An automatic adjusting control system based on liquid crystal display screen backlight

Abstract

The application relates to the technical field of liquid crystal display screen control, and discloses an automatic backlight adjusting control system based on a liquid crystal display screen. The system environment sensing unit is responsible for capturing environmental light parameters and display content parameters; the scene analysis unit pre-processes the parameters, generates adjusting parameters in combination with a fault database; the data fusion unit schedules backlight material data, calculates light efficiency indexes, constructs a light propagation model, and outputs a parameter priority sequence; the prototype construction unit builds a backlight system prototype, collects relevant data, analyzes signal response characteristics of brightness uniformity data; the configuration management unit determines installation area size and working environment constraints, and collects adaptability parameters; the performance evaluation unit calculates life prediction indexes and system adaptability; and the control synthesis unit integrates various data, selects the best backlight material, and generates a backlight control instruction. The system can intelligently adjust the backlight of the liquid crystal display screen, improve display effect, reduce energy consumption, and prolong service life.

Description

Technical Field

[0001] This invention relates to the field of liquid crystal display control technology, specifically to an automatic backlight adjustment control system for liquid crystal displays. Background Technology

[0002] In today's digital age, LCD screens, with their significant advantages such as thinness, low power consumption, and no radiation, have permeated all aspects of people's lives and work. From everyday mobile phones and tablets to essential office computer monitors and home entertainment centers like televisions, LCD screens are ubiquitous. In smartphones, they present users with clear and vibrant images, text, and videos, supporting interactive interfaces for various applications; on computer monitors, they meet the high requirements for image precision and color reproduction in different scenarios such as office work, design, and gaming; and in the television field, large-size LCD screens deliver an immersive viewing experience, making viewers feel as if they are in the movie scene. Furthermore, in professional fields such as industrial control, medical equipment, and automotive displays, LCD screens also play an indispensable role, becoming a key medium for information display and human-computer interaction.

[0003] Traditional LCD screen backlight adjustment methods present numerous inconveniences. Many monitors either have a fixed backlight brightness or only allow for limited manual adjustment. In practical use, this method causes significant problems. For example, when using electronic devices in bright outdoor sunlight during the day, a fixed-brightness screen often appears dim, making it difficult to see the content; conversely, at night, when ambient light dims, an overly bright screen can irritate the eyes and cause eye strain if the backlight brightness is not manually reduced. Furthermore, manual adjustment requires frequent operation of the device's menus or buttons, a cumbersome process that easily distracts the user and fails to automatically adapt to changes in ambient light, thus failing to meet users' needs for convenience and comfort.

[0004] While existing technologies have made some progress in automatic backlight adjustment for LCD screens, significant shortcomings remain. Some current automatic adjustment systems rely solely on ambient light sensors to obtain ambient light intensity information and adjust backlight brightness accordingly, completely ignoring the characteristics of the displayed content itself. For example, when displaying a dark-toned image or video, even in strong ambient light, adjusting the backlight based solely on ambient light may result in an overly bright image, losing detail in dark areas; conversely, when displaying bright-toned content, the image may appear too dim in darker environments. Furthermore, these technologies have poor adaptability to complex and changing environments. In scenarios with rapidly changing light (such as in-vehicle displays entering and exiting tunnels), they cannot quickly and accurately adjust the backlight, easily leading to sudden brightness changes or adjustment lag. Simultaneously, existing technologies lack comprehensive consideration in backlight material selection and system adaptation. They fail to fully explore the intrinsic properties and structural configurations of different materials and their impact on light efficiency, and they are not optimized for different installation area sizes and working environment constraints. Consequently, in practical applications, the performance of the LCD screen cannot be fully utilized, making it difficult to achieve optimal display effects and stability. Therefore, there is an urgent need for a more sophisticated and intelligent automatic backlight adjustment control system to solve the above problems and improve the performance of LCD screens and user experience. Summary of the Invention

[0005] The purpose of this invention is to provide an automatic backlight adjustment control system based on a liquid crystal display screen to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides an automatic backlight adjustment control system based on a liquid crystal display screen, the system comprising:

[0007] The environmental sensing unit captures ambient light parameters and display content parameters;

[0008] The scene analysis unit preprocesses the ambient light parameters and display content parameters, extracts working environment features, and generates corresponding adjustment parameters by combining them with historical fault records in the fault database.

[0009] The data fusion unit schedules the intrinsic property data and structural configuration data of the backlight material, calculates the luminous efficacy characterization index, and constructs the light propagation path model; it evaluates the synergistic relationship between the luminous efficacy characterization index and the light propagation path model, and outputs the parameter priority sequence.

[0010] The prototype construction unit queries the material library for core backlight materials and alternative backlight materials, simulates and builds a backlight system prototype based on the materials, and collects the brightness uniformity data and durability test data of the prototype; it also analyzes the signal response characteristics in the brightness uniformity data.

[0011] The configuration management unit determines the installation area size and working environment constraints of the backlight unit in the LCD screen, and collects the prototype's adaptability parameters;

[0012] The performance evaluation unit calculates life prediction indicators based on durability test data and determines the performance stability level based on the life prediction indicators; it also calculates the system adaptability by combining the installation area size, working environment constraints, and adaptability parameters.

[0013] The control synthesis unit integrates signal response characteristics, performance stability level, and system adaptability to select the best material from the core backlight material and alternative backlight materials, and generates backlight control commands.

[0014] Preferably, the specific steps for the environmental sensing unit to capture ambient light parameters and display content parameters include:

[0015] The ambient light sensor is calibrated to obtain real-time ambient light intensity, and the brightness distribution histogram of the displayed content is extracted through the graphics processing interface. The ambient light intensity is filtered to eliminate noise interference, and the brightness distribution histogram is normalized. Based on the normalized data, the dynamic adaptation coefficient between the ambient light and the displayed content is calculated. According to the dynamic adaptation coefficient, the parameter sampling frequency and accuracy threshold are adjusted.

[0016] Preferably, the step of the scene parsing unit generating the corresponding adjustment parameters includes:

[0017] The working environment characteristics are analyzed to obtain environmental characteristic factors; historical fault records are classified to obtain a fault type set; the correlation between environmental characteristic factors and fault type set is analyzed to generate an association mapping table; based on the association mapping table, key environmental characteristics and high-risk fault types are identified; combined with key environmental characteristics and high-risk fault types, the design constraints for backlight adjustment are determined; and according to the design constraints, the numerical range of the corresponding adjustment parameters is defined.

[0018] Preferably, the step of the data fusion unit calculating the luminous efficacy characterization index includes:

[0019] The intrinsic property data of the backlight material is standardized to obtain standard material data; the optical property set of the material, including transmittance and refractive index, is extracted from the standard material data; key properties in the optical property set are screened and the performance indicators corresponding to the key properties are calculated; based on the performance indicators, a data sequence of light efficiency characterization indicators is generated.

[0020] Preferably, the step of the data fusion unit constructing the optical propagation path model includes:

[0021] The structural configuration data is processed to unify the format to obtain the target structural data; the structural dimension parameter set of the backlight unit is parsed from the target structural data; the structural dimension parameter set is correlated and matched with the optical properties of the material to obtain the attribute correlation parameter set; a light propagation numerical simulation model is established based on the attribute correlation parameter set; the light propagation numerical simulation model is simulated to obtain the dynamic distribution data of light propagation; the dynamic distribution data of light propagation is processed to abstract the path to generate the topology of the light propagation path model.

[0022] Preferably, the step of the data fusion unit evaluating the collaborative relationship includes:

[0023] The propagation feature vectors in the light propagation path model are extracted and their dimensionality is reduced to obtain a simplified feature set. The feature similarity index within the simplified feature set is calculated, as well as the index similarity index between the light efficiency characterization indicators. The correlation factor between the light propagation path model and the light efficiency characterization indicators is calculated. Combining the correlation factor, feature similarity index, and index similarity index, a weighted fusion algorithm is used to calculate the cooperative coupling degree. Based on the cooperative coupling degree, the ranking result of the parameter priority sequence is output.

[0024] Preferably, the step of analyzing signal response characteristics in the prototype building unit includes:

[0025] Outlier removal is performed on the brightness uniformity data to obtain cleaned data. Time-domain and frequency-domain features, including brightness fluctuation rate and spectral distribution, are extracted from the cleaned data. Based on the time-domain and frequency-domain features, a signal characteristic descriptor is constructed. The signal characteristic descriptor is used to analyze the signal response delay and stability of the backlight system prototype.

[0026] Preferably, the step of the performance evaluation unit calculating the lifetime prediction index includes:

[0027] The test parameter set in the durability test data is analyzed to query the common failure modes and failure mechanisms of the backlight unit; the failure modes and failure mechanisms are decomposed into variables to obtain life-influencing variables; based on the life-influencing variables, life assessment criteria are set; a subset of parameters related to the life assessment criteria is selected from the test parameter set; based on the durability test data, the life score of each parameter in the parameter subset is calculated; weight coefficients are assigned to the parameter subset, and the life prediction index is calculated by linear combination of the life score and weight coefficients.

