Liquid crystal display screen driving system with intelligent switching driving mode strategy

Abstract

This invention discloses a liquid crystal display (LCD) driving system with an intelligent switching drive mode strategy, belonging to the field of intelligent driving technology. It aims to solve the problem of making the selection and switching of display drive modes more convenient. This invention introduces a transition mechanism to prevent abrupt changes during switching, and uses closed-loop feedback to correct signals in real time to maintain accuracy. It monitors system status at high frequency across all dimensions, processes data in layers, and uses log-related feedback for decision-making, ensuring display quality and system stability. During decision-making, data is synchronized and aligned, and dynamic weight allocation combined with three-dimensional adaptation calculations increases hardware verification and optimization, improving user experience and the accuracy of drive mode decisions.

Description

Technical Field

[0001] This invention relates to the field of intelligent drive technology, specifically to a liquid crystal display drive system with an intelligent switching drive mode strategy. Background Technology

[0002] Existing LCD screen driving systems have significant drawbacks: environmental parameter acquisition is limited in type and lacks effective verification, leading to data inaccuracies; display data acquisition is asynchronous and formatted inconsistently, with insufficient noise reduction and one-sided feature extraction; user interaction interfaces are limited, there are no high-level configuration entry points, and parameter storage lacks a classification system and backups are inadequate. Furthermore, driving decision data is asynchronous, weights are fixed, and there is no hardware verification or clear optimization rules; mode switching is abrupt, lacking real-time feedback and correction; status monitoring points are few, data noise is high, and there is no tiered anomaly handling, severely impacting display quality and system stability. Summary of the Invention

[0003] The purpose of this invention is to provide a liquid crystal display driving system with an intelligent switching drive mode strategy. By introducing a transition mechanism to prevent abrupt changes during switching, and using closed-loop feedback to correct signals in real time to maintain accuracy, the system status is monitored at high frequency in all dimensions. Data is processed in layers, and logs are correlated with feedback for decision-making to ensure display effect and system stability. Data is synchronized and aligned during decision-making, and dynamic weight allocation combined with three-dimensional adaptation calculation is used to increase hardware verification and optimization, thereby improving the user experience and the accuracy of drive mode decision-making. This invention can solve the problems in the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] An LCD screen driving system with an intelligent switching drive mode strategy includes:

[0006] The environmental parameter acquisition unit is used to collect physical parameter data related to the operating environment of the display screen in real time;

[0007] First, confirm the parameter type of the physical parameter data, and then confirm the sensor based on the parameter type;

[0008] The display data optimization and analysis unit is used to collect display data in real time and analyze the characteristic parameters of the display data that is about to be sent to the display screen in real time.

[0009] The user interaction and drive mode confirmation unit is used to provide a user interface, allowing users to set display preferences and providing the system with advanced strategy configuration options. At the same time, it stores preset drive mode parameters and dynamically updated optimization parameters.

[0010] The provision of user interfaces includes: building multi-dimensional user interaction entry points, which provides diverse user operation interfaces;

[0011] The drive mode decision unit is used to determine the optimal drive mode based on physical parameter data, display content characteristic parameters, and historical data;

[0012] Among them, based on the core indicators of physical parameter data and display content characteristic parameters, candidate modes that meet the basic conditions are selected from the preset driving mode library, and unmatched modes are eliminated during the selection process.

[0013] The selected candidate patterns will then undergo 3D adaptation calculations.

[0014] Preferably, the environmental parameter acquisition unit is further used for:

[0015] Each sensor collects parameter data according to a preset acquisition frequency. After the parameter data is collected, an A / D converter is used to convert the analog signal into a digital signal.

[0016] Meanwhile, during the parameter data acquisition process, an RC low-pass filter is used for filtering and noise reduction.

[0017] The parameter types include light intensity parameters, ambient temperature parameters, ambient humidity parameters, external vibration parameters, and power supply environment parameters;

[0018] Among them, the light intensity parameter is collected using a photosensitive sensor; the ambient temperature parameter is collected using a temperature sensor; the ambient humidity parameter is collected using a humidity sensor; the external vibration parameter is collected using a miniature vibration sensor; and the power supply environment parameter is collected through a current sensor connected in series in the power supply circuit of the drive system and a voltage sensor connected in parallel.

[0019] The parameter data after filtering and denoising is validated. The validation process involves determining whether the collected parameter data is within the normal range of the corresponding sensor, comparing the difference between two sets of data, and removing invalid and abnormal data.

[0020] After the validity verification is completed, the physical parameter data is obtained.

[0021] Preferably, the environmental parameter acquisition unit is further used for:

[0022] If the difference between the collected data at three consecutive collection times of the sensor exceeds the preset environmental threshold and there is a pattern jump with matching values, the parameter type corresponding to the current sensor is marked as the first abnormal parameter type; the three consecutive collection times are the current collection time and two adjacent historical collection times.

[0023] The preset acquisition frequency of the sensor is adjusted using the initial adjustment parameters that match the first abnormal parameter type.

[0024] If the difference between the collected data at three consecutive collection times of the sensor exceeds the preset environmental threshold and there is no pattern jump with matching values, the parameter type corresponding to the current sensor is marked as the second abnormal parameter type.

[0025] The initial adjustment parameter corresponding to the second abnormal parameter type is regarded as the baseline adjustment parameter;

[0026] Extract the historical configuration parameter values ​​of each preference option within a preset historical time period, and perform trend analysis to obtain the corresponding historical preference change characteristics; the historical preference change characteristics include historical setting frequency, historical setting change magnitude, and historical preference change level;

[0027] When the historical preference change level of all the aforementioned preference options is a minor change, the preference option with the highest association influence weight with the second abnormal parameter type is regarded as the first key preference option;

[0028] By utilizing the historical setting frequency of the first key preference option within a preset historical period and its association influence weight with the second abnormal parameter type, the benchmark adjustment parameter is adjusted to obtain the first adjusted parameter;

[0029] The preset acquisition frequency of the sensor is adjusted based on the first adjusted parameters;

[0030] When the historical preference change level of the preference option is a moderate change or a heavy change, obtain the proportion of preference options whose association influence weight with the second abnormal parameter type exceeds the preset influence threshold and whose historical preference change level is a heavy change.

[0031] If the percentage of the options does not exceed the preset percentage threshold, the baseline adjustment parameters are adjusted by using the historical setting frequency and corresponding associated influence weight of the preference options whose correlation influence weight with the second abnormal parameter type exceeds the preset influence threshold within a preset historical period, to obtain the second adjusted parameters.

[0032] The preset acquisition frequency of the sensor is adjusted based on the second adjusted parameters;

[0033] If the proportion of the options exceeds a preset proportion threshold, the preference options whose association influence weight with the second abnormal parameter type exceeds a preset influence threshold will be regarded as the second key preference options.

[0034] Based on the historical setting frequency of the second key preference option within a preset historical period and the weight of its association with the second abnormal parameter type, the benchmark adjustment parameter is adjusted to obtain the third adjusted parameter.

[0035] Obtain the target parameter difference between the benchmark adjustment parameter and the third adjusted parameter;

[0036] A volatility performance score is obtained based on the amplitude influence parameter determined by the historical setting change amplitude of the second key preference option; the amplitude influence parameter includes the number of historical differences in the direction of amplitude change and the variance of historical change amplitude.

[0037] The target number of adjustments is determined using the fluctuation performance score as a matching criterion.

[0038] The target adjustment range is determined based on the number of target adjustments and the difference in target parameters.

[0039] The preset acquisition frequency of the sensor is adjusted according to the target adjustment range until the third adjusted parameter is reached.

[0040] Preferably, the display data optimization and analysis unit is further used for:

[0041] Real-time acquisition of display data involves establishing a synchronous data capture mechanism with the signal input terminal of the display screen to capture the raw display data that is about to be sent to the display screen driver circuit. The raw display data includes dynamic video streams, static image frames, and text information.

[0042] The collected raw display data is format-standardized to convert data from different sources into a recognizable standard format; at the same time, basic noise reduction algorithms are used to remove salt-and-pepper noise and impulse interference introduced during data transmission.

[0043] The data that has been standardized is divided according to the presentation logic of the display content, and then the content of each part is marked in a structured way to confirm the display position and size range of each part.

[0044] The structured and labeled data is then processed to extract feature parameters, including brightness features, color features, dynamic features, spatial features, and dynamic and static features.

[0045] The extracted feature parameters are converted into quantifiable numerical indicators, and a real-time update mechanism is established.

[0046] The set of feature parameters that are updated in real time is packaged, and the feature parameter data that is sent to the display screen is obtained after packaging.

