Constant current light source driver power control method and device

Abstract

The present invention relates to a constant current light source driver power control method and device. The method includes obtaining the light source type, driving parameters, and working environment information of the constant current light source driver, integrating them, and obtaining corresponding basic driver control parameters; obtaining power supply data and temperature data of the constant current light source driver, correlating them with light source characteristics, and obtaining corresponding real-time light source driving parameters; dynamically analyzing the basic driver control parameters based on the real-time light source driving parameters to obtain corresponding power adjustment trends; identifying the light source working state based on the basic driver control parameters to obtain corresponding working mode information, predicting the working mode information based on the power adjustment trend to obtain a corresponding protection strategy; and comprehensively analyzing the initial power control plan and the protection strategy to obtain a corresponding global power control plan. The present invention can achieve global power optimization and control of the light source driver.

Description

Technical Field

[0001] The present invention relates to the technical field of constant current light source drivers, and in particular to a constant current light source driver power control method and device. Background Art

[0002] Constant-current light source drivers, as key components in modern optoelectronic systems, play a vital role in lighting, display, sensing, and other fields. With the rapid development of optoelectronic technology, increasingly stringent requirements are being placed on the power control accuracy and stability of light source drivers. Achieving precise and efficient power regulation of light source drivers has become a key research direction in optoelectronic system optimization. However, existing light source driver power control methods have numerous limitations. Traditional control methods typically consider only single-dimensional parameters, such as light source type or basic driving parameters, while ignoring the combined influence of multiple and complex factors, such as the light source's operating environment, power supply characteristics, and temperature variations. This simplified control model fails to fully capture the dynamic characteristics of light source drivers, potentially leading to inaccurate power control and insufficient system adaptability, which in turn impacts the overall performance and service life of optoelectronic devices. In particular, single-dimensional parameter control is more prone to problems such as power imbalance and improper thermal management in diverse operating environments and complex application scenarios, hindering the stability and reliability of light source drivers. Summary of the Invention

[0003] The main purpose of the present invention is to provide a constant current light source driver power control method and device, which can achieve global power optimization and regulation of the light source driver.

[0004] To achieve the above object, the present invention provides a constant current light source driver power control method, comprising:

[0005] Obtain the light source type, driving parameters and working environment information of the constant current light source driver, and integrate them to obtain the corresponding basic control parameters of the driver;

[0006] Acquire power supply data and temperature data of the constant current light source driver, associate them with light source characteristics, and obtain corresponding real-time light source driving parameters;

[0007] Dynamically analyzing the basic control parameters of the driver according to the real-time light source driving parameters to obtain corresponding power regulation trends;

[0008] Performing system control analysis on the power regulation trend to obtain a corresponding initial power control solution;

[0009] Identify the working state of the light source according to the basic control parameters of the driver to obtain corresponding working mode information, predict the working mode information based on the power regulation trend, and obtain a corresponding protection strategy;

[0010] The initial power control scheme and the protection strategy are comprehensively analyzed to obtain a corresponding global power regulation scheme.

[0011] Furthermore, the light source type, driving parameters and working environment information of the constant current light source driver are obtained and integrated to obtain the corresponding basic control parameters of the driver, including:

[0012] Acquiring light source emission spectrum data of the constant current light source driver, performing feature recognition analysis on the light source emission spectrum data, and obtaining the light source type;

[0013] Acquiring electrical parameters and optical parameters of the constant current light source driver, and performing feature analysis on the electrical parameters and the optical parameters based on the light source type to obtain the driving parameters;

[0014] Acquiring physical environment parameters and mechanical environment parameters of the constant current light source driver, performing multi-dimensional mapping processing on the physical environment parameters and the mechanical environment parameters to obtain the working environment information;

[0015] Performing constraint matching analysis on the driving parameters and the working environment information to obtain corresponding environmental adaptability evaluation results;

[0016] Correcting the driving parameters according to the environmental adaptability assessment result to obtain corresponding initial control parameters;

[0017] Performing entropy weight calculation on the initial control parameters to obtain a corresponding parameter weight configuration matrix;

[0018] Performing cluster optimization on the initial control parameters based on the parameter weight configuration matrix to obtain a corresponding parameter optimization set;

[0019] The initial control parameters are normalized according to the parameter optimization set to obtain the basic control parameters of the driver.

[0020] Furthermore, the acquiring of power supply data and temperature data of the constant current light source driver and correlating the data with light source characteristics to obtain corresponding real-time light source driving parameters includes:

[0021] Performing multi-point sampling on the voltage data of the constant current light source driver to obtain a corresponding power supply voltage signal;

[0022] Performing high-frequency real-time monitoring and fluctuation feature extraction on the current data of the constant-current light source driver to obtain a current change characteristic curve;

[0023] Constructing a power supply characteristic map based on the power supply voltage signal and current change characteristic curve to obtain the power supply data;

[0024] Performing temperature collection on the constant current light source driver to obtain the temperature data, and performing spatial distribution and time change trend analysis to obtain temperature dynamic change characteristics;

[0025] Obtaining a temperature-power coupling feature mapping table by mapping and associating the power supply data with the temperature dynamic change feature;

[0026] Performing a multi-dimensional correlation evaluation on the temperature-power coupling characteristic mapping table to obtain a light source characteristic correlation evaluation result;

[0027] A multi-dimensional parameter correlation calculation is performed based on the light source characteristic correlation evaluation result to obtain the real-time light source driving parameters.

