Intelligent lighting data transmission system based on visible light communication

Abstract

This invention relates to the field of visible light communication technology and discloses a smart lighting data transmission system based on visible light communication. The system includes an optical signal processing module, a transmission management module, and a terminal control module. The optical signal processing module acquires and processes visible light signals, outputting a spatially encoded light intensity signal. The transmission management module receives this signal, decodes it through a signal analysis unit to obtain the original data stream signal and channel interference characteristic signal, processes it through a channel optimization unit to obtain the optimal modulation parameter signal, and outputs a dynamic bandwidth allocation signal through a transmission strategy unit. In the terminal control module, an adaptive modulation execution unit adjusts the light transmitter drive parameters according to the optimal modulation parameter signal, a lighting array control unit adjusts the working state of the lighting units and collects real-time link status signals, and a link monitoring module outputs a communication quality index. This system is suitable for scenarios integrating smart lighting and data transmission.

Description

Technical Field

[0001] This invention relates to the field of visible light communication technology, specifically to a smart lighting data transmission system based on visible light communication. Background Technology

[0002] With the rapid development of IoT technology, the demand for data transmission between various smart devices is increasing daily, and traditional radio frequency (RF) communication technology is gradually showing its limitations in certain scenarios. For example, in indoor environments, RF signals are susceptible to multipath effects, leading to decreased transmission stability and electromagnetic interference problems, making it difficult to meet the needs of fields sensitive to electromagnetic environments such as medical and aviation.

[0003] Visible light communication technology uses visible light emitted by ordinary lighting fixtures to transmit data, combining lighting and communication functions without requiring additional radio frequency spectrum resources, making it an important direction for solving the aforementioned problems. However, current visible light communication-based systems still face many challenges in practical applications.

[0004] Existing systems have limited optical signal processing capabilities, making it difficult to effectively address signal attenuation and distortion caused by factors such as light reflection and obstruction in complex indoor environments. This results in poor quality of the acquired optical signals, affecting the accuracy of subsequent data analysis. In terms of transmission management, most systems lack dynamic adjustment mechanisms, responding slowly to changes in channel conditions and failing to optimize transmission parameters based on real-time light intensity changes and interference. This leads to low communication bandwidth utilization and unstable data transmission rates.

[0005] The terminal control section also has shortcomings. The working status adjustment of the lamp array lacks precise matching with the transmission requirements. The modulation parameters of the optical transmitter are fixed, making it difficult to adapt to the transmission requirements under different channel conditions. At the same time, the real-time and comprehensiveness of the link monitoring are insufficient, making it impossible to detect and report communication quality problems in a timely manner, which limits the reliability and practicality of the entire system. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent lighting data transmission system based on visible light communication to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides an intelligent lighting data transmission system based on visible light communication, the system comprising:

[0008] The optical signal processing module collects and processes visible light signals and outputs spatially encoded light intensity signals.

[0009] The transmission management module receives the optical intensity spatially encoded signal. The transmission management module includes a signal analysis unit, a channel optimization unit, and a transmission strategy unit. The signal analysis unit decodes the optical intensity spatially encoded signal and outputs the original data stream signal and the channel interference characteristic signal. The channel optimization unit processes the optical intensity spatially encoded signal and the original data stream signal to obtain the optimal modulation parameter signal. The transmission strategy unit defines transmission constraints based on the optical intensity spatially encoded signal and outputs a dynamic bandwidth allocation signal.

[0010] The terminal control module includes an adaptive modulation execution unit, a lamp array control unit, and a link monitoring module. The adaptive modulation execution unit adjusts the optical transmitter driving parameters according to the optimal modulation parameter signal. The lamp array control unit adjusts the working state of the lamp light-emitting unit according to the dynamic bandwidth allocation signal and the light intensity spatial coding signal, and collects real-time link status signals. The link monitoring module processes the channel interference characteristic signal and the raw data stream signal and outputs a communication quality index.

[0011] Preferably, the optical signal processing module includes a distributed photoelectric sensor array and a spatial light intensity analysis unit. The distributed photoelectric sensor array is deployed at key locations in the indoor space and collects ambient light intensity distribution data. The spatial light intensity analysis unit obtains a spatial light intensity distribution matrix through multi-angle light intensity sampling technology and forms a spatially encoded base signal. The optical signal processing module receives the ambient light intensity distribution data and the spatially encoded base signal and performs heterogeneous light intensity fusion to generate the light intensity spatially encoded signal. The light intensity spatially encoded signal includes a decoding encoded signal, an optimization encoded signal, and a strategy encoded signal.

[0012] Preferably, the signal analysis unit includes an optical signal decoding model based on time-domain feature extraction and an interference pattern recognition model. The coded signal for analysis includes background light noise spectrum data, multipath reflection delay data, spatial light intensity gradient data, and historical transmission success rate data. The background light noise spectrum data, the multipath reflection delay data, the spatial light intensity gradient data, and the historical transmission success rate data are input into the optical signal decoding model and output as the original data stream signal. The background light noise spectrum data, the multipath reflection delay data, and the spatial light intensity gradient data are fused through the improved interference pattern recognition model and the channel interference feature signal is output.

[0013] Preferably, the optical signal decoding model includes a time-series waveform reconstruction submodule, a spatial feature fusion submodule, an interference quantization submodule, and a signal restoration module. The time-series waveform reconstruction submodule performs waveform superposition analysis on the multipath reflection delay data and extracts an effective signal waveform that meets the communication frequency band requirements as a data carrier signal. The spatial feature fusion submodule uses an attention-weighted convolutional neural network to process the background light noise spectrum data, the multipath reflection delay data, and the spatial light intensity gradient data, and outputs the processing results to the interference quantization submodule. The interference quantization submodule defines an interference index algorithm, specifically including calculating a weighted combination of the mean maximum noise intensity and the spatial light intensity change gradient. The signal restoration module generates a dynamic spatial distribution map of the interference index based on a three-dimensional heatmap, using different color depths to represent the interference index of different location regions.

[0014] Preferably, the channel optimization unit processes the coded signal for optimization and the original data stream signal using a modulation-interference nonlinear relationship model, outputs an initial modulation parameter signal, and iteratively optimizes the initial modulation parameter signal using a heuristic search algorithm to obtain the optimal modulation parameter signal. The coded signal for optimization includes receiver sensitivity parameters and light source characteristic parameters. The modulation-interference nonlinear relationship model is constructed using the receiver sensitivity parameters, the original data stream signal, and the light source characteristic parameters, specifically described as a nonlinear inverse relationship between modulation depth and signal distortion.

[0015] Preferably, the channel optimization unit further includes a dynamic library of light source characteristics, a channel capacity analysis submodule, and a modulation parameter planning submodule. The dynamic library of light source characteristics stores the luminous efficiency characteristic curves of historical light source devices. The channel capacity analysis submodule calculates the maximum transmission capacity of the spatial channel based on the initial modulation parameter signal and the receiver position coordinates. The modulation parameter planning submodule generates the optimal modulation parameter signal through a heuristic search algorithm based on the maximum transmission capacity of the spatial channel, the modulation-interference nonlinear relationship model, and the light source characteristic parameters.

[0016] Preferably, the transmission strategy unit defines transmission constraints based on the strategy-encoded signal, outputs the dynamic bandwidth allocation signal, and adjusts the dynamic bandwidth allocation signal to be no lower than the minimum communication guarantee value through a dynamic programming algorithm. The strategy-encoded signal includes a real-time terminal position signal and a movement speed signal. The real-time terminal position signal and the movement speed signal define the transmission constraints, specifically expressed as a mapping relationship between terminal movement trajectory prediction and available spatial bandwidth.