[0047] Preferably, the user interaction and drive mode confirmation unit is further used for:

[0048] Design the display preference options. After the display preference options are designed, develop the high-level strategy configuration option interface. After both the display preference options and the high-level strategy configuration options are set, validate the legality of the input content.

[0049] Diverse user interfaces include physical interaction, touch interaction, remote interaction, and system integration interaction;

[0050] Display preference options include brightness preference, color preference, dynamic display preference, and energy saving preference;

[0051] Among them, the advanced strategy configuration options are: to open a deep configuration entry for advanced users or system administrators, including mode switching trigger conditions, policy priority settings, scene mode customization, and historical data weight adjustment;

[0052] After successful verification, the preference settings are converted into recognizable parameter commands; at the same time, a configuration log is generated, recording the setting time, user identity, and specific parameter values.

[0053] After the configuration log is generated, a storage system with preset driver mode parameters is built. The storage system construction process is as follows: an independent storage area is divided in the non-volatile storage medium, and an index directory is built according to the mode type. The mode types include standard mode, power saving mode, high image quality mode and outdoor mode. The complete parameter set of the corresponding mode is stored in each directory.

[0054] Meanwhile, temporary cache and permanent storage are allocated for dynamic parameters. The temporary cache is used for high-frequency updates. The cache data is integrated and written to the permanent storage every preset period or when the system is idle.

[0055] Finally, the current state of preset and dynamic parameters is automatically backed up periodically, storing at least 3 historical versions, and a manual backup option is provided, allowing users to actively save snapshots before important configuration changes.

[0056] Finally, the data optimization and storage are completed.

[0057] Preferably, the driving mode decision unit is further configured to:

[0058] It receives physical parameter data output by the environmental parameter acquisition unit in real time; it receives display content feature parameters generated by the display data optimization and analysis unit; and it receives historical data stored by the user interaction and drive mode confirmation unit, and ensures that the three types of data are aligned in the time dimension through a data synchronization mechanism.

[0059] The three types of input data are converted into a unified evaluation dimension. Based on the user's preset strategy priority and combined with the system's default rules, dynamic weight coefficients are assigned to physical parameter data, display content feature parameters, and historical data respectively.

[0060] The three-dimensional adaptation calculation includes environment adaptation score, content adaptation score and historical fit score. The scores of each category are weighted and summed according to their corresponding weights to obtain the comprehensive adaptation value of the candidate mode.

[0061] The overall adaptation value is corrected based on real-time operating status data, which includes the current screen temperature, drive circuit load, and pixel response delay.

[0062] At the same time, it verifies whether the candidate patterns meet the hardware physical limitations and eliminates the patterns that do not meet the constraints;

[0063] The optimal driving mode is selected from the revised candidate modes, based on the overall fit value. If multiple modes have the same score, the option that matches the mode most recently manually confirmed by the user is selected first, or the mode with the most stable historical operation of the system is selected by default.

[0064] Finally, the optimal driving mode was confirmed.

[0065] Preferably, the overall fit value is corrected, including:

[0066] Acquire and analyze the data of the current screen temperature, the drive circuit load, and the pixel response delay, and compare them with the corresponding preset state thresholds to determine the adaptation correction coefficient;

[0067] The overall fit value is corrected using the aforementioned fit correction coefficient.

[0068] Preferred options also include:

[0069] The drive signal execution unit is used to generate corresponding drive signals according to the target drive mode, and control the hardware drive circuit according to the drive signals to complete the switching of display mode;

[0070] Specifically, the instruction for the optimal driving mode is received, and a mapping relationship between the mode parameters and the hardware driving signals is established according to the display hardware specifications. Based on the mapped electrical signal parameters, multiple parallel driving signals are generated through the timing controller.

[0071] The instruction contains the complete parameter set corresponding to the mode. The parameter set is structured and parsed to extract the core control indicators corresponding to the hardware driver circuit and confirm the physical meaning of the core control indicators.

[0072] The mapping relationship between mode parameters and hardware drive signals is as follows: abstract parameters are converted into specific electrical signal parameters; timing parameters are converted into the frequency and phase of the corresponding clock signal; color parameters are converted into the output voltage curve of the digital-to-analog converter.

[0073] The multi-parallel drive signals include pixel drive signals, backlight drive signals, timing synchronization signals, and auxiliary control signals;

[0074] The generated multi-channel parallel drive signals are sent to the signal amplification circuit, where the signals are enhanced according to the impedance characteristics and power requirements of the hardware drive circuit. At the same time, the signal output characteristics are adjusted through the impedance matching circuit.

[0075] Preferably, the drive signal execution unit is further configured to:

[0076] The amplified multi-channel drive signals are then synchronously output to the corresponding hardware circuit modules according to a preset timing sequence.

[0077] A transition mechanism is introduced during the switching from the current mode to the target mode. The transition mechanism is as follows: voltage and current signals are gradually changed in a step-by-step manner; timing signals are first sent with a pre-switching command to put the hardware circuit into a ready state before the switch is completed; and pixel brightness compensation values ​​are adjusted synchronously for backlight brightness and visual sensitivity parameters.

[0078] During the output of the driving signal, the parameters of the actual output signal are collected in real time through the feedback interface in the hardware circuit and compared with the target parameters.

[0079] If the deviation is within the preset range, the signal drive is deemed effective; if the deviation exceeds the limit, the fine-tuning mechanism is immediately activated to correct the output until it meets the target value.

[0080] Finally, the display mode switch is completed.

[0081] Preferred options also include:

[0082] The status monitoring and adjustment unit is used to monitor the operating status of the LCD screen and drive system in real time, and dynamically optimize the drive mode parameters based on the operating status feedback.

[0083] Before real-time monitoring of the operating status of the LCD screen and drive system, monitoring points are deployed, and the collected raw status data is processed in layers. The deviation between the real-time operating status data and the benchmark value is calculated based on the currently effective drive mode parameters.

[0084] The monitoring points are deployed as follows: For the LCD screen, the panel operating temperature is monitored by a temperature sensor built into the edge of the screen, and the brightness uniformity and pixel response delay of each area are monitored by reference pixels in the pixel array; For the driving system, real-time power consumption, core circuit operating voltage and current are collected through the detection interface of the power management module, signal transmission delay and data error rate are recorded through the timing controller, and the luminous intensity and color temperature stability of the backlight module are monitored through the feedback terminal of the backlight driving circuit; each monitoring point collects data at a high frequency period of 10-100ms.

[0085] The layered processing involves: removing high-frequency noise through sliding window filtering, then validating the data and eliminating outliers caused by sensor malfunctions or transmission interference; finally, aligning the multi-source data by timestamp to form a structured state dataset.

[0086] Based on the degree and trend of deviation, the operating status is divided into four levels: normal, slightly abnormal, moderately abnormal, and severely abnormal.

[0087] Optimization plans are formulated based on different anomaly levels. The generated optimization parameters are first verified in a simulation environment based on the formulated optimization plan, and then implemented in stages after the verification is successful.

[0088] Then, the triggering conditions, adjusted parameters, and implementation effects of each optimization are recorded as optimization logs, and stored in association with the corresponding environmental parameters and display content characteristics;

[0089] Finally, the optimized state data is fed back to the driving mode decision unit in real time as a reference for the next mode decision.

[0090] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0091] 1. The intelligent switching drive mode strategy LCD screen driving system provided by this invention constructs a multi-dimensional data acquisition and precise processing system. The environmental parameters cover five key items such as light, temperature and humidity. The data reliability is ensured by noise filtering and double verification. After the display data is captured synchronously, it is standardized and processed to extract multi-dimensional quantitative features, providing comprehensive and accurate data support for drive mode decision-making and avoiding the impact of inaccurate or incomplete data on decision-making.

[0092] 2. The LCD screen driving system with intelligent switching driving mode strategy provided by this invention has diverse operation interfaces and high-level configurations to meet the needs of different users. Parameter storage is classified and backed up in multiple versions to ensure security. Data is synchronized and aligned during decision-making, dynamic weight allocation is combined with three-dimensional adaptation calculation, hardware verification and optimization are added, and the operation experience and driving mode decision accuracy are improved.