[0028] Preferably, the step of the performance evaluation unit calculating the system adaptability includes:

[0029] Based on the installation area dimensions, the physical dimensional parameters of the backlight system prototype are measured, including length, width, and height; the dimensional matching degree between the installation area dimensions and the physical dimensional parameters is calculated; environmental factors in the adaptability parameters are analyzed, including temperature range and humidity range; environmental factor adaptability is calculated by combining working environment constraints and adaptability parameters; and the dimensional matching degree and environmental factor adaptability are integrated to obtain the system adaptability through a geometric mean algorithm.

[0030] Preferably, the step of the control synthesis unit generating backlight control commands includes:

[0031] The system compares the priority of signal response characteristics, performance stability level, and system adaptability, and sets screening conditions. Based on the screening conditions, it selects the best material from the core backlight material and alternative backlight materials. It integrates the corresponding adjustment parameters and parameter priority sequence to generate a parameter optimization list. Combining the characteristic parameters of the best material and the parameter optimization list, it synthesizes backlight control instructions through a rule engine for real-time adjustment of backlight brightness.

[0032] Compared with the prior art, the beneficial effects of the present invention are:

[0033] The environmental perception unit in this invention acts like a keen "observer," accurately capturing ambient light parameters and display content parameters. Whether in bright sunlight outdoors or dim indoor lighting, it can quickly and accurately perceive the intensity and color of ambient light, while simultaneously performing detailed analysis of the brightness and color distribution of the displayed content. The scene analysis unit acts like a wise "analyst," preprocessing these parameters and combining them with historical fault records in the fault database to extract key working environment characteristics, thereby generating corresponding adjustment parameters. This allows the backlight system to intelligently adjust according to changes in the environment and displayed content. For example, when a user watches a video outdoors in strong sunlight, the system automatically increases the backlight brightness to ensure clear visibility; while when switching to a darker indoor environment and displaying text, the backlight brightness decreases accordingly, ensuring clear text readability while avoiding excessive screen brightness that could strain the eyes, greatly improving the user's visual experience and allowing them to enjoy a comfortable and clear display in various scenarios.

[0034] The data fusion unit plays a crucial role in this invention, acting as an efficient "scheduler" that meticulously manages the intrinsic property data and structural configuration data of the backlight materials. Through in-depth analysis and calculation of this data, it derives luminous efficacy characterization indicators and constructs a precise light propagation path model. In practical applications, it can optimize the light propagation path based on the characteristics and structural design of different backlight materials, resulting in a more uniform distribution of light on the LCD screen and improved light energy utilization. For example, for certain backlight materials with special optical properties, the data fusion unit can rationally adjust the layout of the light source and the direction of light propagation based on their intrinsic properties, reducing light scattering and loss, thereby achieving higher brightness and better display effects with the same energy consumption. This not only improves display quality but also reduces energy consumption, aligning with the trend of energy conservation and environmental protection, saving users energy costs, and reducing environmental impact.

[0035] The prototyping unit meticulously selects core and alternative backlight materials from the material library, and uses these materials to simulate and build a prototype backlight system, much like a skilled craftsman creating an experimental product. Subsequently, it collects brightness uniformity and durability test data from the prototype, and conducts in-depth analysis of the signal response characteristics in the brightness uniformity data. The performance evaluation unit calculates lifespan prediction indicators based on the durability test data, determines the performance stability level accordingly, and calculates system adaptability by combining the installation area size, working environment constraints, and compatibility parameters. This series of operations ensures that the backlight system can operate stably and reliably in practical applications. For example, by conducting durability tests on the prototype, potential problems with materials during long-term use, such as aging and brightness decay, can be identified in advance. This allows for timely adjustments to material selection or optimization of system design, extending the lifespan of the backlight system, reducing the frequency of equipment maintenance and replacement, and providing users with a more durable and stable display service.

[0036] The configuration management unit accurately determines the installation area size and operating environment constraints of the backlight unit within the LCD screen and collects the prototype's adaptability parameters, acting like a meticulous "planner" to fully prepare for system installation and operation. The control synthesis unit integrates signal response characteristics, performance stability levels, and system adaptability, selecting the optimal material from the core and alternative backlight materials and generating backlight control commands. This process ensures the system's high adaptability to different LCD screens and operating environments. For example, on LCD screens of different sizes, the most suitable backlight material and layout are selected based on the size and shape of the installation area, allowing the backlight system to seamlessly integrate into the display, fully leveraging its performance advantages, enhancing display effects, and providing users with a superior and more realistic visual experience, meeting their demands for high-quality displays. Attached Figure Description

[0037] Figure 1 This is a schematic diagram illustrating the working principle of the automatic backlight adjustment control system based on a liquid crystal display screen as described in this invention.

[0038] Figure 2 A flowchart of the steps for capturing parameters for the environmental sensing unit;

[0039] Figure 3 A flowchart illustrating the steps involved in calculating the luminous efficacy characterization index for the data fusion unit;

[0040] Figure 4 Analysis of the cooperative coupling degree of the backlight system and parameter priority ranking diagram;

[0041] Figure 5 This is a diagram showing the durability testing and lifespan prediction analysis of the backlight system. Detailed Implementation

[0042] 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.

[0043] Please see Figure 1This invention provides an automatic backlight adjustment control system based on a liquid crystal display screen. The system includes: an environment sensing unit responsible for capturing ambient light parameters and display content parameters, which serve as raw data input to the system. Ambient light parameters are collected by a light sensor, and display content parameters are extracted from a display buffer through a graphics processing interface. A scene analysis unit receives the output of the environment sensing unit and preprocesses the ambient light parameters and display content parameters, including data cleaning and format conversion, to extract working environment characteristics. These characteristics include the stability of ambient light and the brightness distribution pattern of the display content. The scene analysis unit accesses a fault database that stores historical fault records, such as backlight module failure events or performance degradation data. By combining the working environment characteristics and historical fault records, the scene analysis unit generates corresponding adjustment parameters, which guide the initial settings for backlight adjustment. A data fusion unit schedules intrinsic property data and structural configuration data of the backlight material. The intrinsic property data includes the optical properties of the material, and the structural configuration data describes the physical layout of the backlight unit. The data fusion unit calculates the luminous efficacy characterization index, which quantifies the optical efficiency of the backlight material. Simultaneously, it constructs a light propagation path model, simulating the propagation behavior of light within the backlight unit. The data fusion unit evaluates the synergistic relationship between the luminous efficacy characterization index and the light propagation path model, outputting a parameter priority sequence indicating the priority of different adjustment parameters. The prototype construction unit queries a material library for core and alternative backlight materials, containing attribute information for various backlight materials. Based on the retrieved materials, the prototype construction unit simulates and builds a backlight system prototype, collecting brightness uniformity data and durability test data using simulation tools. Brightness uniformity data reflects the spatial distribution consistency of the backlight output, while durability test data relates to the performance changes of the prototype under long-term operation. The prototype construction unit analyzes the signal response characteristics in the brightness uniformity data, which describe the dynamic response of the backlight to input signals. The configuration management unit determines the installation area size and operating environment constraints of the backlight unit within the LCD screen. The installation area size is obtained by measuring the physical structure of the display screen, and the operating environment constraints include external conditions such as temperature and humidity. The configuration management unit collects the prototype's compatibility parameters to assess the compatibility between the prototype and the display screen. The performance evaluation unit calculates a lifespan prediction index based on durability test data. This index estimates the lifespan of the backlight system and determines the performance stability level. The performance evaluation unit also calculates the system fit degree, considering installation area dimensions, environmental constraints, and compatibility parameters. This system fit degree measures the overall system matching. The control synthesis unit integrates signal response characteristics, performance stability level, and system fit degree to select the optimal backlight material from core and alternative materials using a multi-objective optimization algorithm. Finally, the control synthesis unit generates backlight control commands to adjust backlight brightness in real time, implemented through the driver circuitry.

[0044] Example 1: See Figure 2 The ambient light sensor employs a photodiode array structure, composed of multiple photodiode units, each of which responds to ambient light and generates an electrical signal. The calibration process involves adjusting the gain and offset parameters of the ambient light sensor. The gain parameter amplifies the signal amplitude, while the offset parameter corrects baseline errors, ensuring measurement accuracy meets system requirements. Simultaneously, a brightness distribution histogram of the displayed content is extracted via a graphics processing interface connected to the display driver's frame buffer. The frame buffer stores pixel data for the current display frame, and the graphics processing interface analyzes pixel brightness values ​​and statistically analyzes the brightness distribution to generate a brightness distribution histogram. Ambient light intensity is filtered to eliminate noise interference using a low-pass digital filter algorithm. The cutoff frequency of the low-pass digital filter is set according to the characteristics of ambient light fluctuations to remove high-frequency noise components. The brightness distribution histogram is then normalized, mapping histogram values ​​to a range of zero to one, eliminating scale differences between different displayed contents. Based on the normalized data, a dynamic adaptation coefficient between ambient light and displayed content is calculated. This coefficient is obtained using a weighted average algorithm, which assigns weights to both ambient light intensity and display brightness to reflect the matching requirements between the two. Based on the dynamic adaptation coefficient, the parameter sampling frequency and precision threshold are adjusted. The sampling frequency is dynamically adjusted according to environmental changes; when the dynamic adaptation coefficient changes significantly, the sampling frequency is increased. The precision threshold sets the error tolerance range for data acquisition, ensuring data reliability.