[0017] Preferably, the link monitoring module includes a space communication situation platform and a quality early warning engine. The space communication situation platform receives the light intensity spatial coding signal and the real-time link status signal and dynamically renders a visible light signal coverage intensity map. The quality early warning engine processes the channel interference characteristic signal and the original data stream signal according to an improved fuzzy evaluation algorithm and outputs the communication quality index. When the communication quality index is lower than the quality index threshold, the link monitoring module controls the adaptive modulation execution unit to perform emergency modulation enhancement.

[0018] The quality warning engine includes a multi-level quality status color-coding mechanism, namely blue warning status, yellow warning status, and red warning status. When the bit error rate in the original data stream signal rises to the first bit error threshold, the blue warning status is activated, and the data transmission rate is reduced to the basic guaranteed rate. When the bit error rate in the original data stream signal rises to the second bit error threshold, the video stream data transmission is suspended and the adaptive modulation execution unit is controlled to perform signal enhancement. When the bit error rate in the original data stream signal rises to the third bit error threshold, the quality warning engine issues a communication interruption alarm and triggers the system communication protection mechanism.

[0019] Preferably, the optimal modulation parameter signal includes modulation mode type, pulse width range and duty cycle adjustment step size; the adaptive modulation execution unit includes intelligent driving circuit and integrated optical power detection device; the intelligent driving circuit analyzes the optimal modulation parameter signal and adjusts the driving current waveform as needed; the integrated optical power detection device receives the optimal modulation parameter signal and monitors the light flux of the light-emitting unit in real time based on the photoelectric conversion principle; and the real-time link status signal includes receiver signal-to-noise ratio, data packet loss rate and channel delay jitter.

[0020] Preferably, a lamp includes a lamp body, a base, and the aforementioned intelligent lamp data transmission system based on visible light communication. The base is fitted with a connecting plate via a snap-fit ​​structure. The connecting plate is connected to the lamp body via a first decorative plate. Replaceable second decorative plates are also provided on both sides of the connecting plate.

[0021] Compared with the prior art, the beneficial effects of the present invention are:

[0022] The optical signal processing module collects and processes visible light signals and outputs spatially encoded light intensity signals. It can accurately capture light intensity information at different spatial locations, effectively cope with signal interference caused by light reflection and occlusion, improve the quality of the original light signal, and lay a good foundation for subsequent data transmission.

[0023] The signal analysis unit in the transmission management module decodes the optical intensity spatially encoded signal to obtain the original data stream signal and channel interference characteristic signal. This clearly separates valid data from interference information, ensuring accurate extraction of the original data. The channel optimization unit combines the optical intensity spatially encoded signal and the original data stream signal to obtain the optimal modulation parameter signal. It can dynamically optimize the modulation method based on real-time signal characteristics, making the optical signal transmission more suitable for the current channel conditions and reducing signal distortion. The transmission strategy unit defines transmission constraints based on the optical intensity spatially encoded signal and outputs a dynamic bandwidth allocation signal. This allows for flexible allocation of bandwidth resources according to the optical intensity distribution and communication needs of different areas, avoiding bandwidth waste and improving overall transmission efficiency.

[0024] The adaptive modulation execution unit of the terminal control module adjusts the optical transmitter drive parameters based on the optimal modulation parameter signal, matching the optical transmitter's operating state with the channel optimization results. This enhances the adaptability of optical signal transmission and ensures good signal strength under different channel environments. The luminaire array control unit adjusts the operating state of the luminaire's light-emitting units based on dynamic bandwidth allocation signals and light intensity spatial coding signals. This achieves coordinated operation between the luminaire array and data transmission requirements, allowing the luminaires to efficiently handle communication tasks while fulfilling their lighting functions. Simultaneously, the collected real-time link status signals provide timely feedback for system adjustments. The link monitoring module processes channel interference characteristic signals and raw data stream signals and outputs a communication quality index, comprehensively reflecting the quality status during communication. This facilitates timely detection of communication anomalies, providing a reference for further optimization of transmission strategies and terminal control. This creates a closed-loop adjustment mechanism, improving system stability and adaptability, and making intelligent luminaire data transmission based on visible light communication more practical in complex indoor environments. Attached Figure Description

[0025] Figure 1 This is a schematic diagram illustrating the working principle of the intelligent lighting data transmission system based on visible light communication as described in this invention.

[0026] Figure 2 A flowchart for generating spatially encoded light intensity signals for the optical signal processing module;

[0027] Figure 3 A flowchart for optical signal decoding model processing;

[0028] Figure 4 A detailed flowchart for the channel optimization unit;

[0029] Figure 5 A flowchart for communication quality monitoring in the link monitoring module;

[0030] Figure 6 This is a schematic diagram of the overall structure of a lamp according to the present invention;

[0031] Figure 7 This is a schematic diagram showing the overall structure of a lamp according to the present invention.

[0032] In the diagram: 1. Lamp body; 2. Base; 3. Clip-on structure; 4. Connecting plate; 5. First decorative plate; 6. Second decorative plate. Detailed Implementation

[0033] 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.

[0034] Please see Figure 1 This invention provides an intelligent lighting data transmission system based on visible light communication, the system comprising:

[0035] Efficient and stable visible light communication is achieved through the coordinated operation of the optical signal processing module, transmission management module, and terminal control module. The optical signal processing module acquires and processes visible light signals, outputting a spatially encoded light intensity signal. Upon receiving this signal, the transmission management module decodes the original data stream signal and channel interference characteristic signal through a signal analysis unit, generates the optimal modulation parameter signal through a channel optimization unit, and outputs a dynamic bandwidth allocation signal through a transmission strategy unit. The terminal control module adjusts the optical transmitter drive parameters based on the optimal modulation parameter signal and regulates the operating status of the luminaire's light-emitting unit based on the dynamic bandwidth allocation signal. Simultaneously, the link monitoring module evaluates communication quality in real time.

[0036] Example 1: See Figure 2 The distributed photoelectric sensor array of the optical signal processing module adopts a modular design. Each sensing node includes a photodiode, signal conditioning circuit, and microprocessor. The photodiode is a silicon-based PIN device with a spectral response range covering the visible light band, and its peak response wavelength is matched to the LED light source. The signal conditioning circuit includes a transimpedance amplifier and a bandpass filter. The transimpedance amplifier converts the weak current output from the photodiode into a voltage signal, and the bandpass filter suppresses low-frequency fluctuations and high-frequency noise in ambient light. The microprocessor communicates with the main control system via an I2C interface and has a built-in 12-bit ADC that collects light intensity data at a sampling rate of no less than 1kHz. The sensing nodes are deployed in a honeycomb topology on the indoor ceiling, and the node spacing is dynamically adjusted according to the room height to ensure that each location is covered by at least three nodes.

[0037] The multi-angle light intensity sampling technology of the spatial light intensity analysis unit is achieved through a mechanical rotating platform. The platform is equipped with a narrow field-of-view lens and a filter array, performing a 360-degree horizontal scan in 5-degree increments. During the scan, the filter array sequentially switches to red, green, and blue channels to record the light intensity distribution at different wavelengths. After coordinate transformation, the scan data generates a three-dimensional spatial light intensity distribution matrix, with the matrix dimensions corresponding to the number of sampling points in the length, width, and height directions of the room. The spatially encoded substrate signal is generated using a non-uniform quantization method, dividing the light intensity values ​​into 256 levels. The data at each sampling point includes temporal waveform characteristics, spectral energy distribution, and spatial coordinate information.