[0093] 3. The LCD screen driving system with intelligent switching driving mode strategy provided by the present invention introduces a transition mechanism to prevent abrupt changes during switching, and uses closed-loop feedback to correct signals in real time to maintain accuracy; it monitors the system status in all dimensions at high frequency, processes data in layers, and uses log-related feedback to make decisions, ensuring display effect and system stability. Attached Figure Description

[0094] Figure 1 This is a schematic diagram of the liquid crystal display driving unit of the present invention;

[0095] Figure 2 This is a schematic diagram of the intelligent switching steps for driving the liquid crystal display screen according to the present invention. Detailed Implementation

[0096] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0097] To address the issues in existing technologies, such as incomplete environmental parameter collection types, lack of effective verification leading to inaccurate data, asynchronous data collection, inconsistent formats, insufficient noise reduction, and incomplete feature extraction, which hinder accurate and unified data support for driving mode decisions, please refer to [link to relevant documentation]. Figure 1 and Figure 2 This embodiment provides the following technical solution:

[0098] An LCD screen driving system with an intelligent switching drive mode strategy includes:

[0099] The environmental parameter acquisition unit is used to collect physical parameter data related to the operating environment of the display screen in real time;

[0100] First, confirm the parameter type of the physical parameter data, and then confirm the sensor based on the parameter type;

[0101] Each sensor collects parameter data according to a preset acquisition frequency. After the parameter data is collected, an A / D converter is used to convert the analog signal into a digital signal.

[0102] Meanwhile, during the parameter data acquisition process, an RC low-pass filter is used for filtering and noise reduction.

[0103] The display data optimization and analysis unit is used to collect display data in real time and analyze the characteristic parameters of the display data that is about to be sent to the display screen in real time.

[0104] The user interaction and drive mode confirmation unit is used to provide a user interface, allowing users to set display preferences and providing the system with advanced strategy configuration options. At the same time, it stores preset drive mode parameters and dynamically updated optimization parameters.

[0105] The provision of user interfaces includes: building multi-dimensional user interaction entry points, which provides diverse user operation interfaces;

[0106] Next, design the display preference options. After the display preference options are designed, develop the advanced strategy configuration option interface. After both the display preference options and the advanced strategy configuration options are set, validate the legality of the input content.

[0107] The drive mode decision unit is used to determine the optimal drive mode based on physical parameter data, display content characteristic parameters, and historical data;

[0108] The drive signal execution unit is used to generate corresponding drive signals according to the target drive mode, and control the hardware drive circuit according to the drive signals to complete the switching of display mode;

[0109] Specifically, the instruction for the optimal driving mode is received, and a mapping relationship between the mode parameters and the hardware driving signals is established according to the display hardware specifications. Based on the mapped electrical signal parameters, multiple parallel driving signals are generated through the timing controller.

[0110] The status monitoring and adjustment unit is used to monitor the operating status of the LCD screen and drive system in real time, and dynamically optimize the drive mode parameters based on the operating status feedback.

[0111] Before real-time monitoring of the operating status of the LCD screen and drive system, monitoring points are first deployed, and then the collected raw status data is processed in layers. Based on the currently effective drive mode parameters, the deviation between the real-time operating status data and the benchmark value is calculated.

[0112] The environmental parameter acquisition unit is also used for:

[0113] The parameter types include light intensity parameters, ambient temperature parameters, ambient humidity parameters, external vibration parameters, and power supply environment parameters;

[0114] Among them, the light intensity parameter is collected using a photosensitive sensor; the ambient temperature parameter is collected using a temperature sensor; the ambient humidity parameter is collected using a humidity sensor; the external vibration parameter is collected using a miniature vibration sensor; and the power supply environment parameter is collected through a current sensor connected in series in the power supply circuit of the drive system and a voltage sensor connected in parallel.

[0115] The parameter data after filtering and denoising is validated. The validation process involves determining whether the collected parameter data is within the normal range of the corresponding sensor, comparing the difference between two sets of data, and removing invalid and abnormal data.

[0116] After the validity verification is completed, the physical parameter data is obtained.

[0117] Specifically, the parameters are comprehensive and accurate, covering five key physical parameters: light intensity, ambient temperature and humidity, external vibration, and power supply environment. It can capture the environmental conditions of the drive system's surroundings and power supply links from multiple dimensions, avoiding the limitations of single-parameter monitoring and providing a complete data foundation for system operation status assessment. The sensor selection is highly adaptable, using dedicated sensors for different parameters, such as miniature vibration sensors adapted for vibration monitoring, and combinations of series current sensors and parallel voltage sensors in the power supply circuit, ensuring the relevance of parameter acquisition and the accuracy of raw data, reducing errors from cross-type sensor acquisition. The signal processing workflow is sophisticated, and preset acquisition frequencies ensure data timeliness. The analog-to-digital converter converts analog signals to digital signals, facilitating subsequent data storage and analysis. Simultaneously, the RC low-pass filter effectively removes noise caused by external electromagnetic interference or signal fluctuations, improving signal purity. A rigorous data verification mechanism employs dual validity checks—range judgment and comparison of data differences—to accurately eliminate invalid data exceeding the sensor's normal range and abnormal data with unusual fluctuations. This ensures the reliability of the final output physical parameter data, providing high-quality data support for drive system fault warning and operational optimization. The overall solution combines practicality and stability. The table below illustrates the multi-dimensional environmental parameter accurate acquisition and dynamic verification mechanism:

[0118] Parameter type Data acquisition sensor Key processing steps Threshold range Parameter values / standards Innovation Value Light intensity parameters Photosensitive sensor 2. Validity verification (range determination and data difference comparison) using an RC low-pass filter for noise reduction. 0-100000 lux (covering indoor low-light to outdoor high-light scenarios) 1. Acquisition frequency: Default 100ms / acquisition, adjustable via advanced configuration. 2. Data difference threshold: Trigger secondary verification when the difference between two consecutive acquisitions exceeds 2000 lux. 3. Invalid data rejection criteria: Data exceeding the sensor's 0-100000 lux measurement range is directly rejected. 1. Precisely matches outdoor high-light (e.g., midday sunlight, approximately 50,000-100,000 lux) and indoor low-light (e.g., nighttime desk lamp, approximately 100-500 lux) scenarios, providing data support for switching between outdoor mode and standard mode. 2. Avoids frequent mode switching caused by sudden changes in lighting data, improving display stability. Ambient temperature parameters Temperature sensor 2. Validity verification (range determination and data difference comparison) of RC low-pass filter for noise reduction. -20℃ to 80℃ (covering extreme low to high temperature working environments) Acquisition frequency: Default 200ms / acquisition; Data difference threshold: Trigger secondary verification when the difference between two consecutive acquisitions exceeds 5℃; 3. Invalid data rejection criteria: Data exceeding the -20℃ to 80℃ range is directly rejected. 1. Automatically optimizes drive parameters for high-temperature environments (e.g., approximately 40-60℃ inside a car in summer) to reduce screen power consumption and prevent overheating damage. 2. Adjusts pixel response parameters for low-temperature environments (e.g., -10-20℃ outdoors in winter) to prevent image retention. Ambient humidity parameters Humidity sensor 2. Validity verification (range determination and data difference comparison) of RC low-pass filter for noise reduction. 0%-100%RH Acquisition frequency: Default 500ms / acquisition; Data difference threshold: Trigger secondary verification when the difference between two consecutive acquisitions exceeds 10%RH; 3. Invalid data removal criteria: Data exceeding the 0%-100%RH range is directly removed. 1. In high humidity environments (e.g., outdoor rain at 80%-100% RH), automatically enhances the stability of the drive signal to prevent display abnormalities caused by circuit moisture. 2. In low humidity environments (e.g., dry indoor environments at 20%-30% RH), optimizes the backlight drive to balance display performance and power consumption. External vibration parameters Miniature vibration sensor 2. Validity verification (range determination and data difference comparison) of RC low-pass filter for noise reduction. 0-500Hz (covering everyday vibration scenarios) Acquisition frequency: Default 100ms / time (high-frequency acquisition to capture instantaneous vibrations) Data difference threshold: Secondary verification is triggered when the difference between two consecutive acquisitions exceeds 50Hz. 3. Invalid data rejection criteria: Data exceeding the 0-500Hz range is directly rejected. 1. Automatically switch to anti-shake drive mode for vibration scenarios (such as vehicle displays and industrial equipment screens) to reduce image blur. 2. When the vibration frequency exceeds 300Hz, temporarily reduce the dynamic display frame rate to prioritize display stability. Power supply environment parameters Current sensor (in series), voltage sensor (in parallel) 2. Validity verification (range determination and data difference comparison) of RC low-pass filter for noise reduction. 1. Voltage: 5V-24V (covering the power supply range of mainstream displays) 2. Current: 0-5A 1. Data Acquisition Frequency: Default 50ms / acquisition (high-frequency monitoring of power supply stability) 2. Voltage Difference Threshold: Secondary verification is triggered when the difference between two consecutive data acquisitions exceeds 0.5V. 3. Current Difference Threshold: Secondary verification is triggered when the difference between two consecutive data acquisitions exceeds 0.3A. 4. Invalid Data Removal Criteria: Data with voltage exceeding 5V-24V or current exceeding the 0-5A range are directly removed. 1. When voltage fluctuates (e.g., after conversion from 220V±10% in a household circuit), the power supply adaptation parameters of the drive circuit are automatically adjusted to prevent black screen and screen flickering. 2. When the current abnormally increases (e.g., approaching the 5A threshold), the power saving mode is triggered to reduce power consumption and protect the power supply circuit.