[0045] In some embodiments, the calibration of the ambient light sensor also includes a temperature compensation step. Temperature compensation involves monitoring the ambient temperature using a built-in temperature sensor and adjusting gain and offset parameters to counteract temperature drift. The brightness distribution histogram extraction of the graphics processing interface supports multiple color spaces, such as RGB or YUV formats, ensuring compatibility with different display standards. The low-pass digital filter is designed based on the Butterworth filter model, providing smooth frequency response characteristics. Normalization processing employs a min-max scaling method to linearly transform the original histogram values ​​to a standard range. The calculation of dynamic adaptation coefficients incorporates an adaptive weight adjustment mechanism, with weight values ​​dynamically updated based on ambient light stability. The parameter sampling frequency adjustment strategy includes fixed-step variation or proportional control, and the accuracy threshold is set through historical data statistical analysis.

[0046] The scene analysis unit preprocesses ambient light parameters and display content parameters, including data alignment and missing value imputation. Data alignment synchronizes the timestamps of ambient light and display content parameters, while missing value imputation uses linear interpolation to fill data gaps. It extracts working environment features, including indicators such as ambient light intensity gradient, average brightness of display content, and brightness variance. The intensity gradient calculates the spatial or temporal rate of change of ambient light intensity, the average brightness is obtained through arithmetic mean, and the brightness variance measures the dispersion of brightness distribution. Feature analysis is performed on these working environment features to obtain environmental feature factors. Principal component analysis (PCA) is used to reduce the dimensionality of multi-dimensional working environment features and extract the principal components as environmental feature factors. Historical fault records are categorized based on clustering algorithms, such as K-means, which group historical fault records by similarity to obtain a fault type set. The correlation between environmental feature factors and the fault type set is analyzed using Pearson correlation coefficient, which quantifies the linear relationship between environmental feature factors and fault types, generating an association mapping table. The association mapping table stores the correlation strength between environmental features and fault types in matrix form, with rows corresponding to environmental feature factors and columns corresponding to fault types. Based on the association mapping table, key environmental features and high-risk fault types are identified. Key environmental features are determined through threshold screening, with thresholds set according to correlation strength. High-risk fault types are determined based on their occurrence frequency, which is derived from historical fault records. Combining the key environmental features and high-risk fault types, design constraints for backlight adjustment are determined, including brightness adjustment range, response time constraints, and power consumption limits. Based on these design constraints, the numerical ranges of corresponding adjustment parameters are defined. These parameters include backlight brightness, color temperature, and contrast. The numerical ranges are set through boundary value analysis, which considers the upper and lower limits of the design constraints.

[0047] It is understandable that the data acquisition process of the environmental perception unit relies on the collaboration of hardware sensors and software interfaces to ensure real-time performance and accuracy. The preprocessing and feature extraction steps of the scene analysis unit lay the foundation for subsequent adjustments, but over-computation must be avoided to maintain system efficiency. Fault classification processing depends on the quality and completeness of historical data; the generation of the association mapping table needs to be updated regularly to reflect the latest fault modes. The determination of design constraints must balance performance and reliability, and the definition of numerical ranges should consider the actual adjustable range.

[0048] Optionally, preprocessing steps may include anomaly detection, which uses statistical methods such as Z-score to detect outliers and improve data quality. Feature analysis can be combined with factor analysis to replace principal component analysis, with factor analysis extracting latent variables as environmental feature factors. Fault classification processing supports hierarchical clustering algorithms, which provide multi-granularity grouping results. The storage format of the association mapping table supports database indexing, optimizing query speed. Designing constraints can introduce a safety margin, which expands the numerical range to enhance robustness.

[0049] In some embodiments, the extraction of working environment features further includes time series analysis, which calculates the autocorrelation function of ambient light intensity to capture periodic patterns. Access to historical fault records is achieved through database query languages, such as SQL statements, to filter relevant records. Correlation analysis supports Spearman's rank correlation as an alternative, which handles non-linear relationships. Identification of key environmental features employs machine learning classifiers, such as support vector machines, to automatically filter features. The determination of high-risk fault types is combined with severity scoring, which is based on the level of fault impact. The numerical range is set using optimization algorithms such as gradient descent to ensure global optimum.

[0050] In practical implementation, the calibration process of the environmental perception unit includes the configuration of the photodiode array, which is arranged in a grid pattern, with each diode unit covering a specific field of view. Calibration is performed using a standard light source, which provides known light intensity. Gain parameters are adjusted to match the output signal to the standard value, and offset parameters are calibrated to zero under no-light conditions. The brightness distribution histogram extraction of the graphics processing interface includes a frame buffer locking mechanism to prevent data contention. When parsing pixel brightness values, grayscale values ​​are converted, and the number of histogram bins is adaptively set according to the display resolution. The low-pass digital filter for filtering is implemented as a moving average filter, with the moving average window size adjusted according to the sampling rate. The normalization mapping function is a linear transformation, and the normalized data is stored in floating-point format. The weighted average weight of the dynamic adaptation coefficients is set according to the ambient light priority, which is determined by user configuration or a default value. The adjustment of the parameter sampling frequency uses PID control principles. PID control compares the dynamic adaptation coefficients with the target value, outputs the frequency adjustment amount, and sets the accuracy threshold through confidence interval calculation.

[0051] Optionally, ambient light sensor calibration can be integrated with an automatic calibration routine, which is triggered periodically using a built-in reference light source. The graphics processing interface supports hardware acceleration, which directly accesses the frame buffer via the GPU, improving extraction speed. Filtering can utilize a Kalman filter, which predicts ambient light trends and improves noise suppression. Normalization includes nonlinear mapping options, such as logarithmic scaling for high dynamic range data. The calculation of dynamic adaptation coefficients incorporates fuzzy logic, which handles uncertain inputs and enhances robustness. The parameter sampling frequency adjustment strategy can be event-driven, with events such as sudden changes in ambient light triggering frequency changes.

[0052] In practical implementation, the preprocessed data alignment of the scene analysis unit uses a timestamp synchronization algorithm to compensate for the latency of sensors and interfaces. Missing value imputation uses spline interpolation to improve smoothness. The intensity gradient of the working environment features is calculated as a difference value, the average brightness is weighted by pixel area, and the brightness variance is estimated using an unbiased method. Principal component analysis for feature analysis performs covariance matrix decomposition, extracting feature vectors as environmental feature factors, and retaining over 90% of the variance after dimensionality reduction. The K-means algorithm for fault classification initializes the cluster centers with random selection, iteratively optimizing until convergence, and the fault type set is labeled as common patterns such as overheating or decay. The Pearson correlation coefficient for correlation analysis calculates the ratio of covariance to standard deviation, and the association mapping table is stored as a hash table structure to support fast lookup. Percentile thresholds are used for threshold screening of key environmental features, and histogram statistics are used to determine the frequency of high-risk fault types. The brightness adjustment range of the design constraints is set to the minimum and maximum values ​​of the adjustable backlight, and the response time constraint is based on the limits of human visual perception. Boundary value analysis tests are conducted within the numerical range to address extreme cases and ensure that parameters such as backlight brightness are adjusted within a safe range.

[0053] Example 2: See Figure 3The intrinsic property data of the backlight materials are obtained from a materials database, which stores the original parameters of various backlight materials in tabular form. The standardization conversion process uses a min-max normalization method, which linearly maps the original data to a range of zero to one. Specifically, this involves subtracting the minimum value in the dataset from each attribute value and then dividing by the range. This results in standard material data, stored as a floating-point array to ensure dimensional consistency. Optical property sets are extracted from the standard material data, containing key parameters such as transmittance and refractive index. The extraction process uses a database query language to filter corresponding fields. Key attributes are then selected from the optical property set based on predefined sensitivity analysis results. Sensitivity analysis prioritizes attributes by calculating their contribution to backlight efficiency. Finally, performance indicators for the key attributes are calculated. These indicators are calculated as the ratio of the actual measured value to the ideal reference value; for example, transmittance is expressed as the measured transmittance divided by the theoretical maximum transmittance. Based on performance indicators, a data sequence of luminous efficacy characterization indicators is generated. The data sequence is generated by arranging each performance indicator in the order of material number to form a multi-dimensional vector sequence, which facilitates subsequent indexing and comparative analysis.