[0038] When the optical signal processing module performs heterogeneous light intensity fusion, it first performs spatiotemporal alignment processing on the ambient light intensity distribution data collected by the distributed photoelectric sensor array. Temporal alignment uses interpolation to compensate for sampling time deviations between nodes, while spatial alignment transforms all node data to a unified world coordinate system through coordinate system transformation. The fusion algorithm employs a weighted average strategy, with weight coefficients dynamically calculated based on the node signal-to-noise ratio and spatial location. The resulting spatially encoded light intensity signal contains three types of sub-signals: the analytical encoded signal consists of the original light intensity sampling data and time-frequency domain features; the optimization encoded signal includes light source modulation characteristics and channel response parameters; and the strategy encoded signal focuses on recording spatial light intensity gradient changes and moving object detection results.

[0039] The optical signal decoding model of the signal analysis unit adopts a hierarchical processing architecture. When processing multipath reflection delay data, the time-series waveform reconstruction submodule first identifies the arrival time difference between the direct and reflected paths, and eliminates inter-symbol interference caused by multipath effects through an adaptive delay line. After upsampling, the reconstructed waveform uses a matched filter to extract the effective signal components. The spatial feature fusion submodule's convolutional neural network input is a four-dimensional tensor, with three spatial dimensions corresponding to room coordinates and the channel dimension storing different feature maps. The attention mechanism generates a feature weight matrix based on spatial light intensity gradient data, highlighting features in high signal-to-noise ratio regions. Network training employs a semi-supervised learning method, pre-training with a large amount of unlabeled indoor environmental data, and then fine-tuning the model parameters with a small amount of labeled data.

[0040] The interference pattern recognition model employs a dual-branch structure, handling temporal and spatial interference features separately. The temporal branch analyzes the periodic fluctuations in the background light noise spectrum data, using a spectral clustering algorithm to distinguish between natural light fluctuations and artificial light source interference. The spatial branch processes spatial light intensity gradient data, employing an edge detection operator to identify regions of sudden light intensity changes. The outputs of the two branches are fused at the feature level, and a support vector machine classifier is used to determine the interference type. The output includes the location of the interference source, intensity level, and time-varying characteristics. The channel interference feature signal is encapsulated in JSON format, containing interference type encoding, spatial distribution map, and time-varying characteristic curves.

[0041] The recovery process of the raw data stream signal employs an iterative decoding algorithm. Initial demodulation generates a candidate symbol sequence based on standard modulation parameters, and a correlation score is calculated between the sequence and the received waveform. The score is fed back to the decoding model, dynamically adjusting the time-frequency domain feature extraction parameters. During each iteration, the spatial feature fusion submodule updates the attention weight matrix, gradually focusing on the spatial region where effective signals are concentrated. Finally, the output raw data stream signal undergoes CRC verification, and a timestamp and spatial location tag are appended and stored in the system buffer.

[0042] The coded signal used for analysis in the spatially encoded light intensity signal adopts a hierarchical data structure. The bottom layer contains the raw sampled data, storing the time-domain waveforms acquired by each photoelectric sensor node. The middle layer contains time-frequency analysis results, with each sampled data point appended with a short-time Fourier transform spectrum and wavelet decomposition coefficients. The top layer is a feature abstract representation, with compressed feature vectors extracted through an autoencoder network occupying 32 dimensions. This hierarchical structure balances data integrity and processing efficiency, allowing for the selective use of sub-signals from different levels according to subsequent processing requirements.

[0043] The spatial interference index calculation in the interference quantization submodule employs a sliding window method. A cubic neighborhood window is established centered on each sampling point on the three-dimensional spatial light intensity distribution matrix, with the window size adaptively adjusted according to the room height. Within the window, the normalized product of the maximum noise intensity and the average light intensity gradient is calculated, and then mapped to an interference index between 0 and 1 using the sigmoid function. The dynamic spatial distribution map of the interference index is visualized using volume rendering technology, with transparency mapping representing the spatial attenuation characteristics of the interference intensity. The user interface supports 3D rotation and slice viewing, facilitating intuitive identification of the distribution patterns of interference sources.

[0044] Historical transmission success rate data is processed using time series analysis. The system maintains a circular buffer to record the bit error rate, throughput, and latency of the most recent 100 communication tasks. Each time new data arrives, the feature statistics are updated using an exponentially weighted moving average algorithm. These statistics serve as prior knowledge in adjusting the parameters of the optical signal decoding model, providing stability during sudden changes in ambient light conditions. The decoding model dynamically adjusts the sensitivity threshold for feature extraction based on historical data, balancing noise suppression and signal fidelity requirements.

[0045] Example 2: See Figure 3The timing waveform reconstruction submodule of the optical signal decoding model employs a multi-stage signal processing flow to handle multipath reflection delay data. The input signal first passes through an anti-aliasing filter with a cutoff frequency set to 1.5 times the upper limit of the communication band to eliminate high-frequency noise interference. The filtered signal then enters a time-domain alignment unit, which uses a sliding correlation algorithm to detect the arrival time difference between the direct and reflected paths, establishing a multipath delay distribution histogram. The delay value corresponding to the peak value of the histogram is used to initialize the adaptive delay line parameters. The delay line is implemented using a fractional-order delay filter with an adjustment step size of 1 / 8 of the sampling interval. The aligned multipath signals undergo coherent merging in the superposition unit, with the merging weights dynamically allocated based on the signal-to-noise ratio of each path. After interpolation, the reconstructed waveform is fed into a symbol detector based on the maximum likelihood criterion. The detector outputs a complex sequence containing symbol amplitude and phase information.

[0046] The spatial feature fusion submodule employs a channel-spatial dual-path attention mechanism. The channel attention branch performs global average pooling on the input feature map to generate channel description vectors, which are then passed through two fully connected layers to output channel weight coefficients. The spatial attention branch generates a spatial weight map using 3×3 convolutional layers, focusing on regions with drastic light intensity changes. The weight matrices of the two branches are fused using Hadamard product to form the final feature selection mask. The backbone of the convolutional neural network uses a residual connection structure, containing three residual blocks, each consisting of two convolutional layers and a skip connection. Network training employs a multi-task learning strategy, simultaneously optimizing signal recovery quality and interference suppression metrics. The loss function is designed as a weighted sum of mean squared error and cross-entropy.

[0047] The interference index algorithm in the interference quantization submodule employs a nonlinear mapping strategy. The mean maximum noise intensity is obtained through sliding window extreme value statistics, with the window length maintaining an integer multiple relationship to the communication symbol period. The spatial light intensity variation gradient calculation uses the Sobel operator, performing gradient operations in the x, y, and z directions of three-dimensional space, and taking the modulus as the comprehensive gradient value. The weighted combination function adopts a piecewise linear design, assigning higher weights to gradient changes in low-interference regions and emphasizing the influence of noise intensity in high-interference regions. The dynamic range of the interference index is adjusted through adaptive normalization, with the normalization parameter determined based on the 95th percentile of historical statistical data. The quantization results are stored in a three-dimensional mesh data structure in the form of floating-point arrays, with the mesh resolution consistent with the spatial light intensity distribution matrix.

