storage management is performed; otherwise, sleep power consumption data collection optimization management is performed. Sleep power consumption data collection optimization management means optimizing the interference of voltage conversion fluctuation during sleep power consumption data collection, and storing the collected sleep power consumption data.

[0076] In summary, during the power consumption test of the LCD screen to manage the collected power consumption data, the reliability of the LCD screen power consumption data acquisition is low due to voltage fluctuation interference during the switch from standby mode to sleep mode. This application determines whether to send a sleep mode switching command based on the impact result of standby power consumption data acquisition. If sent, the impact result of sleep power consumption data acquisition is obtained; otherwise, standby power consumption data acquisition optimization management is performed. Standby power consumption data acquisition optimization management helps reduce the interference of backlight brightness adjustment during standby power consumption data acquisition. Finally, based on the obtained impact result of sleep power consumption data acquisition, it determines whether to perform power consumption data upload and storage management. Power consumption data upload and storage management helps improve the accuracy of storing and managing the collected sleep power consumption data, achieving more accurate management of LCD screen power consumption data and solving the problem in the prior art of ignoring the impact of switching from standby mode to sleep mode when managing and testing the power consumption data of the LCD screen.

[0077] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0078] This invention is described with reference to flowchart illustrations and / or block diagrams of systems, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0079] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0080] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0081] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0082] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0083] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A data optimization and management system for LCD screens based on big data analytics, characterized in that, include: Standby power consumption data acquisition and management module, sleep power consumption data acquisition and management module, and power consumption data storage management module; The standby power consumption data acquisition and management module is used to perform standby power consumption data acquisition and management on a preset batch of LCD screens in standby mode, and obtain the standby power consumption data acquisition impact result to determine whether to send a sleep mode switching instruction or a standby power consumption data acquisition optimization management instruction to the sleep power consumption data acquisition and management module. The sleep power consumption data acquisition and management module is used to perform sleep power consumption data acquisition and management on a preset batch of LCD screens after switching to sleep mode if a sleep mode switching instruction is received, and to obtain the sleep power consumption data acquisition impact result to characterize the impact of voltage conversion fluctuation interference on the sleep power consumption data acquisition process. If a standby power consumption data acquisition optimization management instruction is received, the standby power consumption data acquisition optimization management is executed. The standby power consumption data acquisition optimization management means optimizing the interference of backlight brightness adjustment during the standby power consumption data acquisition process, and filtering and compressing the acquired standby power consumption data. The power consumption data storage management module is used to determine whether to perform power consumption data upload and storage management based on the obtained sleep power consumption data acquisition impact results. The power consumption data includes standby power consumption data and sleep power consumption data. If so, power consumption data upload and storage management is performed; otherwise, sleep power consumption data acquisition optimization management is performed. Sleep power consumption data acquisition optimization management means optimizing the interference of voltage conversion fluctuations during sleep power consumption data acquisition and storing the acquired sleep power consumption data.

2. The LCD screen data optimization management system based on big data analysis according to claim 1, characterized in that, The standby power consumption data collection results are affected by the following steps: The quantitative indicators of the impact of data acquisition during the standby power consumption data acquisition and management process of LCD screens in a preset batch include the backlight adjustment brightness index, which characterizes the degree of difference between the backlight brightness and the backlight brightness reference value, and the backlight adjustment current index, which characterizes the degree of difference between the backlight current and the backlight current reference value. The results of weighted calculations of the collected influence quantification index and the adjustment interference influence compensation value obtained from the preset database are harmonic averaged to obtain the backlight adjustment influence factor. The adjustment interference influence compensation value is used to describe the degree of influence of the collected influence quantification index on the backlight adjustment influence factor, including the adjustment brightness influence compensation value and the adjustment current influence compensation value. The maximum and minimum standby power consumption data are processed by deviation ratio to obtain the standby power consumption data deviation ratio result. The obtained standby power consumption data deviation ratio result is then processed with the backlight adjustment influence factor to obtain the standby power consumption acquisition influence score, so as to quantify the degree of influence of backlight brightness adjustment interference on the standby power consumption data acquisition process. Determine whether the standby power consumption sampling impact score is not greater than the sampling impact limit score. If so, record the corresponding standby power consumption data sampling impact result as qualified; otherwise, record the corresponding standby power consumption data sampling impact result as abnormal. The standby power consumption data acquisition impact result is used to measure the degree of influence of backlight brightness adjustment interference on the standby power consumption data acquisition process, including whether the acquisition impact limit is qualified and whether the acquisition impact limit is abnormal.