[0119] The environmental parameter acquisition unit is also used for:

[0120] If the difference between the collected data at three consecutive collection times of the sensor exceeds the preset environmental threshold and there is a pattern jump with matching values, the parameter type corresponding to the current sensor is marked as the first abnormal parameter type; the three consecutive collection times are the current collection time and two adjacent historical collection times.

[0121] The preset acquisition frequency of the sensor is adjusted using the initial adjustment parameters that match the first abnormal parameter type.

[0122] If the difference between the collected data at three consecutive collection times of the sensor exceeds the preset environmental threshold and there is no pattern jump with matching values, the parameter type corresponding to the current sensor is marked as the second abnormal parameter type.

[0123] The initial adjustment parameter corresponding to the second abnormal parameter type is regarded as the baseline adjustment parameter;

[0124] Extract the historical configuration parameter values ​​of each preference option within a preset historical time period, and perform trend analysis to obtain the corresponding historical preference change characteristics; the historical preference change characteristics include historical setting frequency, historical setting change magnitude, and historical preference change level;

[0125] When the historical preference change level of all the aforementioned preference options is a minor change, the preference option with the highest association influence weight with the second abnormal parameter type is regarded as the first key preference option;

[0126] By utilizing the historical setting frequency of the first key preference option within a preset historical period and its association influence weight with the second abnormal parameter type, the benchmark adjustment parameter is adjusted to obtain the first adjusted parameter;

[0127] The preset acquisition frequency of the sensor is adjusted based on the first adjusted parameters;

[0128] When the historical preference change level of the preference option is a moderate change or a heavy change, obtain the proportion of preference options whose association influence weight with the second abnormal parameter type exceeds the preset influence threshold and whose historical preference change level is a heavy change.

[0129] If the percentage of the options does not exceed the preset percentage threshold, the baseline adjustment parameters are adjusted by using the historical setting frequency and corresponding associated influence weight of the preference options whose correlation influence weight with the second abnormal parameter type exceeds the preset influence threshold within a preset historical period, to obtain the second adjusted parameters.

[0130] The preset acquisition frequency of the sensor is adjusted based on the second adjusted parameters;

[0131] If the proportion of the options exceeds a preset proportion threshold, the preference options whose association influence weight with the second abnormal parameter type exceeds a preset influence threshold will be regarded as the second key preference options.

[0132] Based on the historical setting frequency of the second key preference option within a preset historical period and the weight of its association with the second abnormal parameter type, the benchmark adjustment parameter is adjusted to obtain the third adjusted parameter.

[0133] Obtain the target parameter difference between the benchmark adjustment parameter and the third adjusted parameter;

[0134] A volatility performance score is obtained based on the amplitude influence parameter determined by the historical setting change amplitude of the second key preference option; the amplitude influence parameter includes the number of historical differences in the direction of amplitude change and the variance of historical change amplitude.

[0135] The target number of adjustments is determined using the fluctuation performance score as a matching criterion.

[0136] The target adjustment range is determined based on the number of target adjustments and the difference in target parameters.

[0137] The preset acquisition frequency of the sensor is adjusted according to the target adjustment range until the third adjusted parameter is reached.

[0138] Specifically, the acquisition time is the time when the data is acquired based on the preset acquisition frequency of the current sensor; the sensor can be a photosensitive sensor, temperature sensor, humidity sensor, micro vibration sensor, current sensor, or voltage sensor.

[0139] The preset acquisition frequency refers to the frequency at which the sensor acquires data under normal conditions, according to a preset time interval.

[0140] The first abnormal parameter type is the parameter type corresponding to the sensor when the difference between the collected data at three consecutive acquisition times exceeds the preset environmental threshold, and there is a pattern jump with matching values. The preset environmental thresholds include preset light intensity parameter threshold, preset environmental temperature parameter threshold, preset environmental humidity parameter threshold, preset external vibration parameter threshold, preset current threshold, and preset voltage threshold.

[0141] Numerical matching mode switching refers to the fact that the data values ​​collected by the sensor at three consecutive acquisition times belong to different modes. These modes include standard mode, power saving mode, high image quality mode, and outdoor mode.

[0142] It should be noted that the mode corresponding to the data values ​​collected by the sensor at three consecutive acquisition times is determined by matching the acquired data values ​​with the complete parameter set of each mode pre-stored based on user interaction and drive mode confirmation unit.

[0143] The initial adjustment parameters are pre-set values ​​for adjusting the acquisition frequency based on the historical data characteristics of the sensors in different scenarios, system performance requirements, and practical application experience, and are all within the range of (0, 1). By establishing a mapping relationship between the initial adjustment parameters and the corresponding parameter types of each sensor, the initial adjustment parameters can be quickly obtained according to the adjustment requirements.

[0144] For example, suppose the current photosensor collects values ​​of 40,000 lux, 50,000 lux, and 70,000 lux at three consecutive moments, respectively, and the preset environmental threshold is 1,500 lux. The difference between the two consecutive collections is 10,000 lux and 20,000 lux, respectively, both of which exceed the preset environmental threshold of 1,500 lux.

[0145] Furthermore, when the collected values ​​are 40000 lux and 50000 lux, the corresponding mode is power saving mode, and when the collected value is 70000 lux, the corresponding mode is high image quality mode. Since a mode jump occurs, the parameter type corresponding to the current photosensitive sensor, namely the light intensity parameter, is regarded as the first abnormal parameter type.

[0146] At this point, the initial adjustment parameters of the first abnormal parameter type, namely the light intensity parameter, are used. The preset sampling frequency of the current photosensitive sensor Adjustments were made to obtain the adjusted sampling frequency. .

[0147] The second abnormal parameter type is the parameter type corresponding to the current sensor when the difference between the collected data at three consecutive collection times exceeds the preset environmental threshold, but there is no numerical matching mode change.

[0148] The baseline adjustment parameter is the initial adjustment parameter that matches the second abnormal parameter type, and is used as the basis for subsequent adjustments based on user preferences.

[0149] The preset historical time period is the time range used to analyze the historical configuration parameter values ​​of preference options, such as the past week or month.

[0150] Preference options refer to the display preference options of the user interaction and drive mode confirmation unit design, including brightness preference, color preference, dynamic display preference and energy saving preference.

[0151] Historical configuration parameter values ​​are the historical configuration values ​​of user display preference options within a preset historical time period.

[0152] The historical setting change range is the difference between the user's historical configuration value for display preference options and the previous historical configuration value within a preset historical time period.

[0153] The historical preference change level is determined by matching a preset change level table with a preference change coefficient calculated by weighting the historical setting frequency and historical average setting change magnitude of the same preference option. Historical preference change levels include minor changes, moderate changes, and significant changes.

[0154] The preset change level table consists of the range of preference change coefficient values ​​and the corresponding historical preference change levels. It is established in advance through comprehensive evaluation of a large amount of user preference setting data, system performance testing, and expert experience.

[0155] The association influence weight is determined in advance based on the mining of actual user operation data and simulation experiments. It is used to represent the degree of association between preference options and parameter types. The higher the weight, the greater the influence of the preference option on the parameter type. The value range of the association influence weight is generally (0,1).

[0156] For example, assuming that the light intensity parameter of the photosensitive sensor is marked as the second anomalous parameter type, if the change level of all preference options is a minor change, and the correlation between brightness preference and light intensity parameter has the highest weight, then brightness preference is the first key preference option.

[0157] At this point, using the first key preference option: brightness preference, the historical setting frequency within the preset historical period is selected. And the weighting of the correlation with the light intensity parameter. Adjust parameters according to the corresponding benchmark Adjustments were made to obtain the first adjusted parameters. ;

[0158] Using the first adjusted parameters The preset sampling frequency of the current photosensor Adjustments were made to obtain the adjusted sampling frequency. .

[0159] The preset influence threshold is a pre-set threshold used to determine whether the correlation between preference options and parameter types is significant, and can be set to 0.7.

[0160] The option percentage is the proportion of the total number of preference options whose association influence weight with the second abnormal parameter type exceeds the preset influence threshold and whose historical preference change level is a weight change, out of the total number of preference options whose association influence weight with the second abnormal parameter type exceeds the preset influence threshold.