[0028] Furthermore, the dynamic analysis of the basic control parameters of the driver according to the real-time light source driving parameters to obtain the corresponding power regulation trend includes:

[0029] Performing correlation decomposition and matrix construction on the real-time light source driving parameters to obtain a parameter decoupling mapping relationship;

[0030] Reconstructing the parameter decoupling mapping relationship and performing nonlinear encoding conversion to obtain a parameter feature space;

[0031] A dynamic logic network is constructed based on the parameter feature space, and a correlation analysis is performed on the basic control parameters of the driver to obtain a parameter correlation determination matrix;

[0032] Performing discretization and segmented prediction on the parameter association determination matrix to obtain a power variation discrete sequence;

[0033] Performing an inflection point sensitivity analysis on the power variation discrete sequence, identifying a critical transition point of power regulation, and obtaining a power regulation trend node;

[0034] Feature matching and association verification are performed on the key nodes of the power regulation trend to obtain the power regulation trend.

[0035] Furthermore, performing system control analysis on the power regulation trend to obtain a corresponding initial power control solution includes:

[0036] Calculating the characteristic gradient of the power regulation trend to obtain a power change slope sequence;

[0037] Performing discrete point interpolation processing on the power regulation trend based on the power change slope sequence to obtain a continuous power change curve;

[0038] Performing differential extraction on the continuous power variation curve to obtain comprehensive derivative features of first-order derivatives and second-order derivatives;

[0039] Performing a loss function calculation on the comprehensive derivative feature gradient according to a preset gradient descent method to obtain a sensitivity coefficient;

[0040] Performing threshold layering processing on the sensitivity coefficients to obtain a multi-dimensional power regulation control mapping relationship;

[0041] performing constraint optimization on the power regulation trend according to the regulation control mapping relationship to obtain the initial power control scheme;

[0042] Among them, the formulas in the loss function construction and calculation process include:

[0043] ;

[0044] represents the actual value, f(xi) represents the predicted value, sign() is the sign function, θ is the model parameter, α is the learning rate, and t is the iteration round.

[0045] Furthermore, the light source working state is identified based on the basic control parameters of the driver to obtain corresponding working mode information, and the working mode information is predicted based on the power regulation trend to obtain a corresponding protection strategy, including:

[0046] Performing multi-dimensional feature parameter extraction on the basic control parameters of the driver to obtain a feature vector of the light source working state;

[0047] Performing light source cluster analysis based on the light source working state feature vector to obtain the working mode information;

[0048] Establishing a light source working mode conversion probability matrix based on the working mode information, and performing probability distribution calculation to obtain a working mode conversion rule;

[0049] Probabilistically analyzing the power regulation trend according to the working mode conversion rule to obtain a corresponding risk assessment indicator;

[0050] Construct a multi-dimensional risk weight matrix based on the risk assessment indicators, and conduct graded warnings to obtain corresponding strategy classification information;

[0051] Comprehensively analyze the policy classification information to obtain the protection policy.

[0052] Furthermore, the light source working mode conversion probability matrix is ​​established based on the working mode information, and probability distribution calculation is performed to obtain the working mode conversion rule, including:

[0053] Segmentally sampling the working mode information to obtain a working mode time slice sequence;

[0054] Performing phase decomposition calculation according to the working mode time slice sequence to obtain a mode conversion phase spectrum;

[0055] Reconstructing the mode conversion phase spectrum to obtain a working mode conversion vector field;

[0056] A function is constructed based on the working mode conversion vector field and a preset Markov state transfer equation to obtain a state transfer probability function;

[0057] Performing Fourier transform on the state transition probability function to obtain a frequency domain characteristic spectrum;

[0058] Extract extreme points according to the frequency domain characteristic spectrum to obtain a working mode conversion topology structure;

[0059] A graph theory analysis is performed on the working mode conversion topology to obtain a working mode conversion rule.

[0060] Furthermore, the initial power control scheme and the protection strategy are comprehensively analyzed to obtain a corresponding global power control scheme, including:

[0061] Performing power feature extraction on the initial power control scheme to obtain a power feature vector;

[0062] Performing working data mapping on the protection strategy to obtain a working information mapping sequence;

[0063] Performing a multi-dimensional correlation analysis based on the power source characteristic vector and the working information mapping sequence to obtain power control related parameters;

[0064] Dynamically calculating the weight coefficients of the power control related parameters to obtain a comprehensive evaluation index of power control;

[0065] The initial power control scheme is iteratively optimized according to the comprehensive evaluation index of power control to obtain the global power control scheme.

[0066] The present invention further provides a constant current light source driver power control device, which is applied to any of the above constant current light source driver power control methods, comprising:

[0067] An acquisition module is used to obtain the light source type, driving parameters and working environment information of the constant current light source driver, and integrate them to obtain the corresponding basic control parameters of the driver;

[0068] An analysis module, configured to obtain power supply data and temperature data of the constant current light source driver, and associate the data with light source characteristics to obtain corresponding real-time light source driving parameters;

[0069] an association module, configured to dynamically analyze the basic control parameters of the driver according to the real-time light source driving parameters to obtain a corresponding power regulation trend;

[0070] a processing module, the processing module being configured to perform system control analysis on the power regulation trend to obtain a corresponding initial power control solution;

[0071] a control module configured to identify the working state of the light source according to the basic control parameters of the driver, obtain corresponding working mode information, predict the working mode information based on the power regulation trend, and obtain a corresponding protection strategy;

[0072] An execution module is used to comprehensively analyze the initial power control scheme and the protection strategy to obtain a corresponding global power regulation scheme.

[0073] The present invention provides a constant current light source driver power control method and device, which has the following beneficial effects:

[0074] By correlating system data with light source characteristic data and performing in-depth correlation analysis based on real-time environmental data, the power requirements of the light source driver under different operating conditions can be more accurately assessed, significantly improving the accuracy of power control and providing a more reliable technical foundation for the design and operation of optoelectronic systems. Dynamic mapping and correlation of power and temperature data enables refined management of the light source driver's operating status, helping to adjust the driver power on demand and avoid excessive energy consumption. Comprehensive analysis of real-time temperature and power characteristics ensures efficient and stable driver operation under varying ambient temperatures and power supply fluctuations, reducing unnecessary energy consumption and improving overall system efficiency. Systematic analysis of the driver's basic control parameters and real-time operating status enables the development of more optimized power control strategies. By identifying specific operating modes and predicting potential risks, targeted protection strategies can be developed, achieving global power optimization for the light source driver. This not only effectively improves the reliability and service life of the equipment, but also allows for flexible adjustment of power control strategies based on the characteristics and changing needs of different application scenarios, making the system more intelligent and adaptable. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 This is a flow chart of a constant current light source driver power control method provided by the present invention;

[0076] Figure 2 This is a structural diagram of a constant current light source driver power control device provided by the present invention.