[0048] The signal reconstruction module generates 3D heatmaps using a ray casting algorithm. During volume data preprocessing, Gaussian smoothing is applied to the interference index to eliminate local abrupt noise. A color map defines a continuous gradient from blue to red, corresponding to the interference index changes from 0 to 1. The rendering pipeline uses an adjustable transparency transfer function to render highly interference areas with richer colors. View transformation supports both orthographic and perspective projection modes, and users can adjust the viewing angle and slice position via an interactive interface. The heatmap is overlaid on the 3D room model; the model data is imported from the building information system and includes information on obstacles such as walls, doors, windows, and furniture. The display system employs a double-buffering mechanism to ensure visual continuity during screen refresh.

[0049] The modulation-interference nonlinear relationship model of the channel optimization unit is established using a polynomial regression method. The model input includes three main variables: modulation depth, light source driving current, and receiver distance. During the data acquisition phase, sample points are obtained through system identification experiments. The experimental design employs the Latin hypercube sampling method to ensure uniform coverage of the input space. The second-order polynomial term includes squared terms and cross terms of the independent variables, and the coefficient matrix is ​​obtained by least squares fitting. Model validation uses k-fold cross-validation, and the dataset is divided into training and test sets. Two conditions are set for the model update triggering mechanism: periodic updates are performed at fixed time intervals, and event-triggered updates are initiated immediately upon detection of light source aging or changes in environmental structure.

[0050] The simulated annealing implementation of the heuristic search algorithm comprises two core components: temperature scheduling and state generation. The initial temperature parameter is set to 10% of the objective function's variation range, employing a logarithmic cooling strategy. The new state generation mechanism combines random perturbation and gradient information, favoring random exploration at high temperatures and increasing the proportion of local search at low temperatures. The acceptance criterion adopts the Metropolis criterion, allowing for the acceptance of inferior solutions with a certain probability to avoid local optima. The algorithm's termination condition is set with three checks: reaching the maximum number of iterations, the temperature falling below a threshold, or the optimal solution remaining unchanged for several consecutive iterations. During each iteration, the algorithm outputs the current optimal solution as candidate parameters for the system to use in real time.

[0051] The receiver sensitivity parameters in the optimized coded signal were obtained through calibration experiments. A standard test environment was set up, and a programmable optical attenuator was used to precisely control the received optical power. Bit error rate (BER) curves were measured under different modulation parameters. Sensitivity was defined as the minimum received optical power required to meet the target BER requirement, and the test results were stored in a lookup table format. Light source characteristic parameters, including the LED's PI characteristic curve and modulation bandwidth characteristics, were obtained from the manufacturer's datasheet combined with measured data. A parameter update mechanism monitors the cumulative effects of LED junction temperature and operating time; recalibration is triggered when the detected decrease in optical output power exceeds a threshold.

[0052] The modeling of the relationship between modulation depth and signal distortion considers the combined effects of nonlinear and linear distortion. Nonlinear distortion mainly originates from the bending segment of the LED's PI characteristic and is approximated using Taylor series expansion. Linear distortion is caused by the channel frequency response and manifests as inter-symbol interference, quantified by eye diagram opening. The comprehensive distortion index is defined as a weighted average of the constellation diagram error vector amplitudes, with the weighting coefficients dynamically adjusted according to the communication service type. The relationship model is established using a piecewise modeling strategy, employing different polynomial orders in different ranges of the LED operating current to balance model accuracy and computational complexity.

[0053] The initial modulation parameter signal is generated using a rule-based method. The system maintains a modulation parameter knowledge base, storing optimal parameter combinations for typical scenarios. Initial parameters for new scenarios are determined through nearest neighbor search, finding the record in the knowledge base with the most similar environmental features as a reference. The parameter adjustment range is dynamically set based on the light source characteristics, ensuring the operating point remains within the LED's safe operating zone. A boundary check mechanism is implemented during parameter optimization to prevent the search algorithm from generating unrealistic parameter suggestions. After each successful communication, the system stores the final adopted modulation parameters along with environmental features in the knowledge base, enabling continuous self-learning capabilities.

[0054] The spatial feature fusion visualization debugging interface provides real-time display of feature maps. The interface uses a split-screen layout, with the original light intensity distribution displayed on the left and the convolutional feature maps of each layer presented on the right. Users can select any feature map channel to observe its activation pattern in space. The attention weight matrix is ​​displayed in a semi-transparent overlay, intuitively showing the key areas of focus for the network. The interface integrates parameter adjustment controls, supporting manual adjustment of convolutional kernel weights and attention parameters for analyzing model decision-making. Debugging data can be recorded and saved for subsequent offline analysis and model optimization.

[0055] The channel optimization unit employs a dynamic load balancing strategy for computing resource management. When the system detects multiple terminals simultaneously requesting parameter optimization, the task scheduler allocates computing resources based on the problem complexity. Optimization tasks for simple scenarios are assigned to edge computing nodes, while tasks for complex scenarios are submitted to the cloud computing cluster. Local computing utilizes fixed-point arithmetic for acceleration, improving processing speed while maintaining accuracy. Resource allocation decisions consider factors such as current system load, task urgency, and energy efficiency, determining the optimal allocation scheme through a weighted scoring method.

[0056] Example 3: See Figure 4The dynamic library for light source characteristics employs a hierarchical storage structure to organize the luminous efficiency characteristic curves of historical light source devices. The bottom layer stores raw test data, including luminous flux measurements under different operating currents, and records ambient temperature and heat sink parameters as test conditions. The middle layer stores fitted curve parameters, with each curve represented using piecewise cubic Hermitian interpolation, and inflection points set according to LED aging characteristics. The top layer stores statistical features, including efficiency decay rate and color temperature drift coefficient, for rapid assessment of light source status. The data access interface supports time range queries and conditional filtering, with query results sorted by confidence level. The dynamic library's update mechanism includes a periodic refit function; when newly accumulated test data exceeds a threshold, curve parameters are automatically recalculated. Aging trend prediction uses time series analysis to establish a correlation model between luminous flux decay and operating time.

[0057]

[0058] in, Let represent the luminous flux at time t. The initial luminous flux, The decay rate coefficient, This is a nonlinear correction factor. Model parameters were determined using maximum likelihood estimation, and confidence intervals were calculated using Monte Carlo simulation.

[0059] The channel capacity analysis submodule calculates the maximum transmission capacity of the spatial channel by considering the combined effects of path loss and interference noise. The path loss model employs a modified free-space propagation formula, incorporating a wall reflection attenuation factor. Receiver position coordinates are obtained using triangulation, with the positioning system consisting of at least three reference nodes, achieving nanosecond-level time synchronization accuracy between nodes. The channel state information acquisition period is adaptively adjusted, shortening the sampling interval when the terminal's movement speed is high. Capacity calculation results are cached, and interpolation is used to reduce redundant calculations when querying spatially adjacent points. A dynamic update mechanism triggers local recalculation upon detecting changes in the environmental layout; change detection is achieved through light intensity distribution comparison.

[0060] The genetic algorithm implementation of the modulation parameter planning submodule employs an elite-preserving strategy. During population initialization, individual gene encoding includes three parts: modulation type, symbol rate, and error correction coding scheme. The fitness function is designed as a multi-dimensional weighted sum, incorporating three indicators: channel capacity utilization, marginal bit error rate, and energy efficiency. Crossover is performed using a two-point crossover method, with crossover points randomly selected while maintaining gene field integrity. Mutation introduces a directed mutation mechanism, increasing the search density near superior individuals. The algorithm is parallelized using a master-slave architecture, with the master node managing population evolution and slave nodes responsible for individual evaluation. The convergence criterion combines generational improvement rate and population diversity indicators to avoid premature convergence.