[0161] The preset percentage threshold is a pre-set value used to determine whether to consider preference options whose association with the second abnormal parameter type has a weight exceeding the preset influence threshold as the second key preference option. For example, the preset percentage threshold can be set to 0.5.

[0162] For example, suppose the historical setting frequency of preference options whose current association influence weight with the second abnormal parameter type exceeds the preset influence threshold within the preset historical period is as follows: The corresponding influence weights are respectively , Then adjust the baseline parameters for the current second abnormal parameter type. After adjustment, the second adjustment parameter is obtained as follows: ;

[0163] Using the second adjusted parameters The preset sampling frequency for the corresponding sensor of the current second abnormal parameter type Adjustments were made to obtain the adjusted sampling frequency. .

[0164] The second key preference option is the preference option whose association with the second abnormal parameter type has a weight exceeding a preset influence threshold when the option percentage exceeds a preset percentage threshold.

[0165] The third adjustment parameter is obtained by adjusting the baseline adjustment parameter based on the historical setting frequency of the second key preference option within a preset historical period and the weight of its association with the second abnormal parameter type.

[0166] For example, the third adjustment parameter ,in, Adjust parameters based on the baseline; The historical setting frequency of the i-th second key preference option within a preset historical period; The weight of the association between the i-th second key preference option and the second anomaly parameter type; This represents the total number of the second key preference options;

[0167] Using the third adjusted parameters The preset sampling frequency of the sensor Adjustments were made to obtain the adjusted sampling frequency. .

[0168] The target parameter difference is the parameter value of the baseline adjustment parameter minus the parameter value after the third adjustment.

[0169] The volatility performance score is a weighted average calculated by normalizing the historical frequency of differences in the direction of magnitude change and the historical variance of magnitude change for the same second key preference option. It is used to characterize the historical volatility of the current second key preference option. The weights assigned to the historical frequency of differences in the direction of magnitude change and the historical variance of magnitude change are determined based on the impact analysis of the data collection frequency adjustment, actual data verification, and expert judgment.

[0170] The historical difference in the direction of magnitude change is the number of times the direction (upward or downward) of the second key preference option setting value has changed within a preset historical period.

[0171] Historical variation variance is the variance of the variation in the second key preference option setting value within a preset historical period, reflecting the degree of fluctuation in the variation range.

[0172] The target adjustment number is the number of frequency adjustments performed on the current sensor's acquisition frequency, determined by the fluctuation performance score as a matching condition. The mapping relationship between the frequency adjustment number and the fluctuation performance score is pre-determined based on simulation experiments and statistical analysis of actual data.

[0173] The target adjustment range is the actual adjustment range of the current sensor's preset acquisition frequency, determined by dividing the target parameter difference by the target adjustment number. The acquisition frequency is gradually adjusted using this actual range until it reaches the second adjusted parameter.

[0174] The beneficial effects of the above technical solution are as follows: by comprehensively considering multiple factors such as abnormal conditions of sensor data collection, whether there are mode jumps, historical configuration parameter values ​​of user preference options and their changing trends, the sensor's preset acquisition frequency can be intelligently and accurately adjusted. This can achieve dynamic optimization of the sensor acquisition frequency according to the actual changes in environmental parameters, avoiding data redundancy or response lag caused by fixed frequency acquisition.

[0175] The working principle of the above technical solution is as follows: First, based on the data difference at three consecutive acquisition times of the sensor and whether there is a pattern jump with numerical matching, the corresponding parameter type of the sensor is marked as either the first abnormal parameter type or the second abnormal parameter type. Next, for the first abnormal parameter type, the acquisition frequency is directly adjusted using the corresponding initial adjustment parameter. For the second abnormal parameter type, its corresponding initial adjustment parameter is used as the benchmark adjustment parameter. Then, by analyzing the historical configuration parameter values ​​and change trends of user preference options within a preset historical time period, and according to different situations (such as the historical preference change level of all preference options, the proportion of option options with a historical preference change level of medium or heavy change, etc.), the benchmark adjustment parameter is adjusted in different ways using the historical setting frequency of preference options and the associated influence weight, to obtain the final adjustment parameter. This accelerates the adjustment of the sensor's preset acquisition frequency. During the adjustment process, factors such as fluctuation performance score are also considered to determine the target number of adjustments and the magnitude, so as to gradually reach a suitable acquisition frequency.

[0176] The data optimization and analysis unit is also used for:

[0177] Real-time acquisition of display data involves establishing a synchronous data capture mechanism with the signal input terminal of the display screen to capture the raw display data that is about to be sent to the display screen driver circuit. The raw display data includes dynamic video streams, static image frames, and text information.

[0178] The collected raw display data is format-standardized to convert data from different sources into a recognizable standard format; at the same time, basic noise reduction algorithms are used to remove salt-and-pepper noise and impulse interference introduced during data transmission.

[0179] The data that has been standardized is divided according to the presentation logic of the display content, and then the content of each part is marked in a structured way to confirm the display position and size range of each part.

[0180] The structured and labeled data is then processed to extract feature parameters, including brightness features, color features, dynamic features, spatial features, and dynamic and static features.

[0181] The extracted feature parameters are converted into quantifiable numerical indicators, and a real-time update mechanism is established.

[0182] The set of feature parameters that are updated in real time is packaged, and the feature parameter data that is sent to the display screen is obtained after packaging.

[0183] Specifically, the data acquisition is comprehensive and real-time, capturing raw display data through a synchronous data capture mechanism, covering dynamic video streams, static image frames, and text information to ensure no data omissions and provide a complete data source for subsequent analysis. The preprocessing stage is highly efficient, standardizing data formats from different sources to resolve compatibility issues. Basic noise reduction algorithms remove salt-and-pepper noise and impulse interference, ensuring data purity and laying a solid foundation for subsequent processing. The data is highly structured, segmenting and labeling data according to presentation logic, clearly defining display positions and size ranges, making the data clear and easy to analyze accurately. Feature extraction is comprehensive and quantifiable, extracting multi-dimensional feature parameters such as brightness and color and converting them into quantifiable indicators. A real-time update mechanism is also established to dynamically reflect changes in display data. Finally, the feature parameter set is packaged, providing accurate and practical data support for display optimization and fault diagnosis. The overall solution combines comprehensiveness and practicality.

[0184] To address the issues in existing technologies, such as limited user interfaces, lack of high-level configuration entry points, lack of parameter storage categorization and insufficient backup; asynchronous decision-making data in driver modes with fixed weights, lack of 3D adaptation calculations and hardware verification, and unclear optimization rules, which negatively impact user experience and decision-making accuracy, please refer to [link to relevant documentation]. Figure 1 and Figure 2 This embodiment provides the following technical solution:

[0185] The user interaction and drive mode confirmation unit is also used for:

[0186] Diverse user interfaces include physical interaction, touch interaction, remote interaction, and system integration interaction;

[0187] Display preference options include brightness preference, color preference, dynamic display preference, and energy saving preference;

[0188] Among them, the advanced strategy configuration options are: to open a deep configuration entry for advanced users or system administrators, including mode switching trigger conditions, policy priority settings, scene mode customization, and historical data weight adjustment;

[0189] After successful verification, the preference settings are converted into recognizable parameter commands; at the same time, a configuration log is generated, recording the setting time, user identity, and specific parameter values.

[0190] After the configuration log is generated, a storage system with preset driver mode parameters is built. The storage system construction process is as follows: an independent storage area is divided in the non-volatile storage medium, and an index directory is built according to the mode type. The mode types include standard mode, power saving mode, high image quality mode and outdoor mode. The complete parameter set of the corresponding mode is stored in each directory.

[0191] Meanwhile, temporary cache and permanent storage are allocated for dynamic parameters. The temporary cache is used for high-frequency updates. The cache data is integrated and written to the permanent storage every preset period or when the system is idle.

[0192] Finally, the current state of preset and dynamic parameters is automatically backed up periodically, storing at least 3 historical versions, and a manual backup option is provided, allowing users to actively save snapshots before important configuration changes.

[0193] Finally, the data optimization and storage are completed.

[0194] Specifically, the user interaction is convenient and diverse, constructing multiple interaction entry points such as physical and touch controls to meet the operational needs of different scenarios. Combined with display preference options such as brightness and energy saving, ordinary users can quickly customize the display effect. The configuration is flexible and professional, with a high-level strategy configuration interface that provides advanced users with in-depth configuration functions such as mode trigger conditions and priority settings, catering to both general and professional users. Data storage is secure and reliable, with independent storage areas and indexes built according to modes to ensure orderly parameter storage. Dynamic parameters are divided into temporary and permanent storage areas to ensure that frequently updated data is not lost. Regular automatic backups are performed and three historical versions are retained, and manual backups are also supported to avoid configuration loss. Operational procedures are traceable, with input content validation and the generation of configuration logs containing time and user identity for easy troubleshooting. The overall solution balances ease of use, professionalism, and security.