[0054] The steps for constructing a light propagation path model using the data fusion unit include: unifying the format of structural configuration data, which contains information on the geometric dimensions, stacking order, and component spacing of the backlight units. This data may originate from different computer-aided design file formats. Format unification converts the heterogeneous data into standard JSON format. Standard JSON uses key-value pairs to describe the topological relationships of the backlight units, resulting in target structural data. The structural dimension parameter set of the backlight units is then parsed from the target structural data. This parsing process uses a parser to read the JSON object and extract key fields such as light guide plate thickness, reflective film angle, and light source spacing, forming the structural dimension parameter set. The structural dimension parameter set is then associated with and matched against material optical properties. This association is achieved by establishing external key associations, i.e., linking the material identifier in the structural dimension parameter set with records in the material optical property database using the material number, resulting in a property-associated parameter set. Based on this property-associated parameter set, a numerical simulation model of light propagation is established. This model employs the Monte Carlo ray tracing algorithm, which simulates the interaction between photons and the backlight unit structure by randomly sampling ray paths. A numerical simulation model of light propagation is performed on a distributed computing cluster. The number of light rays is set to the order of millions to ensure statistical significance, yielding dynamic distribution data of light propagation. This data records the temporal evolution of light intensity at spatial grid points. Path abstraction is then applied to this dynamic distribution data using the shortest path algorithm from graph theory. The photon trajectory is simplified into a network of nodes and edges, where nodes represent key optical interfaces and edges represent propagation paths. This generates the topology of the light propagation path model, which is stored as an adjacency matrix.

[0055] In some embodiments, the standardization transformation process also supports Z-score standardization as an alternative, which scales based on the mean and standard deviation of the dataset. The extraction of optical property sets can be filtered, for example, selecting only property data with wavelengths in the visible light range. Principal component analysis (PCA) can be used instead of sensitivity analysis for key property selection, determining property importance through variance explained rate. The calculation of performance indicators can incorporate logarithmic transformations to handle nonlinear relationships, such as converting transmittance to optical density values. Data sequence generation supports multiple encoding methods, such as one-hot encoding for categorical attributes. Format unification is compatible with XML format conversion, using a nested tag structure to represent hierarchical relationships. The parsing of structural dimension parameter sets includes unit consistency checks, automatically converting imperial units to SI units. The association matching process implements dynamic linking, automatically refreshing association results when the material database is updated. The optical propagation numerical simulation model can integrate a finite element analysis (FEM) module, which handles optical waveguide effects under complex boundary conditions. Simulation calculations support parametric scans, which can batch-execute simulation tasks under different incident angle conditions. Path abstraction provides a variety of simplification algorithm options, such as Kruskal's algorithm, which constructs a minimum spanning tree to simplify the topology.

[0056] Optionally, the standardization transformation of intrinsic attribute data can incorporate robust outlier handling, which uses median and quartile range scaling to reduce the impact of extreme values. The extraction of optical attribute sets can be integrated with real-time measurement data, which is updated online via an embedded spectrometer. Key attribute selection can incorporate expert knowledge rules, with priorities hard-coded in the form of decision trees. Performance index calculation formulas can incorporate weighting factors, which are dynamically adjusted according to the application scenario. Data sequence storage utilizes a time-series database, supporting efficient range queries and trend analysis. Unified format processing of structural configuration data can utilize a data cleaning pipeline, which automatically repairs missing values ​​and corrects format errors. The parsing of structural dimension parameter sets supports direct import of 3D model files; dimensions are extracted from 3D model files such as STL format using a mesh parsing algorithm. The association matching process can implement fuzzy matching logic, which handles textual differences in material names. The Monte Carlo algorithm for the light propagation numerical simulation model can be configured with variance reduction techniques, such as importance sampling, to improve convergence speed. The simulation results visualization module generates a light intensity distribution cloud map, which helps to intuitively verify the model's correctness. The graph theory algorithm for path abstraction can output various topological metrics, such as node degree and centrality, to quantify path importance.

[0057] In practical implementation, the minimum-maximum normalization calculation requires traversing the entire attribute dataset to find the minimum and maximum values. For large-scale datasets, a block traversal strategy is used to avoid memory overflow. The extraction of transmittance and refractive index requires associating with wavelength-dependent curves, which are stored as discrete sampling points. Attribute values ​​at specific wavelengths are calculated through interpolation. Sensitivity analysis employs the Sobol exponential method, which calculates the variance contribution ratio of each attribute output through Monte Carlo simulation. The calculation of performance index ratios involves reference value lookup, obtaining theoretically optimal values ​​from the International Commission on Illumination (ICI) standards database. The vector dimension of the data sequence is fixed to the number of key attributes; if the vector dimension is insufficient, default values ​​are used; and overflow occurs, the data is truncated. JSON format conversion ensures the integrity of nested structures and preserves the hierarchical relationships of the original data; for example, the microstructure array of the light guide plate stores coordinate points in array form. The parser uses a recursive descent parsing algorithm to process the nesting level of JSON objects and verify syntactic correctness. The parsing of light source spacing considers the array topology; array topologies such as rectangular grids or circular arrangements correspond to different spacing calculation rules. External key associations for correlation matching need to handle one-to-many relationships. For example, a structure may correspond to multiple optional materials, thus generating multiple association records. The Monte Carlo ray tracing algorithm uses a cosine weighted distribution for photon emission, simulating the characteristics of a Lambertian light source. The state of each photon is described by its position, orientation, and energy value. The distributed computing cluster uses a master-slave architecture for task scheduling. The master node allocates ray packets to computing nodes, and slave nodes return local statistical results. Dynamic distribution data of light propagation is stored in HDF5 format, which supports efficient reading and writing of large arrays and metadata appending. The shortest path algorithm is implemented using Dijkstra's algorithm. Dijkstra's algorithm calculates the minimum energy loss path from the light source position to each detector point, and node importance is calculated by weighting based on light flux.

[0058] In some embodiments, the mapping range of the standardized transformation can be configured to other intervals, such as negative one to positive one, to accommodate attribute data with both positive and negative values. The update mechanism for the optical attribute set supports triggers, automatically notifying the extraction module when the material database record is modified. The threshold parameters for key attribute screening can be interactively adjusted via a graphical interface, allowing real-time viewing of screening results changes. The calculation of performance indicators can cache intermediate results, avoiding redundant calculations and improving efficiency. Data sequence generation supports compressed storage, which uses differential encoding to reduce storage space. Unified format processing can record data lineage information, tracing the original data source and transformation history. The parsing of the structural dimension parameter set includes geometric constraint verification, checking whether the dimension parameters meet physically realizable conditions. The implementation of association matching provides a manual overriding interface, allowing engineers to force specific association relationships. Boundary condition settings for the light propagation numerical simulation model support various scattering models, such as the Henyey-Greenstein phase function describing anisotropic scattering. The parallel computing framework for simulation operations uses the MPI communication protocol, which coordinates data exchange between multiple nodes. The topology of the path abstraction process can be exported as a standard graph format, such as GraphML, which is convenient for third-party tools to analyze.

[0059] Example 3: Propagation feature vectors are derived from the topology of the light propagation path model. The topology stores node connections in the form of an adjacency matrix. The extraction process of propagation feature vectors traverses all nodes in the topology. Node attributes include graph theory indices such as path length, node degree, and clustering coefficient, forming high-dimensional feature vectors. Dimensionality reduction is performed on the propagation feature vectors using principal component analysis (PCA). PCA calculates the covariance matrix of the feature vectors and solves for eigenvalues. The eigenvector corresponding to the largest eigenvalue is selected as the principal component, and the original high-dimensional vectors are projected into a low-dimensional space to obtain a simplified feature set. The feature similarity index within the simplified feature set is calculated using the cosine similarity algorithm. The cosine similarity algorithm measures the closeness of two feature vectors in a direction, with values ​​ranging from -1 to +1. The index similarity index between luminous efficacy characterization indices is calculated using the Euclidean distance method. The Euclidean distance method calculates the straight-line distance between two index vectors in space; a smaller distance value indicates a higher similarity. The correlation factor between the light propagation path model and the luminous efficacy performance indicators is calculated. This correlation factor is derived through multiple linear regression analysis, which establishes a linear relationship between the topological parameters of the light propagation path model and the luminous efficacy performance indicators. The regression coefficients reflect the correlation strength. Combining the correlation factor, feature similarity index, and indicator similarity index, a weighted fusion algorithm is used to calculate the cooperative coupling degree. The algorithm assigns weight coefficients to each index, which are set based on expert experience. The weighted sum yields the cooperative coupling degree value, which quantifies the overall cooperative performance of the system. Based on the cooperative coupling degree, a priority sequence of parameters is output. This priority sequence is implemented using a priority queue data structure, with parameters corresponding to high cooperative coupling degrees placed at the front of the queue and parameters with low cooperative coupling degrees placed relatively later.