3. The LCD screen data optimization management system based on big data analysis according to claim 2, characterized in that, The specific process for determining whether to send a sleep mode switching command to the sleep power consumption data acquisition and management module is as follows: If the standby power consumption data collection result is within acceptable limits, a sleep mode switching command is sent to switch the LCD screen from standby mode to sleep mode, and the sleep power consumption data collection result is obtained. If the result of standby power consumption data acquisition is that the acquisition impact limitation is abnormal, then the sleep mode switching command will not be sent, but the standby power consumption data acquisition optimization management command will be sent and the standby power consumption data acquisition optimization management will be executed. The standby power consumption data acquisition optimization management includes acquisition impact suppression optimization and data acquisition optimization management. The aforementioned acquisition impact suppression optimization means updating the PWM adjustment frequency and screen refresh rate during the standby power consumption data acquisition management process of the LCD screen based on the standby power consumption data acquisition impact results, so as to suppress the degree of interference from backlight brightness adjustment during the standby power consumption data acquisition process. The data acquisition optimization management refers to filtering and compressing the standby power consumption data during the standby power consumption data acquisition management process of the LCD screen, based on the impact results of standby power consumption data acquisition, in order to improve the accuracy of the standby power consumption data during the standby power consumption data acquisition management process of the LCD screen.

4. The LCD screen data optimization management system based on big data analysis according to claim 3, characterized in that, The specific process for optimizing the suppression of acquisition impact is as follows: A1, update the initial PWM frequency in the standby power consumption data acquisition and management process of the LCD screen to the PWM optimized frequency. The PWM optimized frequency represents the result of mapping the backlight adjustment influence factor in the standby power consumption acquisition influence score to the mapping set of backlight adjustment influence factor and PWM optimized frequency constructed in the preset database. A2, input the standby power consumption sampling impact score into the pre-built mapping set of standby power consumption sampling impact score and screen optimization refresh rate in the preset database to obtain the screen optimization refresh rate, and iteratively update the initial screen refresh rate based on the obtained screen optimization refresh rate; A3: Re-perform standby power consumption data acquisition and management for the preset batch of LCD screens. Determine whether the newly acquired standby power consumption data acquisition impact result is within the acceptable range. If so, send an acquisition impact suppression optimization end command and switch to sleep mode. Otherwise, repeat A1, A2, and A3 until the data acquisition optimization management command sending condition is triggered. The data acquisition optimization management command sending condition includes PWM adjustment optimization frequency not less than the preset PWM frequency adjustment limit and screen optimization refresh rate not less than the preset screen refresh rate limit.

5. The LCD screen data optimization management system based on big data analysis according to claim 3, characterized in that, The specific process for the data acquisition optimization management is as follows: B1, the standby power consumption sampling impact score is input into the sampling window adjustment linear regression model and the sampling frequency adjustment linear regression model respectively, and the corresponding output sampling window optimization strength and sampling frequency optimization strength are output. Based on the obtained sampling window optimization strength and sampling frequency optimization strength, the initial sampling window length and the initial sampling frequency are updated respectively to obtain the sampling optimization window length and sampling optimization frequency. B2, based on the obtained acquisition optimization window length and acquisition optimization frequency, re-acquire the standby power consumption data of the preset batch of LCD screens a preset number of times to obtain standby detection power consumption data and obtain the corresponding standby power consumption data acquisition impact results; B3. Determine whether the results of the reacquisition of the corresponding standby power consumption data acquisition are all within the acceptable range. If so, acquire the results of the sleep power consumption data acquisition; otherwise, proceed to B4. B4. The sampling impact score of the standby power consumption acquired again during the preset number of sampling processes is harmonic and averaged to obtain the sampling impact score adjustment coefficient. The sampling impact score adjustment coefficient is then input into the corresponding outputs of the linear regression model for data filtering and the linear regression model for data compression, namely the data filtering optimization strength and the data compression optimization strength. Based on the obtained data filtering optimization strength, the standby power consumption data is filtered, and then compressed using the data compression optimization strength before being uploaded to the cloud server.

6. The LCD screen data optimization management system based on big data analysis according to claim 5, characterized in that, The specific process for obtaining the impact of sleep power consumption data collection results is as follows: Acquire switching impact management data of the LCD screen during the process of switching from standby mode to sleep mode. The switching impact management data includes voltage fluctuation amplitude, voltage fluctuation frequency, maximum sleep power consumption data, and minimum sleep power consumption data. Based on the switching impact management data, the degree of influence of voltage conversion fluctuations on the sleep power consumption data acquisition process is quantitatively determined, and the sleep power consumption acquisition impact score is obtained. Determine whether the impact score of sleep power consumption acquisition is within the preset switching impact limit. If so, record the corresponding sleep power consumption data acquisition impact result as controllable acquisition switching impact and perform power consumption data upload and storage management. Otherwise, record the corresponding sleep power consumption data acquisition impact result as abnormal acquisition switching impact and perform sleep power consumption data acquisition optimization management.