[0077] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0078] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0079] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0080] Reference Figure 1 As shown, the present invention provides 1. A constant current light source driver power control method, characterized by comprising:

[0081] Step S1: Obtain the light source type, driving parameters and working environment information of the constant current light source driver, and integrate them to obtain the corresponding basic control parameters of the driver;

[0082] Step S2: obtaining power supply data and temperature data of the constant current light source driver, correlating them with light source characteristics, and obtaining corresponding real-time light source driving parameters;

[0083] Step S3: Dynamically analyzing the basic control parameters of the driver according to the real-time light source driving parameters to obtain the corresponding power adjustment trend;

[0084] Step S4: performing system control analysis on the power regulation trend to obtain a corresponding initial power control solution;

[0085] Step S5: Identify the working state of the light source according to the basic control parameters of the driver to obtain corresponding working mode information, predict the working mode information based on the power regulation trend, and obtain a corresponding protection strategy;

[0086] Step S6: Comprehensively analyze the initial power control scheme and the protection strategy to obtain a corresponding global power regulation scheme.

[0087] Based on the above steps, the detailed process is as follows:

[0088] Step S1: Determine the light source type by matching a preset database, reading built-in identifiers, or analyzing characteristic curves. Obtain key driver parameters, including rated output current, voltage range, dimming mode, and performance indicators such as efficiency and power factor. Measure and record working environment information such as ambient temperature, humidity, installation location, and power supply network characteristics. Comprehensively analyze and integrate this information to establish the association between light source type and driving parameters, taking the working environment information into consideration, and ultimately generate a comprehensive driver basic control parameter table and the corresponding driver basic control parameters;

[0089] Step S2: Monitor and record the driver's input voltage and current in real time, calculate actual power consumption, and monitor the temperature of the driver housing and key components, as well as changes in ambient temperature. Based on the acquired light source type, call the corresponding light source characteristic information, analyze the current-luminous flux relationship of the light source, and consider the impact of temperature on light source performance. Establish a correlation between power supply data, temperature data, and light source characteristics, calculate the actual output of the light source under current operating conditions, and evaluate the light source's operating efficiency and stability. Based on these analysis results, calculate the optimal drive current, determine the light source's real-time power requirements, and generate real-time drive parameters containing current, power, temperature, and other information.

[0090] Step S3: Perform a detailed comparison of the real-time light source driving parameters with the basic control parameters, calculate the deviation values ​​of each parameter, and evaluate the trend and magnitude of parameter changes. Identify and quantify the main factors affecting power changes, such as temperature and voltage fluctuations, and establish a multi-factor power impact model. Collect and analyze the driving parameter change data within a certain period of time, use statistical methods to analyze the parameter change patterns, and establish a parameter change trend prediction model. Based on these analysis results, evaluate whether the current power needs to be adjusted and determine the urgency and direction of the adjustment. Taking into account the real-time parameters, historical data, and adjustment requirements, use the prediction model to calculate the short-term power change trend and generate a power adjustment trend that includes the adjustment direction, magnitude, and time.

[0091] Step S4: Determine control objectives based on the light source type and application scenario, including light output stability requirements, power efficiency targets, and light source life and reliability requirements. Evaluate the driver's power regulation capability and range, consider power network constraints, and analyze the impact of environmental factors on control to determine control constraints. Based on the power regulation trend, select an appropriate control strategy (such as PID or fuzzy control), determine the initial values ​​of the control parameters, and design the response characteristics of the control algorithm. Simulate and analyze the power regulation trend to evaluate the effectiveness of different control strategies. Based on the simulation results, select the optimal control strategy, determine the specific power regulation values ​​and timing, and generate an initial power control plan that includes the control strategy, parameters, and expected results.

[0092] Step S5: Using the basic control parameters as a reference, analyze the current light source's current, voltage, power, and other parameters to determine whether the light source is operating normally. Based on the operating status identification results, determine the current operating mode (such as normal, overload, or undervoltage). Deviations from the standard values ​​for each parameter are recorded, and a report containing information on the operating mode and parameter deviations is generated. Using power regulation trend data, apply time series prediction algorithms (such as ARIMA and LSTM) to predict operating mode changes over a period of time and generate an operating mode prediction report. Based on the prediction results, analyze potential risks, assess their severity and probability of occurrence, and identify risk items that require priority. Design appropriate protection measures for high-risk items, formulate response plans for different risk levels, and design the trigger conditions and execution process for the protection strategy.

[0093] Step S6: Compare the requirements of the initial power control solution and protection strategy, identify possible conflicts, and evaluate the priority relationship between the two. Design solutions for the identified conflicts, adjust control parameters or protection thresholds to eliminate conflicts, and redesign parts of the control strategy if necessary. Integrate the various requirements of the power control and protection strategies, use a multi-objective optimization algorithm to find the optimal balance point, and consider the overall performance and reliability of the system. Design a dynamic switching mechanism for the power control and protection strategies, formulate adjustment strategies for different operating conditions, and design emergency response procedures for abnormal situations. Integrate the optimized power control strategy and protection measures, formulate detailed implementation steps and parameter settings, and generate a global solution document that includes control logic, parameter configuration, and expected results.