[0060] The steps for analyzing signal response characteristics in the prototype building unit include outlier removal from brightness uniformity data, which is derived from multi-point brightness measurements of the backlight system prototype. Outlier removal employs a box plot method, calculating the quartiles and interquartile ranges of the data, identifying and removing outliers falling outside the upper and lower bounds, resulting in cleaned data. Time-domain and frequency-domain features are extracted from the cleaned data. Time-domain features include brightness volatility, derived by calculating the standard deviation of brightness values; frequency-domain features include spectral distribution, derived by converting the time-domain signal to the frequency domain using a Fast Fourier Transform. Based on these time-domain and frequency-domain features, a signal characteristic descriptor is constructed. This descriptor is a multi-dimensional vector, with each dimension corresponding to the number of features, comprehensively describing the signal behavior characteristics. Using the signal characteristic descriptor, the signal response delay and stability of the backlight system prototype are analyzed. Signal response delay is calculated by comparing the difference between the input signal timestamp and the output signal's steady-state timestamp. Stability is evaluated by calculating the variance of the signal descriptor; a smaller variance indicates higher stability.

[0061] In some embodiments, the extraction of propagation feature vectors can include a path weight attribute, with the path weight determined by the proportion of light flux. The dimensionality reduction dimension of principal component analysis (PCA) can be dynamically determined through a cumulative contribution rate threshold, for example, retaining principal components with a cumulative contribution rate exceeding 95%. Cosine similarity calculation can incorporate a length normalization step to eliminate the influence of vector magnitude on similarity results. The Euclidean distance method can be extended to weighted Euclidean distance, which assigns importance weights to different dimensions. Multiple linear regression analysis can use stepwise regression to screen significant variables, automatically adding or deleting predictor variables to optimize the model. The weight coefficients of the weighted fusion algorithm can be finely set using the analytic hierarchy process (AHP), which constructs a judgment matrix to calculate weights. The priority queue sorting algorithm can be implemented using heap sort, which guarantees that the time complexity of insertion and retrieval operations is logarithmic. Outlier removal can be combined with the isolated forest algorithm, which identifies outliers by constructing random trees. The calculation of brightness volatility can distinguish between short-term fluctuations and long-term drift, calculating the standard deviation at different time scales. The Hanning window can be used as the window function in the Fast Fourier Transform (FFT) to reduce spectral leakage. The construction of signal characteristic descriptors can incorporate nonlinear features, such as approximate entropy or fractal dimension. Analysis of signal response delay can distinguish between rise and fall times, measuring the response speeds of brightness enhancement and decay separately. Stability assessment can incorporate time series stationarity tests, such as the ADF test, to verify stability.

[0062] It is understandable that the accuracy of collaborative relationship assessment depends on the completeness of propagation feature vector extraction; the omission of any key topological features will affect subsequent analysis. Dimensionality reduction, while reducing computational complexity, may lead to information loss, requiring a balance between efficiency and accuracy. The selection of similarity indices must consider data distribution characteristics; for example, Euclidean distance is sensitive to outliers, while cosine similarity focuses on direction rather than amplitude. The calculation of correlation factors assumes a linear relationship; when the system exhibits strong nonlinearity, more complex modeling methods may be required. The quality of signal response feature analysis is directly affected by the accuracy of measurement data; sensor calibration and sampling rate settings are crucial. The complementary performance of time-domain and frequency-domain features comprehensively characterizes signal properties, but excessively high feature dimensionality may lead to the curse of dimensionality.

[0063] Optionally, propagation feature vectors can incorporate dynamic propagation features, which record the changing patterns of the light path over time. Principal component analysis (PCA) can be combined with kernel tricks to handle nonlinear structures; kernel PCA maps data to a high-dimensional space for dimensionality reduction. Similarity calculation can integrate multiple measurement methods, using a voting mechanism to comprehensively judge similarity. Multiple linear regression analysis can add interaction terms to capture variable interaction effects; these interaction terms are the product terms of independent variables. Weighted fusion algorithms can design adaptive weight adjustment mechanisms, dynamically optimizing weights based on the input data distribution. Outlier removal can employ a multi-algorithm consensus strategy, requiring at least two methods to simultaneously identify an outlier before removal. Frequency domain feature extraction can incorporate wavelet transform analysis, providing time-frequency localization information. Signal characteristic descriptors can implement feature selection to reduce dimensionality; feature selection, such as recursive feature elimination, preserves important features. Stability assessment can be combined with control chart methods, where upper and lower control limits are set to monitor signal fluctuations.

[0064] In some embodiments, the node degree calculation for propagating feature vectors can distinguish between in-degree and out-degree. In a directed graph, the in-degree of a node represents the number of incoming paths, and the out-degree represents the number of outgoing paths. The eigenvalue solution for principal component analysis can use the power iteration method, which efficiently calculates the eigenvector corresponding to the largest eigenvalue. Cosine similarity calculation can be extended to the sparse vector case, where inverted indexing is used to accelerate calculation. The calculation of Euclidean distance can be optimized to squared Euclidean distance, which avoids square root operations and improves efficiency. Residual analysis of multiple linear regression can detect model hypothesis violations, checking whether the residuals are independent and identically distributed. The weight coefficients of weighted fusion can be solved using the particle swarm optimization algorithm, which simulates a flock of birds foraging to find the optimal weight combination. The implementation of a priority queue can support delayed deletion operations; delayed deletion marks the deleted element, and the actual deletion occurs during heap adjustment. The upper and lower bounds calculation for the box plot method can be adjusted by a factor of 1.5 times the interquartile range, with points outside this range considered outliers. Zero-padding in the Fast Fourier Transform (FFT) increases frequency domain resolution; zero-padding adds zeros to the end of the data to increase the number of FFT points. Steady-state determination of signal response delay can be based on a rate-of-change threshold; steady state is considered achieved when the rate of change in brightness continuously falls below the threshold. The sliding window size for stability assessment can be adaptively adjusted, dynamically changing according to the intensity of signal fluctuations.

[0065] In practical implementation, the following mathematical model is used to calculate the degree of synergistic coupling:

[0066]

[0067] in: Represents the degree of cooperative coupling. Represents the correlation factor. Representative feature similarity index, Representative indicator similarity index. , , These represent the weights of the correlation factor, feature similarity index, and indicator similarity index, respectively, and satisfy the following conditions: + + The normalization condition is 1.

[0068] In some embodiments, the path length attribute of the propagation feature vector can be distinguished into geometric path length and optical path length, with the optical path length considering refractive index correction. The covariance matrix calculation for principal component analysis can employ a shrinking estimator, which improves estimation stability under small sample conditions. Cosine similarity calculation can introduce a smoothing factor to prevent division by zero errors; the smoothing factor adds a small constant when the denominator is close to zero. Euclidean distance calculation can be optimized into matrix operation form, with matrix operations utilizing linear algebra libraries to accelerate batch distance calculations. Multiple linear regression analysis can incorporate regularization terms to prevent overfitting; regularization terms such as L2 regularization constrain the magnitude of the constraint coefficients. Weighted fusion algorithms can design nonlinear combination rules; nonlinear combinations such as multiplicative fusion can replace linear weighting. The storage structure of the priority queue can utilize a Fibonacci heap, which optimizes and reduces the cost of decrement operations. The boxplot method for outlier removal can be adapted to skewed distribution data; skewed distributions are calculated using an adjusted boxplot based on the median and MAD. Spectral leakage suppression using the Fast Fourier Transform (FFT) can be achieved using the Kaiser window, which controls the sidelobe level by adjusting the β parameter. Dimensionality compression of signal characteristic descriptors can be achieved using autoencoder techniques, which learn compact feature representations. Analysis of signal response delay can distinguish the delay characteristics at different brightness levels by measuring the response time under low, medium, and high brightness conditions.

[0069] See Figure 4 This paper presents the comprehensive analysis results of the backlight system's cooperative coupling degree. The figures use multiple bar charts to show the performance of different parameters across multiple dimensions, including cooperative coupling degree, feature similarity, index similarity, and correlation factor. Cooperative coupling degree, as a key indicator of the overall cooperative performance of the system, comprehensively reflects the intrinsic correlation strength between the light propagation path model and the luminous efficacy characterization index. Feature similarity is calculated using the cosine similarity algorithm, reflecting the directional proximity of different feature vectors; index similarity uses the Euclidean distance method to quantify the relative positional relationship of index vectors in space; and the correlation factor is derived through multiple linear regression analysis, revealing the linear dependence between system parameters. The parameters in the charts are arranged according to their cooperative coupling degree from high to low priority, providing clear guidance for the optimization and adjustment of backlight system parameters. Parameters with high cooperative coupling degree have greater adjustment value during system operation and should be prioritized for optimization. This multi-dimensional comprehensive analysis allows for a comprehensive evaluation of the backlight system's cooperative performance, providing data support for refined system control and performance improvement. The data shown in the figure is rich in content, including specific values ​​of multiple parameters on different evaluation dimensions. These data together constitute a complete picture of the system's collaborative performance.