[0195] The driving mode decision unit is also used for:

[0196] It receives physical parameter data output by the environmental parameter acquisition unit in real time; it receives display content feature parameters generated by the display data optimization and analysis unit; and it receives historical data stored by the user interaction and drive mode confirmation unit, and ensures that the three types of data are aligned in the time dimension through a data synchronization mechanism.

[0197] The three types of input data are converted into a unified evaluation dimension. Based on the user's preset strategy priority and combined with the system's default rules, dynamic weight coefficients are assigned to physical parameter data, display content feature parameters, and historical data respectively.

[0198] Based on the core indicators of physical parameter data and display content characteristic parameters, candidate modes that meet the basic conditions are selected from the preset driving mode library, and unmatched modes are eliminated during the selection process.

[0199] The selected candidate patterns are subjected to three-dimensional adaptation calculation, which includes environmental adaptation score, content adaptation score and historical fit score. The scores of each category are weighted and summed according to their corresponding weights to obtain the comprehensive adaptation value of the candidate patterns.

[0200] The overall adaptation value is corrected based on real-time operating status data, which includes the current screen temperature, drive circuit load, and pixel response delay.

[0201] At the same time, it verifies whether the candidate patterns meet the hardware physical limitations and eliminates the patterns that do not meet the constraints;

[0202] The optimal driving mode is selected from the revised candidate modes, based on the overall fit value. If multiple modes have the same score, the option that matches the mode most recently manually confirmed by the user is selected first, or the mode with the most stable historical operation of the system is selected by default.

[0203] Finally, the optimal driving mode was confirmed.

[0204] The overall fit value was adjusted, including:

[0205] Acquire and analyze the data of the current screen temperature, the drive circuit load, and the pixel response delay, and compare them with the corresponding preset state thresholds to determine the adaptation correction coefficient;

[0206] The overall fit value is corrected using the aforementioned fit correction coefficient.

[0207] Specifically, the preset state thresholds are determined in advance based on the actual performance parameters, design specifications, historical operating data, industry standards and safety regulations of the screen, driving circuit and pixels, including but not limited to preset screen temperature thresholds, preset load state thresholds (such as preset current state thresholds, preset voltage state thresholds) and preset pixel response delay thresholds.

[0208] The formula for calculating the adaptation correction factor is as follows:

[0209]

[0210] In the formula, Represented as the adaptation correction coefficient; t represents the preset screen temperature threshold; t represents the current screen temperature. This represents the weight of the influence of screen temperature on determining the adaptation correction coefficient. The preset load state threshold is represented as the load data of the j-th drive circuit. Represented as the real-time value of the load data of the j-th type of drive circuit; This represents the number of data categories for the drive circuit load. The load data of the j-th type of drive circuit, and the weight of its influence on the operation of the drive circuit, are determined in advance based on expert experience, experimental test data, and in-depth analysis of the function of the drive circuit. This is expressed as the weight of the influence of the drive circuit load on determining the adaptation correction coefficient; This is represented as the preset pixel response delay threshold; This represents the response delay of the current pixel; denoted as the weight of the effect of pixel response delay on determining the adaptation correction coefficient; ln represents the natural logarithm; e represents the base of the natural logarithm, with a value of 2.7; This represents the percentage of real-time operating status data that is less than a preset state threshold out of all real-time operating status data. This real-time operating status data includes screen temperature, drive circuit load (such as current, voltage, power), and pixel response delay.

[0211] The weights assigned to the screen temperature, drive circuit load, and pixel response delay data are obtained by solving the matrix constructed by pairwise comparison and scoring using the analytic hierarchy process, and the values ​​range from (0, 1).

[0212] For example, using the adaptation correction coefficient For the overall fit value After making corrections, the corrected overall fit value is equal to .

[0213] The beneficial effects of the above technical solution are as follows: by comparing the data of the current screen temperature, the load of the driving circuit and the pixel response delay with the corresponding preset state thresholds, the adaptation correction coefficient is calculated and the comprehensive adaptation value is corrected, which can provide an effective and accurate basis for determining the final mode, making the driving mode decision more accurate and reliable.

[0214] Specifically, the data processing system is comprehensive, ensuring accurate decision-making. The solution receives three key types of information in real time: environmental parameters, display content characteristics, and historical data. A data synchronization mechanism aligns these information across time dimensions, preventing decision-making biases caused by misaligned data sequences. Simultaneously, the three types of data are converted into a unified evaluation dimension, and dynamic weight coefficients are assigned based on user-preset strategy priorities and system default rules. This respects individual user needs while ensuring the rationality of data weight allocation, providing high-quality data support for subsequent decisions. The mode selection and calculation logic is scientific, improving decision accuracy. First, based on core indicators of environmental parameters and display content characteristics, candidate modes are selected from a preset mode library, and mismatched options are eliminated, reducing unnecessary calculations. Then, through three-dimensional adaptation calculations, combined with dynamic weights, a comprehensive adaptation value is derived. Real-time operating status data such as screen temperature and drive circuit load are also incorporated to correct the score, further aligning with the actual system operation and making the evaluation results more accurate. The decision-making mechanism is rigorous yet flexible, balancing reliability and user experience. During the screening process, the candidate modes are checked to ensure they meet the hardware physical limitations, thus avoiding damage to the device due to mode compatibility issues. When selecting the optimal mode, if there is a tie, the mode most recently manually confirmed by the user or the most stable mode in history is prioritized. This ensures both system stability and user habits. The overall solution makes the driver mode decision more accurate and reliable, fully adapting to the needs of actual application scenarios.

[0215] To address the issues in existing technologies, such as poor parameter conversion and signal synchronization during display mode switching, abrupt transitions, insufficient drive precision due to lack of real-time feedback correction, few status monitoring points, high data noise, lack of tiered anomaly handling mechanisms, lack of simulation verification for optimization, and difficulty in reusing experience, which negatively impact display performance and system stability, please refer to [link to relevant documentation]. Figure 1 and Figure 2 This embodiment provides the following technical solution:

[0216] The drive signal execution unit is also used for:

[0217] The instruction contains the complete parameter set corresponding to the mode. The parameter set is structured and parsed to extract the core control indicators corresponding to the hardware driver circuit and confirm the physical meaning of the core control indicators.

[0218] The mapping relationship between mode parameters and hardware drive signals is as follows: abstract parameters are converted into specific electrical signal parameters; timing parameters are converted into the frequency and phase of the corresponding clock signal; color parameters are converted into the output voltage curve of the digital-to-analog converter.

[0219] The multi-parallel drive signals include pixel drive signals, backlight drive signals, timing synchronization signals, and auxiliary control signals;

[0220] The generated multi-channel parallel drive signals are sent to the signal amplification circuit, where the signals are enhanced according to the impedance characteristics and power requirements of the hardware drive circuit. At the same time, the signal output characteristics are adjusted through the impedance matching circuit.

[0221] The amplified multi-channel drive signals are then synchronously output to the corresponding hardware circuit modules according to a preset timing sequence.

[0222] A transition mechanism is introduced during the switching from the current mode to the target mode. The transition mechanism is as follows: voltage and current signals are gradually changed in a step-by-step manner; timing signals are first sent with a pre-switching command to put the hardware circuit into a ready state before the switch is completed; and pixel brightness compensation values ​​are adjusted synchronously for backlight brightness and visual sensitivity parameters.

[0223] During the output of the driving signal, the parameters of the actual output signal are collected in real time through the feedback interface in the hardware circuit and compared with the target parameters.

[0224] If the deviation is within the preset range, the signal drive is deemed effective; if the deviation exceeds the limit, the fine-tuning mechanism is immediately activated to correct the output until it meets the target value.

[0225] Finally, the display mode switch is completed.