[0094] The present invention provides a constant-current light source driver power control method. By correlating system data with light source characteristic data and performing in-depth correlation analysis based on real-time environmental data, the method can more accurately assess the power requirements of the light source driver under different operating conditions, significantly improving the accuracy of power control and providing a more reliable technical foundation for the design and operation of optoelectronic systems. Dynamic mapping and correlation processing of power supply and temperature data enable refined management of the light source driver's operating status, helping to adjust the drive power on demand and avoid excessive energy consumption. Comprehensive analysis of real-time temperature and power supply characteristics ensures efficient and stable operation of the driver under varying ambient temperatures and power supply fluctuations, reducing unnecessary energy consumption and improving overall system efficiency. Systematic analysis of the driver's basic control parameters and real-time operating status enables the development of more reasonable power control strategies. By identifying specific operating modes and predicting potential risks, targeted protection strategies can be established, achieving global power optimization and control of the light source driver. This not only effectively improves the reliability and service life of the device, but also allows flexible adjustment of the power control strategy based on the characteristics and changing needs of different application scenarios, making the system more intelligent and adaptable.

[0095] In one embodiment, the light source type, driving parameters, and working environment information of the constant current light source driver are obtained and integrated to obtain the corresponding basic control parameters of the driver, including:

[0096] Acquire the constant current light source driver's emission spectrum data and perform feature recognition analysis on it to determine the light source type. Spectral data includes information such as the light source's wavelength distribution and peak wavelength. Feature recognition analysis compares it with a pre-set library of light source spectrum features to identify the specific light source type, such as LED or halogen lamp.

[0097] Obtain the electrical and optical parameters of the constant current light source driver and perform feature analysis on these parameters based on the light source type to obtain the driver parameters. Electrical parameters include operating voltage and current, while optical parameters include light intensity and color temperature. During feature analysis, key parameters are extracted based on the characteristics of different light source types and normalized to form a standardized set of driver parameters.

[0098] Obtain the physical and mechanical environmental parameters of the constant current light source driver and perform multi-dimensional mapping on these parameters to obtain operating environment information. Physical environmental parameters include temperature and humidity, while mechanical environmental parameters include vibration and shock. Multi-dimensional mapping converts these parameters into unified evaluation indicators for subsequent analysis.

[0099] A constraint matching analysis is performed between the drive parameters and the working environment information to obtain the corresponding environmental adaptability assessment results. During the constraint matching analysis process, a correlation model between the drive parameters and environmental factors is established to evaluate the adaptability of the current drive parameters in a given environment and generate quantitative assessment results.

[0100] Based on the environmental adaptability assessment results, the driving parameters are modified to obtain the corresponding initial control parameters. Based on the assessment results, the modification process adjusts the sensitive items in the driving parameters to improve their adaptability and stability in the current environment.

[0101] The entropy weight calculation method takes into account the information content and importance of each parameter, assigns a reasonable weight to each parameter, and forms a weight configuration matrix.

[0102] Based on the parameter weight configuration matrix, cluster optimization is performed on the initial control parameters to obtain the corresponding parameter optimization set. In the cluster optimization process, similar parameters are grouped together to form several parameter clusters, and the optimal parameter combination is selected from each cluster to form the parameter optimization set.

[0103] This embodiment can accurately determine the type of light source by performing feature recognition analysis on the light source emission spectrum data, providing an accurate basis for subsequent parameter adjustment. By performing feature analysis on electrical and optical parameters and combining them with the light source type, more accurate driving parameters can be obtained, thereby improving driving efficiency. Multi-dimensional mapping processes environmental parameters so that different types of environmental factors can be uniformly evaluated, thereby enhancing environmental adaptability. Constraint matching analysis realizes dynamic adaptation of driving parameters to the environment, thereby improving the stability and reliability of the system. Entropy weight calculation and clustering optimization ensure the scientific nature and effectiveness of parameter adjustment, and avoid the risks brought about by blind adjustment. Normalization processing ensures the compatibility of the final basic control parameters of the driver with the control system, thereby improving control accuracy. The entire process realizes comprehensive consideration from light source characteristic analysis to environmental adaptability assessment, and then to parameter optimization, significantly improving the power control effect and adaptability of the constant current light source driver.

[0104] In one embodiment, power supply data and temperature data of a constant current light source driver are obtained, and are correlated with light source characteristics to obtain corresponding real-time light source driving parameters, including:

[0105] During the power supply data acquisition phase, multi-point sampling technology is used to accurately measure the voltage of the constant current light source driver. Specifically, at least five different time points are selected, each with a 50-millisecond interval. The power supply voltage signal is acquired using a high-precision analog-to-digital converter, and the voltage change amplitude is recorded.

[0106] The current monitoring process uses high-frequency real-time monitoring technology, set at a monitoring frequency of 10kHz, to capture even the smallest current fluctuations. A digital signal processing algorithm extracts the current variation characteristic curve, focusing on the amplitude, frequency, and periodicity of the current fluctuations. When the current fluctuation amplitude exceeds 5% of the baseline value, an abnormality monitoring mechanism is triggered, recording detailed current change data.

[0107] Power supply characteristic mapping is constructed based on voltage signals and current variation curves using a multidimensional mapping model. This model comprehensively considers factors such as voltage amplitude, current fluctuation characteristics, and time dimensions to establish a power supply characteristic mapping relationship. The mapping model can identify the nonlinear characteristics of the power supply system and provide a data foundation for subsequent analysis.

[0108] Temperature data is collected using a multi-point temperature sensor array covering key locations of the constant current light source driver. The temperature sensors are spatially distributed across the driver chip, power module, and cooling system, with a sampling frequency of 1 Hz. Temperature data analysis uses a time series algorithm to extract temperature trends and characteristics, focusing on temperature gradients and rates of change. When the temperature exceeds a preset threshold or the rate of change is abnormal, the temperature anomaly monitoring process is initiated.