[0070] Example 4: Durability test data is derived from time-series data collected during accelerated aging tests of the backlight system prototype. The test parameter set includes multiple parameters such as temperature cycle count, constant current drive current value, brightness maintenance rate measurement, and voltage fluctuation range. Common failure modes and mechanisms of the backlight unit are queried. Common failure modes include brightness decay exceeding the threshold, color coordinate drift, and dark spots. Failure mechanisms involve physicochemical processes such as phosphor thermal quenching, light guide plate yellowing, and excessively high LED chip junction temperature. Variable decomposition is performed on the failure modes and mechanisms, breaking down each failure process into independent influencing factors. For example, brightness decay is decomposed into temperature stress factor, current stress factor, and inherent material decay rate, resulting in lifetime-influencing variables. Based on these lifetime-influencing variables, a lifetime assessment standard is set, referencing relevant standards from the International Commission on Illumination (ICI), defining the effective lifetime endpoint as the point when the brightness maintenance rate drops to 70%. A subset of parameters related to the lifetime assessment standard is selected from the test parameter set. This subset is filtered through correlation analysis to identify parameters significantly correlated with the rate of brightness maintenance rate decline, such as the proportion of high-temperature operating time and average drive current density. Based on durability test data, a lifespan score is calculated for each parameter in the parameter subset. The lifespan score is determined by mapping the measured parameter values ​​to a scoring scale from 0 to 100, with the endpoints of the scale corresponding to the upper and lower limits of the parameter's safe operating condition. Weighting coefficients are assigned to the parameter subset, determined using the analytic hierarchy process (AHP). The AHP constructs a judgment matrix to compare the relative importance of each parameter's impact on lifespan. Combining the lifespan score and weighting coefficients, a lifespan prediction index is calculated through a linear combination. This linear combination multiplies the lifespan score of each parameter by its weight and sums the results to obtain a lifespan prediction index value ranging from 0 to 100; a higher value indicates a longer predicted lifespan.

[0071] Referring to Table 1, the steps for calculating the system adaptability of the performance evaluation unit include: First, measuring the physical dimensional parameters of the backlight system prototype based on the installation area dimensions. The installation area dimensions are obtained from the structural drawings of the LCD screen, including the length, width, and depth tolerances of the backlight cavity. The physical dimensional parameters include the length, width, and height of the backlight system prototype, which are obtained by scanning and measuring the prototype using a 3D coordinate measuring machine. Second, calculating the dimensional matching degree between the installation area dimensions and the physical dimensional parameters. This matching degree is calculated by comparing the tolerance range of the installation area dimensions with the measured values ​​of the physical dimensional parameters, calculating the degree of conformity in each dimension, and taking the geometric mean. Third, analyzing environmental factors in the adaptability parameters. These adaptability parameters are derived from test records of the prototype in a simulated environment chamber. Environmental factors include the operating temperature range, operating humidity range, and vibration tolerance level. Fourth, combining the working environment constraints and the adaptability parameters, calculating the environmental factor adaptability. The working environment constraints define the temperature and humidity limits of the display deployment location. The environmental factor adaptability is obtained by comparing the environmental tolerance in the adaptability parameters with the degree of matching of the working environment constraints. The system fit degree is obtained by integrating size matching degree and environmental factor fit degree through geometric mean algorithm. The geometric mean algorithm multiplies the size matching degree and environmental factor fit degree and takes the square root to obtain the system fit degree value in the range of 0 to 1. The closer the value is to 1, the higher the system fit degree.

[0072] Table 1: Correspondence between lifespan-influencing variables and parameter weights

[0073]

[0074] In some embodiments, the acquisition of durability test data can be enhanced with online monitoring, which records the prototype's operating status in real time via embedded sensors. Failure mode lookup can connect to an industry failure database, which includes typical failure cases of different backlight technologies. The variable decomposition process can employ fault tree analysis, which decomposes the data layer by layer from the top event as the root node to the bottom event. The setting of lifespan assessment criteria can incorporate acceleration factor conversion, which uses the Arrhenius equation to equate accelerated testing time to actual usage time. The selection of parameter subsets can utilize machine learning feature selection methods, such as LASSO regression, to automatically filter important parameters. The mapping function for lifespan scoring can be designed as a piecewise linear function, with different scoring slopes set for different parameter intervals. The determination of weighting coefficients can be combined with the Delphi method, which converges weighting opinions through multiple rounds of expert consultation. Linear combination calculations can be extended to nonlinear fusion models, such as neural networks, to capture the interaction effects between parameters. The measurement of physical dimensional parameters can be performed using a laser tracker, which provides micron-level measurement accuracy. The calculation of dimensional fit can incorporate tolerance zone analysis, which examines the cumulative error of the dimensional chain. The calculation of environmental factor fit can distinguish between steady-state and transient conditions; steady-state conditions assess continuous working capability, while transient conditions assess shock resistance. The geometric mean algorithm can be weighted into a more general power mean form, where the power mean adjusts the contribution of each factor through an exponential parameter.

[0075] It is understandable that the accuracy of lifespan prediction metrics heavily relies on the completeness of durability test data and the representativeness of test conditions; any missing or biased test parameters will affect the prediction results. Calculating system fit requires precise dimensional measurements and environmental parameter calibration; measurement errors are directly transmitted to the fit value. The setting of weighting coefficients is somewhat subjective, and different application scenarios may require adjustments to the weighting allocation. The geometric mean algorithm assumes that dimensional fit and environmental factor fit are equally important; in specific applications, the weighting strategy may need to be adjusted.

[0076] Optionally, failure mode queries can establish a failure knowledge graph, which represents the causal relationships between failure modes in a graph structure. Variable decomposition can employ sensitivity analysis tools, which quantify the impact of each variable on lifespan through Monte Carlo simulation. Lifespan assessment criteria can define composite criteria that integrate multiple indicators, simultaneously considering degradation in multiple dimensions such as brightness, color temperature, and uniformity. The selection of parameter subsets can incorporate time-series features, such as autocorrelation coefficients, to capture the dynamic changes in parameters. The lifespan scoring scale can dynamically adjust endpoint values, updating the safe operating boundary based on advancements in materials technology. The determination of weighting coefficients can incorporate data-driven methods, using historical data regression analysis to inversely deduce weights. The calculation of dimensional fit can consider thermal expansion coefficient correction, compensating for dimensional changes caused by temperature variations. The assessment of environmental factor suitability can incorporate chemical environmental factors, such as hydrogen sulfide concentration, to assess corrosion resistance. The calculation of system suitability can integrate stress simulation data, which uses finite element analysis to evaluate the mechanical structure's compatibility.

[0077] In practice, durability test data is recorded with minute-level sampling intervals, and each parameter's timestamp is aligned and stored in a time-series database. Failure mode queries retrieve matching records from a relational database using SQL queries, with query conditions including keywords such as backlight type and service life. Variable decomposition employs orthogonal experimental design, arranging multi-factor, multi-level test schemes to separate the effects of each variable. The brightness maintenance rate threshold for lifespan assessment standards is adjusted according to the application scenario; 70% is required for civilian display products, while 80% may be required for industrial products. For parameter subset selection, the Pearson correlation coefficient between each parameter and the brightness maintenance rate is calculated, retaining parameters with an absolute correlation coefficient greater than 0.3. Lifespan scores are calculated using linear interpolation, mapping measured parameter values ​​to a scoring scale; for example, a measured temperature stress factor of 50 hours corresponds to a lifespan score of 60. The weighting coefficients are constructed using the analytic hierarchy process (AHP) to create pairwise comparison matrices, where matrix elements represent the relative importance of parameters, and the weight vectors are solved using the eigenvector method. Before linear combination calculations, the lifespan scores are normalized to ensure consistent dimensions for each parameter's score. The tolerance range for the installation area dimensions is defined in millimeters, with upper and lower deviation values ​​extracted from the display screen design specifications. Measurement points for physical dimensional parameters are evenly distributed on the prototype surface, with nine measurements taken in each dimension and the average value calculated to reduce random errors. The degree of conformity in dimensional matching is calculated using a tolerance method; a score of 1 is awarded for measured values ​​falling within the tolerance zone, and points are deducted proportionally for deviations. The degree of conformity in environmental factor adaptation is calculated using a multi-level scoring system; full marks are awarded for working environment constraints within the adaptability parameter range, and 20 points are deducted for every 10% exceeding this range. The square root operation of the geometric mean algorithm is implemented using Newton's iteration method, which rapidly approximates the square root value.