[0061] The real-time terminal position signal acquisition system employs ultra-wideband pulse radar technology. Anchor nodes are deployed at the four corners of the room, while tag nodes are integrated into the terminal equipment. The ranging signal uses time-hopping pulse position modulation to effectively suppress multipath interference. Position calculation uses the least squares method, combining RSSI ranging and AOA angle measurement data. The movement speed signal is processed through a Kalman filter to handle the original position sequence, and the state equation is modeled as uniformly accelerated motion. The filter parameters are adaptively adjusted, increasing the process noise weight during the straight-line motion phase and increasing the measurement update frequency during the turning phase. The data fusion module weights and merges the visual-assisted positioning results with the radio frequency positioning results, with the weighting coefficients dynamically adjusted based on the confidence level.

[0062] The mathematical expression of transmission constraints employs a spatiotemporal joint modeling method. Terminal trajectory prediction is based on current speed and historical location, generating future trajectories through cubic spline interpolation. A spatial available bandwidth mapping is used for discretization, dividing the indoor space into a cubic grid, with each grid storing the current channel capacity and allocated bandwidth. The constraint generator calculates the sequence of grids traversed by the trajectory, extracting the remaining bandwidth of each grid to form a constraint vector. A conflict detection mechanism identifies bandwidth contention among multiple terminals, and a weighted round-robin scheduling strategy is used to resolve the contention.

[0063] The state transition equation of the dynamic programming algorithm is designed as a multi-stage decision-making process. The stages correspond to discrete time intervals, and the state variables include the location grid index and available bandwidth. The decision variable is the bandwidth allocation, and the reward function is defined as the product of communication quality satisfaction and resource utilization. Boundary conditions are set to guarantee minimum communication security values, and hard constraints are handled using the Lagrange relaxation method. The algorithm implementation employs a value iteration method, calculating the state value function of each stage in parallel. A result caching mechanism stores solutions for common scenarios, accelerating the online decision-making process.

[0064] The interface design between the modulation parameter planning submodule and the hardware driver layer adopts an abstract command pattern. The optimal modulation parameter signal is converted into a device-independent sequence of operation instructions, which include delay requirements and electrical parameter tolerances. The state feedback of the driver circuit is incorporated into closed-loop optimization; when the deviation between the actual output waveform and the expected value exceeds a threshold, parameter recalculation is triggered. The safety monitoring unit monitors the LED junction temperature and drive current in real time, triggering protective modulation degradation in abnormal situations. The interface is compatible with various light source drivers and supports different manufacturers' protocol standards through an adapter mode.

[0065] The spatial grid partitioning for channel capacity analysis employs an octree data structure. The initial grid size is set to the wavelength order of magnitude, with automatic refinement in high-capacity gradient regions. Grid attributes include path loss coefficients, multipath delay spread, and interference noise spectral density. A dynamic update mechanism locally reconstructs the grid structure upon detecting furniture movement; change detection is achieved through background subtraction. Query optimization utilizes spatial indexing for acceleration, with range query response times controlled within milliseconds. Grid data is persistently stored, and the system quickly restores its working state upon restart.

[0066] The coordinate transformation module for real-time terminal position signals uses quaternion rotation representation. The transformation relationship between the indoor global coordinate system and the terminal local coordinate system is determined through a calibration process. Coordinate correction considers changes in device attitude, and the built-in inertial measurement unit compensates for installation deviations. Data time alignment uses interpolation to eliminate acquisition delays from different sensors. A confidence index is added to the coordinate transformation results for upper-layer algorithms to evaluate position reliability. An outlier detection mechanism identifies signal jumps and determines whether they represent genuine movement through historical trajectory analysis.

[0067] The resource reservation mechanism of the transmission strategy unit adopts a sliding window prediction method. The window size is adaptively adjusted according to the terminal's movement pattern, extending the window for linear movement and shortening it for random movement. The reserved bandwidth release strategy includes two modes: timeout release and explicit release. The conflict resolution module implements two-level arbitration, first attempting time-frequency resource reuse, and triggering priority preemption if necessary. The resource status visualization interface displays a bandwidth utilization heatmap for each spatial region to assist in network operation and maintenance decisions.

[0068] The quality assessment module for modulation parameter planning employs digital twin technology. Before parameter deployment, its performance in a real-world environment is predicted through virtual simulation. The simulation model incorporates key factors such as light source nonlinearity, channel multipath, and receiver noise. Model parameters are updated in real-time through system identification to maintain consistency with the physical system. Evaluation results include multi-dimensional performance indicators, visually demonstrating the advantages and disadvantages of different schemes through radar charts. Deviation data between virtual simulation and actual deployment is used to continuously optimize model accuracy.

[0069] Example 4: See Figure 5The visible light signal coverage intensity map of the space communication situational awareness platform is implemented using layered rendering technology. The bottom layer is an indoor 3D structural model, imported from CAD design files and converted into lightweight mesh data. The middle layer displays real-time light intensity distribution; sampling point data is converted into texture maps through color mapping, with red representing high-intensity areas (>2000 lux) and blue representing low-intensity areas (<500 lux). The top layer overlays communication quality markers, using icons of different shapes to represent the connection status of each terminal. The rendering engine uses a physically based lighting model to simulate the radiation characteristics of LED light sources. View control supports zooming, rotation, and profile viewing; the profile plane can be positioned along any axis. Data updates use a differential transmission mechanism, sending only incremental data for changed areas to reduce network load.

[0070] The improved fuzzy evaluation algorithm for the quality early warning engine includes a feature normalization step in its input processing. The interference index in the channel interference feature signal is converted into a 0-1 membership degree, dividing it into three fuzzy sets: "slight," "moderate," and "severe." The bit error rate in the original data stream signal is logarithmically transformed and matched with a Gaussian membership function. The 25 rules in the fuzzy rule base are semantically defined, such as "if the interference is severe and the bit error rate is high, then the quality is very poor." The inference process uses the Mamdani method, and defuzzification uses the centroid method to calculate the precise output value. The grading thresholds for the communication quality index are determined through statistical analysis, considering the tolerance differences among different service types.

[0071] A multi-level quality status color-coding mechanism implements a tiered response strategy. Under a blue alert, the system automatically switches to a more robust modulation and coding scheme, such as downgrading from 16QAM to QPSK. A yellow alert triggers content adaptation, switching the video stream to lower-resolution encoding and enabling stronger error correction encoding for the audio stream. A red alert activates an emergency communication protocol, employing a repeated transmission and acknowledgment mechanism for critical control commands. A hysteresis interval is set for state transitions to prevent system oscillations caused by frequent switching. All alert events are recorded in the audit log, including timestamps, location information, and handling measures.

[0072] The emergency modulation enhancement function of the link monitoring module is implemented through dynamic parameter adjustment. When the communication quality index falls below the threshold, the system gradually increases the modulation depth until it reaches the preset upper limit. The drive current waveform is changed to a trapezoidal pulse to reduce edge steepness and minimize spectral leakage. The symbol period is appropriately extended, sacrificing rate for reliability. The duration of the enhancement mode is dynamically adjusted based on channel stability assessment, and an exponential backoff algorithm is used to gradually attempt to restore normal parameters. After each parameter adjustment, the bit error rate is monitored, and the setting is fixed if effective; otherwise, it reverts to the safe mode.

[0073] The integration of the space communication situation platform and the building management system enables environment-linked control. When it is detected that the communication quality in a certain area deteriorates continuously, the auxiliary lighting intensity in that area is automatically adjusted. The curtain control system adjusts the opening and closing degree according to the degree of sunlight interference to maintain a stable background light environment. The air conditioner wind direction avoids key communication areas to reduce the refractive changes caused by airflow disturbance. The linkage instructions are transmitted through the OPC UA protocol, and the execution results are fed back to the situation platform for real-time display. The system maintains a device status matrix to record the response characteristics and adjustment ranges of each controllable device.