[0109] Temperature-power coupling feature mapping is achieved by establishing multidimensional correlations. This deeply correlates power supply data with dynamic temperature change characteristics. Using a feature fusion algorithm, a coupling feature mapping table is constructed, including metrics such as correlation coefficient and trend matching. This mapping table reveals how temperature changes affect power system performance.

[0110] The light source characteristic correlation assessment utilizes a multi-dimensional correlation analysis method, including statistical correlation analysis and information entropy calculation. The assessment considers multiple dimensions, including power supply data, temperature characteristics, and light source performance indicators, and uses a comprehensive scoring mechanism to derive the light source characteristic correlation assessment results. The assessment results quantify the impact of each factor on light source driving performance.

[0111] Real-time light source driver parameter calculation utilizes an adaptive parameter optimization algorithm based on the correlation evaluation results of light source characteristics. This algorithm dynamically adjusts the drive current, voltage, and power parameters based on these correlation evaluation results to ensure the light source maintains stability and efficiency under varying operating conditions. The parameter calculation process fully considers the system's dynamic characteristics and real-time requirements.

[0112] This embodiment achieves comprehensive and accurate capture of the power and temperature characteristics of the light source driving system through multi-point refined data collection and high-frequency real-time monitoring, significantly improving the accuracy and depth of system performance analysis. The innovative multi-dimensional correlation analysis technology can establish a deep mapping relationship between power data, temperature characteristics and light source performance, breaking through the limitations of traditional single-dimensional analysis. The adaptive parameter optimization algorithm enables the light source driver to adjust the driving parameters in real time and dynamically to ensure stable and efficient performance in complex and changing working environments. Compared with traditional methods, the present invention significantly improves the intelligence level of the constant current light source driver, realizes precise regulation and adaptive optimization of system performance, and has important technological innovation value.

[0113] In one embodiment, dynamic analysis of basic driver control parameters is performed based on real-time light source driving parameters to obtain corresponding power regulation trends, including:

[0114] Using multi-dimensional matrix construction technology, the drive parameters are decoupled and mapped from dimensions such as time, current, and voltage. Based on the decoupled mapping, a feature reconstruction algorithm is used to perform nonlinear encoding conversion on the parameters to generate a multi-dimensional parameter feature space that accurately reflects the complex correlations between the parameters.

[0115] The dynamic logic network is constructed based on the parameter feature space. By establishing a parameter correlation matrix, in-depth correlation analysis of the basic drive control parameters is performed. The correlation matrix incorporates weighting factors and a correlation scoring mechanism to quantify the degree of mutual influence between parameters. During the matrix construction process, strict threshold screening rules are implemented to ensure that only significantly correlated parameter correlation paths are retained, effectively reducing analysis complexity.

[0116] Power change prediction uses a discretized segmented prediction method to convert continuous power changes into discrete sequences. The prediction model incorporates a probability density function and a state transition matrix, and through statistical analysis of historical data, constructs a probabilistic prediction model for power changes. The generation of discrete sequences adheres to the minimum variance principle, minimizing prediction errors and improving prediction accuracy.

[0117] Inflection point sensitivity analysis is a key step in identifying power regulation trends. By performing differential and second-order derivative analysis on discrete series, critical transition points of power changes can be precisely located. Sensitivity analysis incorporates dynamic thresholds and rate-of-change assessment mechanisms to effectively distinguish between minor fluctuations and significant changes. Critical transition points are identified based on multiple metrics, including rate of change, amplitude, and duration.

[0118] The final determination of the power regulation trend is based on feature matching and correlation verification of key nodes. This verification process utilizes multimodal feature fusion technology, comprehensively considering electrical, thermodynamic, and temporal characteristics. The feature matching algorithm constructs a similarity measurement model to assess the strength and consistency of correlations between nodes. Correlation verification incorporates a confidence scoring mechanism, ensuring that only significant and stable correlations between nodes are included in the final power regulation trend.

[0119] This embodiment significantly improves the accuracy and depth of driver parameter analysis through multi-dimensional parameter decoupling and feature reconstruction technology, enabling power regulation to be based on a more complex and comprehensive parameter correlation model. The dynamic logic network construction method breaks through the limitations of traditional single-dimensional parameter analysis and introduces a multi-dimensional correlation evaluation mechanism, which greatly enhances the system's sensitivity and response speed to parameter changes. Discrete segmented prediction and inflection point sensitivity analysis technology make power regulation trend prediction more accurate and reliable. By introducing probability density functions and state transition matrices, the key transition points of power changes can be effectively identified, reducing prediction errors. The correlation verification method of multimodal feature fusion ensures the stability and consistency of the power regulation trend and improves the adaptability and robustness of the driver.

[0120] In one embodiment, a system control analysis is performed on the power regulation trend to obtain a corresponding initial power control solution, including:

[0121] Systematic analysis and control of power regulation trends are performed. By calculating the gradient of power changes, researchers select time series data points, calculate the rate of change between adjacent data points, and construct a power change slope sequence. This slope calculation uses a differential method: the difference between the power values ​​of two adjacent points is divided by the time interval, thereby obtaining the dynamic characteristics of power changes.

[0122] Spline interpolation is used to create a continuous curve fit for discrete power slope sequences. Cubic spline interpolation is chosen as the interpolation algorithm to ensure smoothness and continuity of the curve, maintaining the continuity of the first-order derivative at the interpolation points. This interpolation process transforms the originally discrete power variation data into a continuous variation curve.

[0123] Perform differential calculations on the continuous power change curve to extract the first- and second-order derivatives. The first-order derivative reflects the rate of power change, while the second-order derivative reveals the trend of the power change rate. These two derivatives are combined in a weighted manner to construct a composite derivative feature. The weight coefficient is dynamically adjusted based on the actual application scenario and system characteristics.