[0078] Optionally, durability test data can be supplemented with failure time records, which record the specific time points of failure occurrence to refine the model. Failure mode queries can utilize a fuzzy matching mechanism to handle non-standardized failure description text. Variable decomposition can employ principal component analysis (PCA) for dimensionality reduction, merging related variables into comprehensive factors to simplify the model. Lifespan assessment criteria can incorporate the concept of economic lifespan, considering maintenance costs rather than just technical performance thresholds. Parameter subset selection can utilize mutual information metrics, capturing the nonlinear relationship between parameters and lifespan. Lifespan scoring can be mapped using nonlinear functions, such as sigmoid functions, which better characterize the critical effects of parameters. Determining weighting coefficients can incorporate game theory methods, balancing the weight preferences of different stakeholders. Dimensional fit calculations can include geometric tolerance assessments, encompassing features like flatness and parallelism. Environmental factor suitability assessments can consider the synergistic effects of multiple environmental factors, such as the combined effects of temperature and humidity accelerating material aging.

[0079] See Figure 5 This chart illustrates the performance changes and lifespan prediction analysis results of the backlight system during durability testing. The chart uses a dual Y-axis format: the primary Y-axis displays the actual measured value of brightness maintenance rate, while the secondary Y-axis displays the lifespan prediction index based on multi-parameter fusion calculations. The brightness maintenance rate curve reflects the degradation trend of the backlight system's luminous performance during long-term operation, while the lifespan prediction index integrates a weighted score of multiple lifespan-influencing variables such as temperature stress, current stress, material degradation rate, and environmental humidity, providing a scientific prediction of the system's remaining lifespan. The chart clearly marks the lifespan termination threshold line when the brightness maintenance rate drops to 70%, which is the effective lifespan end standard defined by relevant standards of the International Commission on Illumination (ICI). Comparative analysis of actual test data and the prediction model accurately identifies the system's critical failure points. The chart clearly delineates the three stages—excellent lifespan, deteriorating lifespan, and lifespan termination—using different colored filled areas, providing an intuitive reference for system maintenance decisions and lifespan assessment. The consistency between the lifespan prediction index curve and the actual brightness maintenance rate trend verifies the accuracy of the prediction model; this multi-parameter fusion lifespan assessment method significantly improves the reliability of the prediction results. The data in the charts show the complete performance trajectory of the system throughout the entire testing cycle, providing important experimental evidence for the reliability design and lifespan optimization of the backlight system.

[0080] Example 5: Signal response characteristics include signal response delay and stability data of the backlight system prototype. Performance stability levels are divided into four grades (A / B / C / D) based on lifetime prediction indicators. System adaptability is a value ranging from 0 to 1. Priority settings are based on predefined rules, which stipulate that in industrial application scenarios, performance stability level takes precedence over system adaptability, and system adaptability takes precedence over signal response characteristics. Screening conditions are set, which are combinations of thresholds, such as requiring a performance stability level no lower than grade B, a system adaptability no lower than 0.85, and a signal response delay no more than 10 milliseconds. Based on the screening conditions, the optimal material is selected from core backlight materials and candidate backlight materials. Core backlight materials include light-guide grade PMMA and optical grade PC, while candidate backlight materials include high-refractive-index glass and silicon-based light-guide materials. The selection process uses a multi-attribute decision method, which calculates a comprehensive score for each material. The material with the highest comprehensive score is determined as the optimal material.

[0081] The system integrates corresponding adjustment parameters and parameter priority sequences. The adjustment parameters, derived from the scene analysis unit, include the backlight brightness adjustment range and color temperature adjustment step value. The parameter priority sequence, derived from the data fusion unit, indicates the priority order of each adjustment parameter. A parameter optimization list is generated and stored in tabular form, with columns including parameter name, optimized value, and priority weight. The system combines the characteristic parameters of the optimal material with the parameter optimization list. The characteristic parameters of the optimal material include light extraction efficiency, thermal resistance coefficient, and refractive index. Backlight control commands are synthesized through a rule engine based on if-then logic rules. The rule condition part matches material characteristics and optimized parameters, while the rule action part generates specific control code. The backlight control commands are used to adjust the backlight brightness in real time. The commands are transmitted to the backlight driver via PWM modulation signals or an I2C digital interface, and the drive current and duty cycle are dynamically adjusted according to the control commands.

[0082] In some embodiments, priority comparison can introduce a dynamic weighting mechanism, which automatically adjusts the importance ratios of signal response characteristics, performance stability levels, and system adaptability based on the application scenario. Filtering conditions can support fuzzy logic judgment, which uses membership functions to handle the transition intervals of boundary conditions. The multi-attribute decision-making method can employ the TOPSIS algorithm, which calculates the relative closeness of each material to the ideal solution as the selection criterion. The generation of the parameter optimization list can include parameter constraints, which define the safe operating range of each parameter. The rule engine can integrate real-time inference capabilities, which dynamically update the control logic during system runtime. The transmission of backlight control commands can support multi-protocol adaptation, compatible with the communication specifications of drivers from different manufacturers.

[0083] In practical implementation, a specific example of priority comparison shows that when the response delay in the signal response characteristics is 8 milliseconds, the performance stability level is B+, and the system adaptability is 0.92, according to predefined rules, the performance stability level is first ensured to meet the requirement of B or above. Then, the system adaptability is compared to see if it exceeds the 0.9 threshold. Finally, the signal response delay is evaluated to see if it is within the 20 millisecond limit. The threshold combination of the screening conditions is set according to the display application scenario. Medical diagnostic displays require a performance stability level of A and a system adaptability exceeding 0.95, while commercial advertising displays can accept B-level stability and 0.8 adaptability. A specific example of optimal material selection shows that when the light extraction efficiency of the core backlight material, light guide grade PMMA, is 88% and the thermal resistance coefficient is 120K / W, and the light extraction efficiency of the candidate material, high-refractive-index glass, is 92% and the thermal resistance coefficient is 95K / W, the multi-attribute decision method calculates the performance scores of each item. High-refractive-index glass is selected as the optimal material due to its higher light extraction efficiency and better thermal performance.

[0084] A specific example of the parameter optimization list includes three rows of records: the first row's parameter name is backlight brightness, the optimized value is 300 nits, and the priority weight is 0.6; the second row's parameter name is color temperature, the optimized value is 6500K, and the priority weight is 0.3; the third row's parameter name is refresh rate, the optimized value is 120Hz, and the priority weight is 0.1. A specific example of the rule engine's operation shows that when the rule condition detects that the optimal material is high-refractive-index glass and the backlight brightness has the highest priority in the parameter optimization list, the rule action generates a PWM duty cycle adjustment instruction, setting the duty cycle to 75% corresponding to a 300-nit brightness output. A specific example of the backlight control instruction transmission shows that under the I2C communication protocol, the control instruction is encoded into a 2-byte data frame. The first byte is written to the driver address 0x34, and the second byte contains the brightness control code 0x7F. After receiving the data, the driver adjusts the output current to 80% of the rated value.

[0085] Optionally, a machine learning classifier can be introduced for priority comparison. This classifier is trained on historical data to obtain the optimal priority ranking model. A tiered threshold system can be set for screening conditions, matching different threshold requirements to different performance levels. A multi-attribute decision-making method can be combined with the Analytic Hierarchy Process (AHP), which calculates the weight coefficients of each material attribute by constructing a judgment matrix. The parameter optimization list can support the definition of parameter dependencies, which describe the linkage constraints between parameters. The rule engine can be expanded with a confidence factor, which evaluates the reliability of rule condition matching. Redundant check codes can be added to backlight control commands, using the CRC-16 algorithm to ensure data transmission integrity.

[0086] In some embodiments, the priority comparison of signal response characteristics, performance stability level, and system adaptability can be implemented as a weighted scoring system. This system assigns a base score to each indicator and adjusts the weighting coefficients according to the application scenario. The threshold combination of screening conditions can be dynamically adjusted, gradually relaxing the threshold requirements based on the usage time of the backlight unit. Cost constraints can be introduced into the optimal material selection process, with material price as one of the selection factors. The generation of the parameter optimization list can consider parameter coupling effects, establishing an influence relationship model between parameters using experimental data. The rule engine's rule base can support online updates, downloading the latest control strategy via a network interface. The generation of backlight control commands can include safety protection rules, which force a switch to safety mode when abnormal parameters are detected.

[0087] Optionally, priority comparison can incorporate multi-objective optimization algorithms, such as NSGA-II, to solve for Pareto-optimal priority schemes. Screening criteria can be applied to different material groups, setting differentiated threshold standards for core and alternative materials. Multi-attribute decision-making methods can be combined with fuzzy comprehensive evaluation, which handles uncertainties and fuzziness in material properties. The parameter optimization list can be exported to a machine-readable format, such as JSON, for easy access by other systems. The execution efficiency of the rule engine can be optimized through rule compilation, which converts rules into binary code to improve execution speed. A retransmission mechanism can be implemented for backlight control command transmission, automatically resending command data packets in case of communication failure.

[0088] It is understandable that the overall performance of the control synthesis unit depends on the accuracy and consistency of the output data from each front-end unit; any anomaly in the input data will lead to deviations in the control commands. Optimal material selection requires a balance between performance, cost, and reliability; optimizing a single metric does not necessarily lead to the optimal overall system performance. The rule design of the rule engine needs to cover various boundary conditions, and the completeness of the rule base directly affects the robustness of the control system. The generation frequency of backlight control commands needs to be synchronized with the refresh rate of the display content; an excessively low command update rate will result in adjustment lag.