[0226] Specifically, precise parameter analysis lays the foundation for the driving process. After receiving the optimal driving mode command, the solution performs structured analysis on the complete parameter set, accurately extracting the core control indicators corresponding to the hardware driving circuit and clarifying their physical meaning. This avoids driving errors caused by misunderstandings of parameters, providing a clear basis for subsequent signal generation. The signal mapping is highly adaptable and meets hardware requirements. Based on the display hardware specifications, abstract parameters are transformed into specific electrical signal parameters, timing parameters correspond to clock signal frequency and phase, and color parameters are transformed into the output voltage curve of the digital-to-analog converter. This achieves precise matching between parameters and hardware driving signals, ensuring that the driving signals conform to the hardware operating characteristics, smooth mode switching, and guaranteeing visual and hardware safety. A transition mechanism is introduced during the switching process: voltage and current signals change gradually in a stepped manner, timing signals send pre-switching commands first, and visually sensitive parameters are adjusted and compensated synchronously. This avoids signal abrupt changes from impacting the hardware and prevents problems such as flickering and abrupt changes in the display screen, balancing hardware protection and visual experience. Fourth, closed-loop output control ensures driving reliability. The actual output signal parameters are collected in real time through the hardware feedback interface and compared with the target parameters. When the deviation exceeds the limit, the fine-tuning mechanism is immediately activated to correct it, forming a closed-loop control of output, monitoring and correction. This effectively avoids problems such as signal attenuation and interference, and ensures that the drive signal always meets the target requirements. The overall solution makes the display mode switching accurate, stable and reliable, and fully meets the requirements of hardware operation and display effect.

[0227] The condition monitoring and adjustment unit is also used for:

[0228] The monitoring points are deployed as follows: For the LCD screen, the panel operating temperature is monitored by a temperature sensor built into the edge of the screen, and the brightness uniformity and pixel response delay of each area are monitored by reference pixels in the pixel array; For the driving system, real-time power consumption, core circuit operating voltage and current are collected through the detection interface of the power management module, signal transmission delay and data error rate are recorded through the timing controller, and the luminous intensity and color temperature stability of the backlight module are monitored through the feedback terminal of the backlight driving circuit; each monitoring point collects data at a high frequency period of 10-100ms.

[0229] The layered processing involves: removing high-frequency noise through sliding window filtering, then validating the data and eliminating outliers caused by sensor malfunctions or transmission interference; finally, aligning the multi-source data by timestamp to form a structured state dataset.

[0230] Based on the degree and trend of deviation, the operating status is divided into four levels: normal, slightly abnormal, moderately abnormal, and severely abnormal.

[0231] Optimization plans are formulated based on different anomaly levels. The generated optimization parameters are first verified in a simulation environment based on the formulated optimization plan, and then implemented in stages after the verification is successful.

[0232] Then, the triggering conditions, adjusted parameters, and implementation effects of each optimization are recorded as optimization logs, and stored in association with the corresponding environmental parameters and display content characteristics;

[0233] Finally, the optimized state data is fed back to the driving mode decision unit in real time as a reference for the next mode decision.

[0234] Specifically, monitoring points such as screen temperature and brightness uniformity are deployed for the display screen, and data such as power consumption and signal delay are collected for the drive system, achieving full-dimensional monitoring of the screen and drive. A high-frequency acquisition cycle of 10-100ms can quickly capture instantaneous state changes, avoiding the omission of key operational information. Rigorous data processing ensures data reliability. High-frequency noise is removed through sliding window filtering, outliers are eliminated through effectiveness verification, and multi-source data is aligned by timestamp to form a structured dataset. This layered data purification provides a high-quality data foundation for subsequent analysis, reducing the impact of data errors on evaluation results. State assessment is scientific, and anomaly classification is clear. Deviation is calculated based on the current drive mode parameters, classifying states into four anomaly levels. This avoids a "one-size-fits-all" evaluation approach, accurately pinpointing the severity of problems and providing a basis for differentiated optimization. Optimization execution is safe and controllable, balancing effectiveness and stability. Optimization solutions are first verified in a simulated environment and then implemented in stages after successful verification, avoiding direct adjustments that could impact the system. Simultaneously, optimization logs are recorded and associated with environmental and display characteristic data, facilitating traceability of effects and subsequent improvements. Fifth, a decision-making closed loop is formed, improving overall adaptability. The optimized status data is fed back to the drive mode decision unit in real time, providing the latest basis for the next decision, making the drive mode adjustment more in line with the real-time status of the system, forming a virtuous cycle of monitoring, optimization, feedback and decision-making, and continuously improving the system's operating performance.

[0235] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0236] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. A liquid crystal display driving system with an intelligent switching driving mode strategy, characterized in that, include: The environmental parameter acquisition unit is used to collect physical parameter data related to the operating environment of the display screen in real time. First, confirm the parameter type of the physical parameter data, and then confirm the sensor based on the parameter type; The display data optimization and analysis unit is used to collect display data in real time and analyze the characteristic parameters of the display data that is about to be sent to the display screen in real time. The user interaction and drive mode confirmation unit provides a user interface, allows users to set display preferences, and provides the system with advanced strategy configuration options. It also stores preset drive mode parameters and dynamically updated optimization parameters. The provision of user interfaces includes: building multi-dimensional user interaction entry points, which provides diverse user operation interfaces; The drive mode decision unit is used to determine the optimal drive mode based on physical parameter data, display content characteristic parameters, and historical data. Among them, based on the core indicators of physical parameter data and display content characteristic parameters, candidate modes that meet the basic conditions are selected from the preset driving mode library, and unmatched modes are eliminated during the selection process. And perform 3D adaptation calculations on the selected candidate patterns; The environmental parameter acquisition unit is also used for: If the difference between the collected data at three consecutive collection times of the sensor exceeds the preset environmental threshold and there is a pattern jump with matching values, the parameter type corresponding to the current sensor is marked as the first abnormal parameter type; the three consecutive collection times are the current collection time and two adjacent historical collection times. The preset acquisition frequency of the sensor is adjusted using the initial adjustment parameters that match the first abnormal parameter type. If the difference between the collected data at three consecutive collection times of the sensor exceeds the preset environmental threshold and there is no pattern jump with matching values, the parameter type corresponding to the current sensor is marked as the second abnormal parameter type. The initial adjustment parameter corresponding to the second abnormal parameter type is regarded as the baseline adjustment parameter; Extract the historical configuration parameter values ​​of each preference option within a preset historical time period, and perform trend analysis to obtain the corresponding historical preference change characteristics; the historical preference change characteristics include historical setting frequency, historical setting change magnitude, and historical preference change level; When the historical preference change level of all the aforementioned preference options is a minor change, the preference option with the highest association influence weight with the second abnormal parameter type is regarded as the first key preference option; By utilizing the historical setting frequency of the first key preference option within a preset historical period and its association influence weight with the second abnormal parameter type, the benchmark adjustment parameter is adjusted to obtain the first adjusted parameter; The preset acquisition frequency of the sensor is adjusted based on the first adjusted parameters; When the historical preference change level of the preference option is a moderate change or a heavy change, obtain the proportion of preference options whose association influence weight with the second abnormal parameter type exceeds the preset influence threshold and whose historical preference change level is a heavy change. If the percentage of the options does not exceed the preset percentage threshold, the baseline adjustment parameters are adjusted by using the historical setting frequency and corresponding associated influence weight of the preference options whose correlation influence weight with the second abnormal parameter type exceeds the preset influence threshold within a preset historical period, to obtain the second adjusted parameters. The preset acquisition frequency of the sensor is adjusted based on the second adjusted parameters; If the proportion of the options exceeds a preset proportion threshold, the preference options whose association influence weight with the second abnormal parameter type exceeds a preset influence threshold will be regarded as the second key preference options. Based on the historical setting frequency of the second key preference option within a preset historical period and the weight of its association with the second abnormal parameter type, the benchmark adjustment parameter is adjusted to obtain the third adjusted parameter. Obtain the target parameter difference between the benchmark adjustment parameter and the third adjusted parameter; A volatility performance score is obtained based on the amplitude influence parameter determined by the historical setting change amplitude of the second key preference option; the amplitude influence parameter includes the number of historical differences in the direction of amplitude change and the variance of historical change amplitude. The target number of adjustments is determined using the fluctuation performance score as a matching criterion. The target adjustment range is determined based on the number of target adjustments and the difference in target parameters. The preset acquisition frequency of the sensor is adjusted according to the target adjustment range until the third adjusted parameter is reached.

2. The liquid crystal display driving system with intelligent switching driving mode strategy according to claim 1, characterized in that, The environmental parameter acquisition unit is also used for: Each sensor collects parameter data according to a preset acquisition frequency. After the parameter data is collected, an A / D converter is used to convert the analog signal into a digital signal. Meanwhile, during the parameter data acquisition process, an RC low-pass filter is used for filtering and noise reduction. The parameter types include light intensity parameters, ambient temperature parameters, ambient humidity parameters, external vibration parameters, and power supply environment parameters; Among them, the light intensity parameter is collected using a photosensitive sensor; the ambient temperature parameter is collected using a temperature sensor; the ambient humidity parameter is collected using a humidity sensor; the external vibration parameter is collected using a miniature vibration sensor; and the power supply environment parameter is collected through a current sensor connected in series in the power supply circuit of the drive system and a voltage sensor connected in parallel. The parameter data after filtering and denoising is validated. The validation process involves determining whether the collected parameter data is within the normal range of the corresponding sensor, comparing the difference between two sets of data, and removing invalid and abnormal data. After the validity verification is completed, the physical parameter data is obtained.