[0074] The rule self-optimization mechanism of the quality warning engine is continuously improved through case learning. After each warning processing is completed, the system records the environmental characteristics, disposal measures, and actual effects. The typical case library uses a similarity retrieval algorithm to preferentially match historical successful cases in new scenarios. The rule weights are dynamically adjusted according to the disposal effects, and successful experiences strengthen the influence of relevant rules. The abnormal disposal analysis module identifies contradictory cases and triggers the expert review process. The knowledge graph technology establishes an association network between environmental characteristics and disposal solutions to assist decision-making reasoning.

[0075] The spatial interpolation algorithm of the communication quality index addresses the sparse sampling problem. The quality indices of known monitoring points are extended to unsampled areas through radial basis function interpolation. The interpolation kernel function uses an adaptive support radius to reduce the range of action in areas with dense obstacles. Boundary processing considers the wall reflection characteristics and introduces mirror virtual sampling points. The interpolation results are corrected for obstacle occlusion to ensure compliance with the physical propagation law. The spatial continuity constraint prevents drastic quality jumps in adjacent grids and eliminates local outliers through smoothing filtering.

[0076] The communication protection mechanism of the link monitoring module implements a multi-level defense strategy. The first-level protection triggers parameter adjustment to attempt to restore communication on the existing link. The second-level protection activates the standby optical path to establish a detour link through adjacent lamps. The third-level protection switches to the radio frequency auxiliary channel to maintain a minimum control connection. The protection level is dynamically selected according to the business criticality, and critical instructions directly reach the third-level protection. The progressive fallback strategy is adopted during the status recovery phase to preferentially restore high-value service links. The protection log details the fault characteristics and disposal process to support root cause analysis after the event.

[0077] Table 1: Multi-level quality state conversion conditions and response measures;

[0078] Status Level Triggering conditions Modulation scheme adjustment Business adaptation measures Recovery test method Blue Alert Bit error rate > 1E-4 Reduced-order modulation (e.g., 16QAM → QPSK) Reduce non-real-time service bandwidth Try to restore the original modulation every 5 minutes. Yellow Alert Bit error rate > 1E-3 Increase modulation depth by 20% Pause video stream, enhance FEC Channel stability is tested every 2 minutes. Red Alert Bit error rate > 1E-2 Switch to baseband transmission mode Transmit only critical control commands Continue monitoring until the bit error rate is <1E-4

[0079] The human-computer interface design of the quality early warning engine follows the principle of situational awareness. The 3D situational map and the 2D planar map are synchronized and linked, supporting multi-view collaborative analysis. Early warning events are displayed in clusters based on spatial location, and clicking on them pops up a details panel showing the time-series change curves. The list of handling suggestions is sorted by priority, providing one-click execution. The historical data comparison function supports overlaying and displaying quality distributions across multiple time periods to help identify potential problems. The interface theme color changes with the system's early warning status, gradually transitioning from a calm blue to an alarming red.

[0080] The emergency-modulated enhanced safety monitoring module implements multiple protection measures. Real-time monitoring of the drive current prevents it from exceeding the LED's maximum rated value. A temperature sensor network detects the lamp's heat dissipation status, automatically reducing power in case of overheating. A waveform distortion analysis module monitors modulation quality, triggering protective shutdown upon abnormal waveforms. All protection actions are recorded as safety events, requiring authorized confirmation before the system can be reset. Protection parameters are set in layers, allowing for differentiated configurations for areas with different safety levels.

[0081] The space communication situational awareness platform performs data fusion processing of multi-source heterogeneous information. Visible light positioning data and UWB positioning results are spatiotemporally aligned and fused using Kalman filtering. Environmental sensor data (temperature, humidity, illuminance) are correlated with communication quality indicators. Personnel density information is extracted from video surveillance footage through image recognition for load balancing decisions. The fusion results are stored in a spatiotemporal cube database, supporting multi-dimensional joint queries and analysis. Data timestamps are synchronized using the BeiDou Navigation Satellite System to ensure cross-system synchronization accuracy.

[0082] The predictive maintenance function of the link monitoring module is based on an equipment degradation model. LED light decay curves are updated with model parameters by monitoring optical power online. Aging characteristics of the driver circuit are extracted from current harmonic components. Degradation of heat dissipation performance is assessed by changes in the relationship between cooling fan speed and temperature. Predictive results generate maintenance priority scores to guide on-site maintenance planning. The spare parts inventory management system correlates with predictive data to proactively allocate critical replacement parts. Maintenance records are stored in a structured manner, supporting statistical analysis by equipment lifecycle.

[0083] The mobile terminal push service for quality early warnings implements a tiered notification strategy. Blue warnings are sent to the mobile terminals of regional maintenance personnel, prompting them to observe the situation. Yellow warnings are accompanied by a notification to the on-duty engineer, requiring them to prepare a response plan. Red warnings trigger a multi-party conference call, convening relevant experts for consultation. The message content includes on-site snapshots and key parameter curves, supporting remote diagnostics. A confirmation receipt mechanism ensures information delivery; if no response is received within the time limit, the notification level is automatically escalated. After communication is restored, a summary report is sent, summarizing the cause of the fault and improvement measures.

[0084] Example 5: The selection of modulation scheme type in the optimal modulation parameter signal adopts an adaptive decision-making mechanism based on environmental characteristics. The system maintains a modulation scheme feature matrix, recording the applicability indicators of modulation types such as PPM, OFDM, and CAP in different scenarios. The decision-making process considers three dimensions: current channel interference characteristics, terminal mobility speed, and service type requirements. PPM modulation is preferentially applied to high-interference, low-rate control command transmission, and its anti-interference capability comes from the redundancy characteristics of pulse position coding. OFDM modulation is suitable for high-capacity data transmission in static environments, adapting to frequency-selective fading through multi-subcarrier allocation. CAP modulation performs well in medium-speed scenarios, and its band-limited characteristics effectively suppress adjacent-channel interference. A smooth transition period is set during the modulation type switching process to avoid communication interruption. During the transition period, dual-modulation parallel transmission is used to ensure data continuity.

[0085] Dynamic adjustment of the pulse width range is achieved through the digital control unit of the drive circuit. The control register stores the current pulse width parameter with nanosecond-level resolution. Parameter updates employ a buffer mechanism, waiting for the next clock cycle to take effect after a new value is written. The width adjustment algorithm considers the response speed limitations of the LED devices, avoiding setting narrow pulses beyond the capabilities of the physical devices. The protection circuit monitors the pulse waveform quality and automatically reverts to safe parameters when edge distortion is detected. The matching relationship between pulse width and symbol rate is maintained through a lookup table, the contents of which are periodically calibrated based on measured eye diagram quality. During dynamic adjustment, the system monitors changes in the bit error rate in real time to ensure that parameter optimization does not reduce communication reliability.

[0086] Fine-grained control of the duty cycle adjustment step size relies on a high-precision timer architecture. The timers employ a cascaded design, with a coarse-tuning counter handling millisecond-level period settings and a fine-tuning phase-locked loop achieving step-level fine adjustment. The duty cycle adjustment strategy distinguishes between two objectives: thermal management and communication quality. In thermal management mode, the system gradually reduces the duty cycle until the junction temperature returns to a safe range, while simultaneously monitoring changes in luminous flux. In communication quality optimization mode, the duty cycle and modulation depth are adjusted collaboratively to find the optimal signal-to-noise ratio operating point. The step size dynamically changes according to environmental stability; small step sizes are used for fine-grained adjustment in stable environments, while larger step sizes are used to accelerate convergence in rapidly changing scenarios. Historical adjustment records are used to build a device characteristic model and predict system performance under different duty cycles.