[0089] In practical implementation, predefined rules for priority comparison are stored in the form of a decision table, with rows corresponding to different application scenarios and columns corresponding to comparison rules. Threshold combinations for filtering conditions are configured in a configuration file and loaded into memory upon system startup. The TOPSIS algorithm for multi-attribute decision-making includes four steps: data normalization, weighted normalization, ideal solution calculation, and relative proximity calculation. The parameter optimization list table structure includes fields such as parameter ID, parameter value, weight, and upper and lower limits, stored in a relational database table. The PWM modulation of the backlight control command uses a hardware timer to generate a precise square wave signal with a duty cycle resolution of 16 bits. I2C digital communication follows standard protocol specifications, with a clock frequency set to 400kHz, and each data frame includes start bits, address bits, data bits, and stop bits.

[0090] Optionally, the priority comparison decision table supports hot updates, allowing modification of priority rules during system runtime without service interruption. Threshold management for filtering conditions provides a graphical interface, enabling engineers to intuitively adjust threshold parameters. Multi-attribute decision methods can add sensitivity analysis functionality, assessing the impact of weight changes on material selection results. The generation of parameter optimization lists can automate parameter tuning, continuously optimizing parameter values ​​through feedback control loops. The rule engine can integrate rule version management, recording rule modification history and supporting rapid rollback. Backlight control command verification can add a simulation testing step, verifying the rationality and security of the commands before actual transmission.

[0091] In practice, the control synthesis unit's operating cycle is synchronized with the display frame rate, executing a complete control command generation process once per display frame cycle. Signal response characteristics, performance stability levels, and system adaptability data are transmitted to the control synthesis unit via shared memory, with timestamps ensuring timing consistency. The optimal material selection process is executed once during system initialization and is only retried during operation when material performance degradation is detected. The parameter optimization list is dynamically updated based on ambient light changes; ambient light sensor data is sampled every 100 milliseconds, triggering parameter recalculation. The rule engine employs a forward chain reasoning strategy for rule matching, deriving control commands from known facts.

[0092] 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, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A control system for automatic backlight adjustment based on a liquid crystal display screen, characterized in that, The system includes: An environmental sensing unit captures ambient light parameters and display content parameters; The scene analysis unit preprocesses the ambient light parameters and display content parameters, extracts working environment features, and generates corresponding adjustment parameters by combining them with historical fault records in the fault database. The data fusion unit schedules the intrinsic property data and structural configuration data of the backlight material, calculates the luminous efficacy characterization index, and constructs the light propagation path model; it evaluates the synergistic relationship between the luminous efficacy characterization index and the light propagation path model, and outputs the parameter priority sequence. The steps for evaluating the collaborative relationship in the data fusion unit include: extracting propagation feature vectors from the light propagation path model, performing dimensionality reduction on the propagation feature vectors to obtain a simplified feature set; calculating the feature similarity index within the simplified feature set and the index similarity index between luminous efficacy characterization indicators; calculating the correlation factor between the light propagation path model and the luminous efficacy characterization indicators; combining the correlation factor, feature similarity index, and index similarity index, calculating the collaborative coupling degree through a weighted fusion algorithm; and outputting the ranking result of the parameter priority sequence based on the collaborative coupling degree. The prototype construction unit queries the material library for core backlight materials and alternative backlight materials, simulates and builds a backlight system prototype based on the materials, and collects the brightness uniformity data and durability test data of the prototype; it also analyzes the signal response characteristics in the brightness uniformity data. The configuration management unit determines the installation area size and working environment constraints of the backlight unit in the LCD screen, and collects the prototype's adaptability parameters; The performance evaluation unit calculates life prediction indicators based on durability test data and determines the performance stability level based on the life prediction indicators; it also calculates the system adaptability by combining the installation area size, working environment constraints, and adaptability parameters. The control synthesis unit integrates signal response characteristics, performance stability level, and system adaptability to select the best material from the core backlight material and alternative backlight materials, and generates backlight control commands.

2. The automatic backlight adjustment control system based on a liquid crystal display screen as described in claim 1, characterized in that, The specific steps for the environmental sensing unit to capture ambient light parameters and display content parameters include: The ambient light sensor is calibrated to obtain real-time ambient light intensity, and the brightness distribution histogram of the displayed content is extracted through the graphics processing interface. The ambient light intensity is filtered to eliminate noise interference, and the brightness distribution histogram is normalized. Based on the normalized data, the dynamic adaptation coefficient between the ambient light and the displayed content is calculated. According to the dynamic adaptation coefficient, the parameter sampling frequency and accuracy threshold are adjusted.

3. The automatic backlight adjustment control system based on a liquid crystal display screen as described in claim 1, characterized in that, The steps for the scene parsing unit to generate the corresponding adjustment parameters include: The working environment characteristics are analyzed to obtain environmental characteristic factors; historical fault records are classified to obtain a fault type set; the correlation between environmental characteristic factors and fault type set is analyzed to generate an association mapping table; based on the association mapping table, key environmental characteristics and high-risk fault types are identified; combined with key environmental characteristics and high-risk fault types, the design constraints for backlight adjustment are determined; and according to the design constraints, the numerical range of the corresponding adjustment parameters is defined.

4. The automatic backlight adjustment control system based on a liquid crystal display screen as described in claim 1, characterized in that, The steps for the data fusion unit to calculate the luminous efficacy characterization index include: The intrinsic property data of the backlight material is standardized to obtain standard material data; the optical property set of the material, including transmittance and refractive index, is extracted from the standard material data; key properties in the optical property set are screened and the performance indicators corresponding to the key properties are calculated; based on the performance indicators, a data sequence of light efficiency characterization indicators is generated.

5. The automatic backlight adjustment control system based on a liquid crystal display screen as described in claim 1, characterized in that, The steps for the data fusion unit to construct the optical propagation path model include: The structural configuration data is processed to unify the format to obtain the target structural data; the structural dimension parameter set of the backlight unit is parsed from the target structural data; the structural dimension parameter set is correlated and matched with the optical properties of the material to obtain the attribute correlation parameter set; a light propagation numerical simulation model is established based on the attribute correlation parameter set; the light propagation numerical simulation model is simulated to obtain the dynamic distribution data of light propagation; the dynamic distribution data of light propagation is processed to abstract the path to generate the topology of the light propagation path model.

6. The automatic backlight adjustment control system based on a liquid crystal display screen as described in claim 1, characterized in that, The steps for analyzing signal response characteristics in the prototype building unit include: Outlier removal is performed on the brightness uniformity data to obtain cleaned data. Time-domain and frequency-domain features, including brightness fluctuation rate and spectral distribution, are extracted from the cleaned data. Based on the time-domain and frequency-domain features, a signal characteristic descriptor is constructed. The signal characteristic descriptor is used to analyze the signal response delay and stability of the backlight system prototype.

7. The automatic backlight adjustment control system based on a liquid crystal display screen as described in claim 1, characterized in that, The steps for calculating lifetime prediction metrics by the performance evaluation unit include: The test parameter set in the durability test data is analyzed to query the common failure modes and failure mechanisms of the backlight unit; the failure modes and failure mechanisms are decomposed into variables to obtain life-influencing variables; based on the life-influencing variables, life assessment criteria are set; a subset of parameters related to the life assessment criteria is selected from the test parameter set; based on the durability test data, the life score of each parameter in the parameter subset is calculated; weight coefficients are assigned to the parameter subset, and the life prediction index is calculated by linear combination of the life score and weight coefficients.

8. The automatic backlight adjustment control system based on a liquid crystal display screen as described in claim 1, characterized in that, The steps for the performance evaluation unit to calculate the system adaptability include: Based on the installation area dimensions, the physical dimensional parameters of the backlight system prototype are measured, including length, width, and height; the dimensional matching degree between the installation area dimensions and the physical dimensional parameters is calculated; environmental factors in the adaptability parameters are analyzed, including temperature range and humidity range; environmental factor adaptability is calculated by combining working environment constraints and adaptability parameters; and the dimensional matching degree and environmental factor adaptability are integrated to obtain the system adaptability through a geometric mean algorithm.

9. The automatic backlight adjustment control system based on a liquid crystal display screen as described in claim 1, characterized in that, The step of generating backlight control commands by the control synthesis unit includes: The system compares the priority of signal response characteristics, performance stability level, and system adaptability, and sets screening conditions. Based on the screening conditions, it selects the best material from the core backlight material and alternative backlight materials. It integrates the corresponding adjustment parameters and parameter priority sequence to generate a parameter optimization list. Combining the characteristic parameters of the best material and the parameter optimization list, it synthesizes backlight control instructions through a rule engine for real-time adjustment of backlight brightness.

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