3. The liquid crystal display driving system with intelligent switching driving mode strategy according to claim 2, characterized in that, The display data optimization and analysis unit is also used for: Real-time acquisition of display data involves establishing a synchronous data capture mechanism with the signal input terminal of the display screen to capture the raw display data that is about to be sent to the display screen driver circuit. The raw display data includes dynamic video streams, static image frames, and text information. The collected raw display data is format-standardized to convert data from different sources into a recognizable standard format; at the same time, basic noise reduction algorithms are used to remove salt-and-pepper noise and impulse interference introduced during data transmission. The data that has been standardized is divided according to the presentation logic of the display content, and then the content of each part is marked in a structured way to confirm the display position and size range of each part. Extract feature parameters from the structured and labeled data; The extracted feature parameters are converted into quantifiable numerical indicators, and a real-time update mechanism is established. The set of feature parameters that are updated in real time is packaged, and the feature parameter data that is sent to the display screen is obtained after packaging.

4. The liquid crystal display driving system with intelligent switching driving mode strategy according to claim 3, characterized in that, The user interaction and drive mode confirmation unit is also used for: Design the display preference options. After the display preference options are designed, develop the high-level strategy configuration option interface. After both the display preference options and the high-level strategy configuration options are set, validate the legality of the input content. Diverse user interfaces include physical interaction, touch interaction, remote interaction, and system integration interaction; Display preference options include brightness preference, color preference, dynamic display preference, and energy saving preference; Among them, the advanced strategy configuration options are: to open a deep configuration entry for advanced users or system administrators, including mode switching trigger conditions, policy priority settings, scene mode customization, and historical data weight adjustment; After successful verification, the preference settings are converted into recognizable parameter commands; at the same time, a configuration log is generated, recording the setting time, user identity, and specific parameter values. After the configuration log is generated, a storage system with preset driver mode parameters is built. The storage system construction process is as follows: an independent storage area is divided in the non-volatile storage medium, and an index directory is built according to the mode type. The mode types include standard mode, power saving mode, high image quality mode and outdoor mode. The complete parameter set of the corresponding mode is stored in each directory. Meanwhile, temporary cache and permanent storage are allocated for dynamic parameters. The temporary cache is used for high-frequency updates. The cache data is integrated and written to the permanent storage every preset period or when the system is idle. Finally, the current state of preset and dynamic parameters is automatically backed up periodically, storing at least 3 historical versions, and a manual backup option is provided, allowing users to actively save snapshots before important configuration changes. Finally, the data optimization and storage are completed.

5. The liquid crystal display driving system with intelligent switching driving mode strategy according to claim 4, characterized in that, The driving mode decision unit is also used for: It receives physical parameter data output by the environmental parameter acquisition unit in real time; it receives display content feature parameters generated by the display data optimization and analysis unit; and it receives historical data stored by the user interaction and drive mode confirmation unit, and ensures that the three types of data are aligned in the time dimension through a data synchronization mechanism. The three types of input data are converted into a unified evaluation dimension. Based on the user's preset strategy priority and combined with the system's default rules, dynamic weight coefficients are assigned to physical parameter data, display content feature parameters, and historical data respectively. The three-dimensional adaptation calculation includes environment adaptation score, content adaptation score and historical fit score. The scores of each category are weighted and summed according to their corresponding weights to obtain the comprehensive adaptation value of the candidate mode. The overall adaptation value is corrected based on real-time operating status data, which includes the current screen temperature, drive circuit load, and pixel response latency. At the same time, it verifies whether the candidate patterns meet the hardware physical limitations and eliminates the patterns that do not meet the constraints; The optimal driving mode is selected from the revised candidate modes, based on the overall fit value. If multiple modes have the same score, the option that matches the mode most recently manually confirmed by the user is selected first, or the mode with the most stable historical operation of the system is selected by default. Finally, the optimal driving mode was confirmed.

6. The liquid crystal display driving system with intelligent switching driving mode strategy according to claim 5, characterized in that, The overall fit value was adjusted, including: Acquire and analyze the data of the current screen temperature, the drive circuit load, and the pixel response delay, and compare them with the corresponding preset state thresholds to determine the adaptation correction coefficient; The overall fit value is corrected using the aforementioned fit correction coefficient.

7. The liquid crystal display driving system with intelligent switching driving mode strategy according to claim 6, characterized in that, Also includes: The drive signal execution unit is used to generate corresponding drive signals according to the target drive mode, and control the hardware drive circuit according to the drive signals to complete the switching of display mode; Specifically, the instruction for the optimal driving mode is received, and a mapping relationship between the mode parameters and the hardware driving signals is established according to the display hardware specifications. Based on the mapped electrical signal parameters, multiple parallel driving signals are generated through the timing controller. The instruction contains the complete parameter set corresponding to the mode. The parameter set is structured and parsed to extract the core control indicators corresponding to the hardware driver circuit and confirm the physical meaning of the core control indicators. The mapping relationship between mode parameters and hardware drive signals is as follows: abstract parameters are converted into specific electrical signal parameters; timing parameters are converted into the frequency and phase of the corresponding clock signal; color parameters are converted into the output voltage curve of the digital-to-analog converter. The multi-parallel drive signals include pixel drive signals, backlight drive signals, timing synchronization signals, and auxiliary control signals; The generated multi-channel parallel drive signals are sent to the signal amplification circuit, where the signals are enhanced according to the impedance characteristics and power requirements of the hardware drive circuit. At the same time, the signal output characteristics are adjusted through the impedance matching circuit.

8. The liquid crystal display driving system with intelligent switching driving mode strategy according to claim 7, characterized in that, The drive signal execution unit is also used for: The amplified multi-channel drive signals are then synchronously output to the corresponding hardware circuit modules according to a preset timing sequence. A transition mechanism is introduced during the switch from the current mode to the target mode. The transition mechanism is: a step-by-step gradual change is used for voltage and current signals. For timing signals, a pre-switching command is sent first to put the hardware circuit into a ready state before the switching is completed; for backlight brightness and visual sensitivity parameters, the pixel brightness compensation value is adjusted synchronously. During the output of the driving signal, the parameters of the actual output signal are collected in real time through the feedback interface in the hardware circuit and compared with the target parameters. If the deviation is within the preset range, the signal drive is deemed effective. If the deviation exceeds the limit, the fine-tuning mechanism will be activated immediately to correct the output until it meets the target value; Finally, the display mode switch is completed.

9. The liquid crystal display driving system with intelligent switching driving mode strategy according to claim 8, characterized in that, Also includes: The status monitoring and adjustment unit is used to monitor the operating status of the LCD screen and drive system in real time, and dynamically optimize the drive mode parameters based on the operating status feedback. Before real-time monitoring of the operating status of the LCD screen and drive system, monitoring points are deployed, and the collected raw status data is processed in layers. The deviation between the real-time operating status data and the benchmark value is calculated based on the currently effective drive mode parameters. The monitoring points are deployed as follows: For the LCD screen, the panel operating temperature is monitored by a temperature sensor built into the edge of the screen, and the brightness uniformity and pixel response delay of each area are monitored by reference pixels in the pixel array; For the driving system, real-time power consumption, core circuit operating voltage and current are collected through the detection interface of the power management module, signal transmission delay and data error rate are recorded through the timing controller, and the luminous intensity and color temperature stability of the backlight module are monitored through the feedback terminal of the backlight driving circuit; each monitoring point collects data at a high frequency period of 10-100ms. The layered processing involves: removing high-frequency noise through sliding window filtering, then validating the data and eliminating outliers caused by sensor malfunctions or transmission interference; finally, aligning the multi-source data by timestamp to form a structured state dataset. Based on the degree and trend of deviation, the operating status is divided into four levels: normal, slightly abnormal, moderately abnormal, and severely abnormal. Optimization plans are formulated based on different anomaly levels. The generated optimization parameters are first verified in a simulation environment based on the formulated optimization plan, and then implemented in stages after the verification is successful. Then, the triggering conditions, adjusted parameters, and implementation effects of each optimization are recorded as optimization logs, and stored in association with the corresponding environmental parameters and display content characteristics; Finally, the optimized state data is fed back to the driving mode decision unit in real time as a reference for the next mode decision.

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