[0087] The intelligent drive circuit of the adaptive modulation execution unit adopts a mixed-signal design architecture. The digital section includes a parameter resolver and timing controller, which convert abstract modulation parameters into specific register configuration values. The analog section includes a programmable current source and a voltage buffer stage, with the current output range covering the entire LED operating range. The drive waveform calibration function is automatically executed after each parameter change, measuring the deviation between the actual output and the expected waveform through a feedback loop, and fine-tuning the drive parameters to compensate for the difference. The circuit protection module integrates overcurrent, overvoltage, and overheat detection, triggering hardware-level protection without software intervention in abnormal situations. The drive capability is designed in a hierarchical manner, with the basic drive unit handling routine communication and the auxiliary enhancement unit providing additional current margin during emergency modulation.

[0088] The photoelectric conversion link of the integrated optical power detection device adopts a differential input structure. The detection front end includes an optical filter and a programmable gain transimpedance amplifier. The filter bandwidth matches the LED emission spectrum to suppress ambient light interference. The amplifier gain automatically switches according to the expected optical power range to avoid signal saturation or signal-to-noise ratio degradation. The analog-to-digital conversion adopts a Σ-Δ architecture, achieving 20-bit effective resolution at a 1kHz sampling rate. The optical power calculation algorithm compensates for the change in LED luminous efficiency with temperature, and performs real-time correction with reference to the readings of the built-in temperature sensor. The detection data is transmitted to the processing unit through a digital isolator, and the isolation voltage meets the safety requirements of mixed high-voltage and low-voltage scenarios. The device periodically performs a self-calibration sequence, verifying the accuracy of the detection link through a built-in standard light source.

[0089] The receiver signal-to-noise ratio (SNR) measurement in the real-time link status signal employs in-band pilot technology. The system reserves several unmodulated subcarriers within the communication band as noise sampling points. The SNR calculator compares the power spectral density of the data subcarriers with that of the pilot subcarriers to eliminate the influence of channel frequency response. The measurement results are filtered using a moving average to suppress fluctuations caused by transient interference. A confidence level flag is added to the SNR indicator; low-confidence measurements do not trigger system parameter adjustments. Historical SNR data is stored as a time series for analyzing the periodic characteristics of channel changes.

[0090] The packet loss rate is calculated using a sliding window counting method. The sender appends a sequence number to each packet, and the receiver identifies lost packets by detecting the continuity of these sequence numbers. The statistics window contains the most recent 100 packets, and a counter records the number and distribution characteristics of lost packets. Different handling strategies are employed for sudden and random packet loss: sudden loss triggers enhanced forward error correction parameters, while random loss adjusts the retransmission timeout. The statistics module distinguishes between packets from different priority services, and critical control commands are calculated separately for loss rate metrics. The counter is periodically reset to zero to avoid long-term accumulated errors, and the peak loss rate is recorded before resetting for analysis.

[0091] Channel delay jitter measurement is based on a hardware timestamp mechanism. The transmitter embeds the precise transmission time in the physical layer frame header, and the receiver records the arrival time. A time synchronization protocol ensures that the clock deviation between the two ends is less than 1 microsecond. Delay calculation filters out propagation delays with fixed transmission paths, focusing on jitter component analysis. Jitter assessment uses a percentile statistical method, recording delay fluctuation values ​​at the 95th percentile. Measurement results are correlated with current modulation parameters to establish a correspondence between delay characteristics and system settings. Abnormal jitter detection triggers a link diagnostic mode, systematically investigating the sources of delay.

[0092] The closed-loop control of modulation parameters and link status employs a multi-loop feedback structure. The inner loop rapidly responds to changes in hardware layer metrics, such as optical power fluctuations or temperature increases, implementing millisecond-level parameter fine-tuning. The outer loop handles higher-level performance metrics, such as bit error rate and throughput, performing second-level strategy adjustments. An inter-loop coordinator manages the priority of control commands to prevent multi-loop adjustment conflicts. Control parameters are stored as multi-version snapshots, supporting rapid rollback to a known stable state. The closed-loop algorithm performs a self-tuning process upon system startup, optimizing control parameters based on actual response characteristics.

[0093] The waveform quality monitoring of the intelligent drive circuit employs oversampling analysis technology. A high-speed ADC samples the drive current waveform at a frequency 10 times the symbol rate, capturing detailed distortions. Analysis algorithms extract key characteristic parameters such as rise time, overshoot, and undershoot, comparing them with an ideal waveform template to generate a quality score. When the score falls below a threshold, a waveform optimization process is triggered, adjusting the compensation network parameters of the drive stage. The monitoring results are visualized as an eye diagram to assist engineers in diagnosing hardware problems. Long-term waveform trends are used to predict the aging state of the drive circuit.

[0094] The ambient light suppression algorithm for optical power detection employs spectral feature recognition. It analyzes the frequency domain characteristics of the detected signal using Fast Fourier Transform to distinguish between communication signals and ambient light components. An adaptive filter dynamically adjusts the notch filter frequency based on the recognition results, effectively suppressing periodic interference sources such as fluorescent lamps. In strong background light environments, the algorithm automatically increases the transmit power to compensate for signal-to-noise ratio loss, while simultaneously monitoring the safe operating range of the LED. The ambient light spectral feature database is continuously updated to learn the interference characteristics of new light sources.

[0095] The real-time link diagnostic function implements a layered testing strategy. Physical layer loopback testing verifies hardware path integrity, data link layer testing checks frame synchronization and error correction performance, and network layer testing evaluates end-to-end transmission quality. Test results generate diagnostic reports to pinpoint problematic modules. The diagnostic process is non-interrupted, conducted through dedicated test slots, without affecting normal communication. Historical diagnostic data establishes device health records, supporting fault prediction and maintenance planning.

[0096] The knowledge base management of modulation parameters employs a version control approach. Each optimized parameter configuration is saved as a new version, accompanied by environmental characteristics and performance metric tags. The retrieval system supports multi-condition combined queries, quickly locating historically optimal parameters for similar scenarios. A version difference analysis tool visually displays the parameter adjustment path, aiding in understanding the optimization process. The knowledge base periodically cleans up invalid entries, retaining representative configurations for typical scenarios. The distributed architecture allows for the sharing of parameter optimization experience among different nodes.

[0097] The link status visualization employs a multi-dimensional dashboard design. Indicators such as signal-to-noise ratio, packet loss rate, and latency jitter are displayed using circular progress bars, with colors ranging from green to red indicating deterioration. Trend charts show the changes in these indicators over the past minute, aiding in the assessment of system stability. The alarm panel summarizes current anomalies and sorts them by severity. Displayed content supports customized filtering, focusing on key performance parameters. The visualization system updates synchronously with the hardware status, with latency kept imperceptible to the human eye.

[0098] Please see Figures 6-7 A lighting fixture includes a lamp body 1, a base 2, and the aforementioned intelligent lighting data transmission system based on visible light communication. One end of the base 2 is mounted on a wall or other structure, and the other end of the base 2 is fitted with a connecting plate 4 via a snap-fit ​​structure 3. The connecting plate 4 is connected to the lamp body 1 via a first decorative plate 5. Replaceable second decorative plates 6 are also provided on both sides of the connecting plate 4. The first decorative plate 5 and the second decorative plate 6 have various styles and can be replaced according to actual needs.