[0124] Based on the gradient descent method, a loss function is constructed for the gradient of the integrated derivative feature. This loss function design considers the accuracy and stability of power control, including multi-dimensional indicators such as power deviation, regulation speed, and energy consumption. Through iterative optimization, a sensitivity coefficient is calculated to reflect the system's sensitivity. This coefficient quantifies the impact of power regulation on system performance.

[0125] The sensitivity coefficients are layered using thresholds to establish a multi-dimensional power regulation control mapping. The thresholds are divided into different levels based on the system's dynamic response characteristics and control accuracy requirements, with each level corresponding to a different power regulation strategy. This layered approach makes power control more adaptable, enabling dynamic adjustment of control strategies based on real-time system status.

[0126] Based on the constructed control mapping, the power regulation trend is constrained and optimized to generate an initial power control solution. The optimization process incorporates constraints such as upper and lower power limits, rate of change constraints, and energy efficiency requirements, and the optimal control solution is solved using a multi-objective optimization algorithm.

[0127] Among them, the formulas in the loss function construction and calculation process include:

[0128] ;

[0129] represents the actual value, f(xi) represents the predicted value, sign() is the sign function, θ is the model parameter, α is the learning rate, and t is the iteration round.

[0130] This embodiment achieves accurate description and continuous simulation of power change trends through gradient calculation and spline interpolation, significantly improving the dynamic response accuracy of power regulation. The use of comprehensive derivative feature extraction technology can fully capture the rate and acceleration characteristics of power changes, providing more in-depth system insights for power control. By constructing the loss function and calculating the sensitivity coefficient through the gradient descent method, this method establishes a power regulation evaluation mechanism based on multi-dimensional indicators, which can accurately quantify system performance and regulation effects. The threshold layered processing technology makes the power control strategy more adaptable, and the control parameters can be dynamically adjusted according to the real-time system status, effectively improving the robustness and adaptability of the driver. The multi-objective optimization algorithm is introduced through the constrained optimization method to generate the optimal power control solution while meeting the constraints such as power upper and lower limits, change rate and energy efficiency.

[0131] In one embodiment, the light source operating state is identified based on the basic control parameters of the driver to obtain corresponding operating mode information. The operating mode information is predicted based on the power regulation trend to obtain a corresponding protection strategy, including:

[0132] By extracting multi-dimensional characteristic parameters from the basic driver control parameters, a characteristic vector of the light source's operating state is constructed. This feature extraction process involves collecting data from dimensions such as current amplitude, voltage fluctuation, temperature variation, and time series. This data is then standardized to ensure that all characteristic parameters are compared and analyzed on the same scale.

[0133] After constructing the feature vectors, the K-means clustering algorithm was used to classify the light source operating states. Cluster analysis, based on the Euclidean distance metric, clusters similar operating states into distinct operating mode clusters. Each cluster represents a typical light source operating mode, such as stable operating mode, overload mode, or abnormal fluctuation mode. The clustering results serve as the basis for subsequent analysis.

[0134] During the operating mode transition analysis phase, a Markov transition probability matrix is ​​constructed. This matrix records the transition probabilities between different operating modes and is calculated by counting the frequency of mode transitions in historical data. For example, the probability of transitioning from stable mode to overload mode, or the probability of transitioning from abnormal fluctuation mode to stable mode, can be found. This probability matrix can reflect the dynamic changes in the light source's operating state.

[0135] Power regulation trend probabilistic analysis uses a transition probability matrix, combined with the current operating mode and historical transition patterns, to predict possible future power trends. By calculating the probabilities of various transition paths, the risk level in different power regulation scenarios is quantified. Risk assessment metrics include multi-dimensional quantitative indicators such as transition probability, mode stability index, and extreme value deviation.

[0136] The risk weight matrix is ​​constructed using the Analytic Hierarchy Process (AHP), assigning weights to each risk indicator based on its importance. The matrix categorizes risk levels into low, medium, and high, each corresponding to different warning thresholds and response strategies. When a high risk level is detected, the system triggers appropriate protection mechanisms, such as reducing power, adjusting operating parameters, or initiating emergency mode.

[0137] Protection strategies comprehensively consider risk levels, operating mode characteristics, and historical transition patterns. These strategies adhere to the principle of risk minimization, minimizing potential failure risks while ensuring the normal operation of the light source. These strategies include adaptive parameter adjustment, dynamic power limiting, and preventive maintenance recommendations, ensuring the safe and reliable operation of constant current light source drivers.

[0138] This embodiment achieves accurate identification of the working status of the light source through multi-dimensional feature parameter extraction and cluster analysis, significantly improving the driver's adaptability and intelligent perception capabilities to complex working environments. The construction of the Markov transition probability matrix enables the system to dynamically capture the rules of working mode conversion, effectively predict potential risks, and achieve proactive risk management. Through hierarchical risk assessment and graded early warning mechanisms, it is possible to respond to abnormal working conditions in a timely manner, reduce the probability of equipment failure, and extend the service life of the light source. The innovative design of the multi-dimensional risk weight matrix makes the protection strategy more accurate and personalized, which not only ensures the safe operation of the equipment, but also achieves energy consumption optimization and performance adjustment, reflecting the advancement and practicality of intelligent control.

[0139] In one embodiment, a light source operating mode conversion probability matrix is ​​established based on the operating mode information, and a probability distribution calculation is performed to obtain an operating mode conversion rule, including:

[0140] The operating mode information is precisely sampled in segments. The sampling process adheres to the Nyquist sampling theorem, ensuring that the sampling frequency is no less than twice the highest signal frequency. A high-precision analog-to-digital converter discretizes the continuous operating mode information into a sequence of time slices. Each time slice represents the operating mode state within a specific time window, and the slice length is dynamically adjusted based on the system's dynamic response characteristics and the frequency of signal changes.