7. A data optimization and management system for LCD screens based on big data analysis according to claim 6, characterized in that, The specific steps for obtaining the sleep power consumption sampling impact score are as follows: The voltage fluctuation amplitude and voltage fluctuation frequency are respectively proportional to the voltage fluctuation amplitude threshold and voltage fluctuation frequency threshold obtained from the preset database to obtain the corresponding voltage fluctuation amplitude index and voltage fluctuation frequency index. The voltage conversion fluctuation weighting results are harmonic averaged to obtain the voltage conversion influence factor; The voltage conversion fluctuation weighting result represents the result of weighting the voltage fluctuation amplitude index, the voltage fluctuation frequency index, and the conversion fluctuation compensation value obtained from the preset database. The conversion fluctuation compensation value is used to describe the degree of influence of the voltage fluctuation amplitude index and the voltage fluctuation frequency index on the voltage conversion influence factor, including the fluctuation amplitude compensation value and the fluctuation frequency compensation value. The maximum and minimum sleep power consumption data are processed to obtain the deviation ratio of sleep power consumption data. The obtained deviation ratio of sleep power consumption data is then processed with the voltage conversion influence factor to obtain the sleep power consumption acquisition influence score, which is used to quantitatively judge the degree of influence of voltage conversion fluctuation interference on the sleep power consumption data acquisition process.

8. A data optimization and management system for LCD screens based on big data analysis according to claim 6, characterized in that, The specific process for performing power consumption data upload and storage management is as follows: C1, compresses power data with power data storage compression intensity, wherein the power data storage compression intensity represents the result of mapping the power storage compression harmonic average result to the mapping set of power storage compression harmonic average result and power data storage compression intensity that has been constructed in the preset database, wherein the power storage compression harmonic average result represents the result of harmonic averaging the standby power consumption acquisition influence score and the sleep power consumption acquisition influence score; C2 updates the fragmented transmission data volume with the compressed power consumption data using the fragmented upload update intensity to obtain the fragmented management transmission data volume. The fragmented management transmission data volume is then used to upload fragments to the cloud server. The fragmented upload update intensity represents the result of mapping the power consumption storage compression harmonic average result input into the preset database to the mapping set between the power consumption storage compression harmonic average result and the fragmented upload update intensity.

9. A data optimization and management system for LCD screens based on big data analysis according to claim 6, characterized in that, The specific process for performing sleep power consumption data acquisition and optimization management is as follows: D1, determine whether the deviation between the sleep power consumption acquisition impact score and the switching impact limit score is within the preset abnormal impact limit range. If yes, then execute D2. Otherwise, update the soft start time during the process of switching the LCD screen from standby mode to sleep mode to the soft start optimization time. The soft start optimization time represents the result of mapping the deviation of the sleep power consumption acquisition impact score to the mapping set of the deviation of the sleep power consumption acquisition impact score and the soft start optimization time that has been constructed in the preset database. D2, input the sleep power consumption acquisition impact score into the corresponding output of the switching impact acquisition optimization linear regression model to adjust the switching acquisition cycle. Based on the obtained switching acquisition cycle adjustment, update the initial switching acquisition cycle to obtain the switching optimization management cycle. D3. During the switching optimization management cycle, the sleep power consumption data of the preset batch of LCD screens is collected a preset number of times to obtain sleep detection power consumption data. The sleep detection power consumption data is compressed using the switching influence compression adjustment strength and uploaded to the cloud server. The switching influence compression adjustment strength represents the result of mapping the sleep power consumption collection influence score into the mapping set of sleep power consumption collection influence score and switching influence compression adjustment strength that has been constructed in the preset database.

10. A method for optimizing and managing LCD screen data based on big data analytics, applied to the LCD screen data optimization and management system based on big data analytics as described in any one of claims 1-9, characterized in that, The method includes the following steps: In standby mode, standby power consumption data is collected and managed for a preset batch of LCD screens to obtain the standby power consumption data collection results and determine whether to send a sleep mode switching command. If a sleep mode switching command is received, sleep power consumption data acquisition management is performed on a preset batch of LCD screens after switching to sleep mode. The sleep power consumption data acquisition impact result is obtained to characterize the impact of voltage conversion fluctuation interference on the sleep power consumption data acquisition process. If a standby power consumption data acquisition optimization management command is received, standby power consumption data acquisition optimization management is executed. The standby power consumption data acquisition optimization management means optimizing the interference of backlight brightness adjustment during standby power consumption data acquisition, and filtering and compressing the acquired standby power consumption data. Based on the obtained sleep power consumption data acquisition results, it is determined whether to perform power consumption data upload and storage management. If so, power consumption data upload and storage management is performed; otherwise, sleep power consumption data acquisition optimization management is performed. The sleep power consumption data acquisition optimization management refers to optimizing the interference of voltage conversion fluctuations during the sleep power consumption data acquisition process and managing the storage of the acquired sleep power consumption data.

Images