[0099] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0100] 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 smart lighting data transmission system based on visible light communication, characterized in that, include: The optical signal processing module collects and processes visible light signals and outputs spatially encoded light intensity signals. The transmission management module receives the optical intensity spatially encoded signal. The transmission management module includes a signal analysis unit, a channel optimization unit, and a transmission strategy unit. The signal analysis unit decodes the optical intensity spatially encoded signal and outputs the original data stream signal and the channel interference characteristic signal. The channel optimization unit processes the optical intensity spatially encoded signal and the original data stream signal to obtain the optimal modulation parameter signal. The transmission strategy unit defines transmission constraints based on the optical intensity spatially encoded signal and outputs a dynamic bandwidth allocation signal. The terminal control module includes an adaptive modulation execution unit, a lamp array control unit, and a link monitoring module. The adaptive modulation execution unit adjusts the optical transmitter driving parameters according to the optimal modulation parameter signal. The lamp array control unit adjusts the working state of the lamp light-emitting unit according to the dynamic bandwidth allocation signal and the light intensity spatial coding signal, and collects real-time link status signals. The link monitoring module processes the channel interference characteristic signal and the raw data stream signal and outputs a communication quality index. The optical signal processing module includes a distributed photoelectric sensor array and a spatial light intensity analysis unit. The distributed photoelectric sensor array is deployed at key locations in the indoor space and collects ambient light intensity distribution data. The spatial light intensity analysis unit obtains a spatial light intensity distribution matrix through multi-angle light intensity sampling technology and forms a spatially encoded base signal. The optical signal processing module receives the ambient light intensity distribution data and the spatially encoded base signal and performs heterogeneous light intensity fusion to generate the light intensity spatially encoded signal. The light intensity spatially encoded signal includes an analytical encoded signal, an optimization encoded signal, and a strategy encoded signal. The signal analysis unit includes an optical signal decoding model based on time-domain feature extraction and an interference pattern recognition model. The coded signal used for analysis includes background light noise spectrum data, multipath reflection delay data, spatial light intensity gradient data, and historical transmission success rate data. The background light noise spectrum data, the multipath reflection delay data, the spatial light intensity gradient data, and the historical transmission success rate data are input into the optical signal decoding model and output as the original data stream signal. The improved interference pattern recognition model fuses the background light noise spectrum data, the multipath reflection delay data, and the spatial light intensity gradient data and outputs as the channel interference feature signal.

2. The intelligent lighting data transmission system based on visible light communication according to claim 1, characterized in that, The optical signal decoding model includes a time-series waveform reconstruction submodule, a spatial feature fusion submodule, an interference quantization submodule, and a signal restoration module. The time-series waveform reconstruction submodule performs waveform superposition analysis on the multipath reflection delay data and extracts the effective signal waveform that meets the communication frequency band requirements as the data carrier signal. The spatial feature fusion submodule uses an attention-weighted convolutional neural network to process the background light noise spectrum data, the multipath reflection delay data, and the spatial light intensity gradient data, and outputs the processing results to the interference quantization submodule. The interference quantization submodule defines an interference index algorithm, including calculating a weighted combination of the mean maximum noise intensity and the spatial light intensity change gradient. The signal restoration module generates a dynamic spatial distribution map of the interference index based on a three-dimensional heatmap, using different color depths to represent the interference index of different location regions.

3. The intelligent lighting data transmission system based on visible light communication according to claim 1, characterized in that, The channel optimization unit processes the coded signal for optimization and the original data stream signal through a modulation-interference nonlinear relationship model, outputs an initial modulation parameter signal, and uses a heuristic search algorithm to iteratively optimize the initial modulation parameter signal to obtain the optimal modulation parameter signal. The optimized coded signal includes receiver sensitivity parameters and light source characteristic parameters. The modulation-interference nonlinear relationship model is constructed using the receiver sensitivity parameters, the original data stream signal, and the light source characteristic parameters. It is described as a nonlinear inverse relationship between modulation depth and signal distortion.

4. The intelligent lighting data transmission system based on visible light communication according to claim 3, characterized in that, The channel optimization unit further includes a dynamic library of light source characteristics, a channel capacity analysis submodule, and a modulation parameter planning submodule. The dynamic library of light source characteristics stores the luminous efficiency characteristic curves of historical light source devices. The channel capacity analysis submodule calculates the maximum transmission capacity of the spatial channel based on the initial modulation parameter signal and the receiver position coordinates. The modulation parameter planning submodule generates the optimal modulation parameter signal through a heuristic search algorithm based on the maximum transmission capacity of the spatial channel, the modulation-interference nonlinear relationship model, and the light source characteristic parameters.

5. The intelligent lighting data transmission system based on visible light communication according to claim 1, characterized in that, The transmission strategy unit defines transmission constraints based on the strategy-encoded signal, outputs the dynamic bandwidth allocation signal, and adjusts the dynamic bandwidth allocation signal to ensure it is not lower than the minimum communication guarantee value using a dynamic programming algorithm. The strategy-encoded signal includes a real-time terminal position signal and a movement speed signal. The real-time terminal position signal and the movement speed signal define the transmission constraints, which are expressed as a mapping relationship between terminal movement trajectory prediction and available spatial bandwidth.

6. The intelligent lighting data transmission system based on visible light communication according to claim 1, characterized in that, The link monitoring module includes a space communication situation platform and a quality early warning engine. The space communication situation platform receives the light intensity spatial coding signal and the real-time link status signal and dynamically renders a visible light signal coverage intensity map. The quality early warning engine processes the channel interference characteristic signal and the original data stream signal according to an improved fuzzy evaluation algorithm and outputs the communication quality index. When the communication quality index is lower than the quality index threshold, the link monitoring module controls the adaptive modulation execution unit to perform emergency modulation enhancement. The quality warning engine includes a multi-level quality status color-coding mechanism, namely blue warning status, yellow warning status, and red warning status. When the bit error rate in the original data stream signal rises to the first bit error threshold, the blue warning status is activated, and the data transmission rate is reduced to the basic guaranteed rate. When the bit error rate in the original data stream signal rises to the second bit error threshold, the video stream data transmission is suspended and the adaptive modulation execution unit is controlled to perform signal enhancement. When the bit error rate in the original data stream signal rises to the third bit error threshold, the quality warning engine issues a communication interruption alarm and triggers the system communication protection mechanism.

7. The intelligent lighting data transmission system based on visible light communication according to claim 1, characterized in that, The optimal modulation parameter signal includes modulation mode type, pulse width range and duty cycle adjustment step size. The adaptive modulation execution unit includes intelligent driving circuit and integrated optical power detection device. The intelligent driving circuit analyzes the optimal modulation parameter signal and adjusts the driving current waveform as needed. The integrated optical power detection device receives the optimal modulation parameter signal and monitors the light flux of the light-emitting unit in real time based on the photoelectric conversion principle. The real-time link status signal includes receiver signal-to-noise ratio, data packet loss rate and channel delay jitter.

8. A lamp, characterized in that, The system includes a lamp body (1) and a base (2) as well as a smart lighting data transmission system based on visible light communication as described in any one of claims 1 to 7. The base (2) is equipped with a connecting plate (4) through a snap-fit ​​structure (3). The connecting plate (4) is connected to the lamp body (1) through a decorative plate (5). The connecting plate (4) is also provided with replaceable second decorative plates (6) on both sides.

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