[0141] Phase decomposition is performed on the obtained time-slice sequence of operating modes. Using the Hilbert-Huang transform method, the complex signal is decomposed into several intrinsic mode functions through empirical mode decomposition. Each mode function is then Hilbert transformed to extract instantaneous frequency and amplitude information. An adaptive threshold mechanism is introduced during the phase decomposition process to effectively suppress noise interference and extract key phase characteristics of mode transitions.

[0142] The mode transition phase spectrum is reconstructed and transformed to construct the operating mode transition vector field. This vector field is based on differential geometry theory, mapping discrete mode transition points to a continuous manifold space. Gradient descent and manifold learning algorithms are used to capture the topological structure and flow characteristics of the mode transition. The vector field characterizes the directionality and energy transfer patterns of inter-mode transitions, laying the foundation for subsequent analysis.

[0143] Based on the operating mode transition vector field and the preset Markov state transition equation, a state transition probability function is constructed. The Markov state transition equation describes the probabilistic dependence of the system's transition from one operating mode to another. The probability function incorporates entropy weights and information gain factors, as well as the uncertainty and information complexity of mode transitions.

[0144] Applying a Fourier transform to the state transition probability function yields a frequency-domain characteristic spectrum. The Fourier transform converts the time-domain signal into a frequency-domain representation, revealing the frequency characteristics and periodicity of mode transitions. Power spectral density analysis identifies dominant frequency components and spectral peaks, reflecting the system's dynamic characteristics.

[0145] We extract extreme points from the frequency domain characteristic spectrum and construct a topological structure for the operating mode transitions. This extreme point extraction utilizes wavelet transforms and multiscale analysis to identify key turning points and characteristic nodes in the characteristic spectrum. This topological structure reflects the connectivity and correlation of mode transitions, revealing the inherent transition mechanisms of the system.

[0146] Finally, a graph-theoretic analysis of the operating mode transition topology was performed to extract the operating mode transition patterns. Based on complex network theory, this graph-theoretic analysis calculated node centrality, connectivity, and modularity. Through feature extraction and pattern recognition, a probability matrix for operating mode transitions was established, providing a theoretical basis for power control of constant-current light source drivers.

[0147] This embodiment significantly improves the accuracy and dynamic response capability of the power control of the constant current light source driver through precise segmented sampling and phase decomposition of the working mode information. The Hilbert-Huang transform and adaptive threshold mechanism are used to effectively suppress the system noise interference and improve the accuracy of the mode conversion feature extraction. The working mode conversion vector field constructed based on the differential geometry theory realizes the high-dimensional mapping and dynamic modeling of the complex conversion process of the system, and reveals the topological structure and energy transfer law of the inter-mode conversion. The Markov state transition probability function and entropy weight factor are introduced to quantify the uncertainty and information complexity of the system conversion. Through frequency domain characteristic spectrum analysis and graph theory methods, the present invention establishes a probability matrix for working mode conversion, which provides a theoretical basis for the power control of the constant current light source driver. Compared with traditional methods, this method can more accurately capture the dynamic characteristics of the system, improve the intelligence and adaptability of power control, and significantly optimize the performance and energy efficiency of the light source driver.

[0148] In one embodiment, the initial power control scheme and the protection strategy are comprehensively analyzed to obtain a corresponding global power control scheme, including:

[0149] During the power feature extraction phase, multi-dimensional signal decomposition technology is used to perform spectrum analysis and waveform feature extraction on the power input signal, constructing a power feature vector that includes indicators such as voltage fluctuation, current stability, and harmonic content. The feature vector construction adheres to IEEE standards to ensure data accuracy and representativeness.

[0150] In the operational data mapping phase, a mapping model for the light source driver's operating status is established, standardizing key operating parameters such as temperature, current, and load to generate a unique and comparable operational information mapping sequence. Normalization is incorporated into the mapping process to eliminate dimensional differences between parameters and ensure fairness in subsequent correlation analysis. The mapping rules are based on the light source driver's operating characteristic curve, encompassing data mapping strategies for the normal operating range, transition range, and extreme range.

[0151] Multidimensional correlation analysis, based on association algorithms in machine learning, deeply explores the potential correlations between power feature vectors and operating information mapping sequences. This analysis method utilizes techniques such as the Pearson correlation coefficient, mutual information theory, and principal component analysis to construct a multidimensional correlation evaluation model. By calculating the strength and direction of correlations between different feature dimensions, it extracts key parameters that significantly influence power regulation, forming a set of power regulation correlation parameters.

[0152] The dynamic calculation of the weight coefficients of power control parameters utilizes an adaptive weighting algorithm, dynamically adjusting the importance of each parameter based on the real-time operating environment and historical operating data. This weighting calculation incorporates entropy weighting and fuzzy evaluation theory, comprehensively considering the discreteness, sensitivity, and stability of the parameters. By constructing a multi-criteria decision-making model, a comprehensive power control evaluation index is generated that fully reflects the complexity of the system.

[0153] Based on comprehensive evaluation metrics, the initial power control scheme is iteratively optimized. The optimization algorithm utilizes a hybrid strategy combining an improved particle swarm optimization algorithm and a simulated annealing algorithm to precisely adjust the power control scheme while ensuring system stability. Multiple constraints, including maximum power deviation, energy conversion efficiency, and temperature rise control, are incorporated into the iterative process to ensure that the optimized solution meets the performance requirements of the constant current light source driver. The resulting global power control scheme is adaptive and intelligent, significantly improving the driver's operational reliability and energy efficiency.

[0154] This embodiment establishes a precise power control model through multi-dimensional power feature extraction and working data mapping, which can realize comprehensive perception and intelligent analysis of the operating status of the constant current light source driver. This method breaks through the static limitations of traditional power control, introduces a dynamic and adaptive control mechanism, and significantly improves the working adaptability and stability of the driver. The innovative multi-dimensional correlation analysis technology effectively explores the deep correlation between power supply characteristics and working parameters, and realizes accurate evaluation and intelligent optimization of the power control process through precise dynamic calculation of weight coefficients. Compared with traditional methods, this method can more accurately predict and adjust the power output of the driver, reduce system energy consumption, and improve energy conversion efficiency. In addition, the global power control scheme based on iterative optimization not only ensures the stability of the system, but also has a high degree of intelligent features. It can autonomously adjust the power control strategy according to the real-time working environment, effectively extending the service life of the constant current light source driver and improving the reliability and performance of the equipment.

[0155] Reference Figure 2 As shown, the present invention provides a constant current light source driver power control device, which is applied to any of the above constant current light source driver power control methods, including:

[0156] The acquisition module is used to obtain the light source type, driving parameters and working environment information of the constant current light source driver, and integrate them to obtain the corresponding basic control parameters of the driver;

[0157] An analysis module is used to obtain power supply data and temperature data of the constant current light source driver, and associate them with the light source characteristics to obtain corresponding real-time light source driving parameters;

[0158] The correlation module is used to dynamically analyze the basic control parameters of the driver according to the real-time light source driving parameters to obtain the corresponding power adjustment trend;

[0159] A processing module is used to perform system control analysis on the power regulation trend and obtain a corresponding initial power control plan;

[0160] The control module is used to identify the working state of the light source according to the basic control parameters of the driver, obtain the corresponding working mode information, predict the working mode information based on the power regulation trend, and obtain the corresponding protection strategy;

[0161] The execution module is used to comprehensively analyze the initial power control plan and the protection strategy to obtain the corresponding global power regulation plan.

[0162] The present invention provides a constant-current light source driver power control device. By correlating system data with light source characteristic data and performing in-depth correlation analysis based on real-time environmental data, it can more accurately assess the power requirements of the light source driver under different operating conditions, significantly improving the accuracy of power control and providing a more reliable technical foundation for the design and operation of optoelectronic systems. Dynamic mapping and correlation processing of power supply and temperature data enable refined management of the light source driver's operating status, helping to adjust the drive power on demand and avoid excessive energy consumption. Comprehensive analysis of real-time temperature and power supply characteristics ensures efficient and stable operation of the driver under varying ambient temperatures and power supply fluctuations, reducing unnecessary energy consumption and improving overall system efficiency. Systematic analysis of the driver's basic control parameters and real-time operating status enables the development of more reasonable power control strategies. By identifying specific operating modes and predicting potential risks, targeted protection strategies can be established, achieving global power optimization and control of the light source driver. This not only effectively improves the reliability and service life of the device, but also allows flexible adjustment of the power control strategy based on the characteristics and changing needs of different application scenarios, making the system more intelligent and adaptable.

[0163] It should be noted that, those skilled in the art will clearly understand that, for the sake of convenience and brevity of description, the specific working processes of the above-described system and each module can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0164] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A constant current light source driver power control method, characterized in that: include: Obtain the light source type, driving parameters and working environment information of the constant current light source driver, and integrate them to obtain the corresponding basic control parameters of the driver; Acquire power supply data and temperature data of the constant current light source driver, associate them with light source characteristics, and obtain corresponding real-time light source driving parameters; Dynamically analyzing the basic control parameters of the driver according to the real-time light source driving parameters to obtain corresponding power regulation trends; Performing system control analysis on the power regulation trend to obtain a corresponding initial power control solution; Identify the working state of the light source according to the basic control parameters of the driver to obtain corresponding working mode information, predict the working mode information based on the power regulation trend, and obtain a corresponding protection strategy; Comprehensively analyzing the initial power control scheme and the protection strategy to obtain a corresponding global power regulation scheme; The light source type, driving parameters and working environment information of the constant current light source driver are obtained and integrated to obtain the corresponding basic control parameters of the driver, including: Acquiring light source emission spectrum data of the constant current light source driver, performing feature recognition analysis on the light source emission spectrum data, and obtaining the light source type; Acquiring electrical parameters and optical parameters of the constant current light source driver, and performing feature analysis on the electrical parameters and the optical parameters based on the light source type to obtain the driving parameters; Acquiring physical environment parameters and mechanical environment parameters of the constant current light source driver, performing multi-dimensional mapping processing on the physical environment parameters and the mechanical environment parameters to obtain the working environment information; Performing constraint matching analysis on the driving parameters and the working environment information to obtain corresponding environmental adaptability evaluation results; Correcting the driving parameters according to the environmental adaptability assessment result to obtain corresponding initial control parameters; Performing entropy weight calculation on the initial control parameters to obtain a corresponding parameter weight configuration matrix; Performing cluster optimization on the initial control parameters based on the parameter weight configuration matrix to obtain a corresponding parameter optimization set; The initial control parameters are normalized according to the parameter optimization set to obtain the basic control parameters of the driver.

2. The constant current light source driver power control method according to claim 1, characterized in that: The acquiring of the power supply data and temperature data of the constant current light source driver and the correlating processing with the light source characteristics to obtain corresponding real-time light source driving parameters includes: Performing multi-point sampling on the voltage data of the constant current light source driver to obtain a corresponding power supply voltage signal; Performing high-frequency real-time monitoring and fluctuation feature extraction on the current data of the constant-current light source driver to obtain a current change characteristic curve; Constructing a power supply characteristic map based on the power supply voltage signal and current change characteristic curve to obtain the power supply data; Performing temperature collection on the constant current light source driver to obtain the temperature data, and performing spatial distribution and time change trend analysis to obtain temperature dynamic change characteristics; Obtaining a temperature-power coupling feature mapping table by mapping and associating the power supply data with the temperature dynamic change feature; Performing a multi-dimensional correlation evaluation on the temperature-power coupling characteristic mapping table to obtain a light source characteristic correlation evaluation result; A multi-dimensional parameter correlation calculation is performed based on the light source characteristic correlation evaluation result to obtain the real-time light source driving parameters.

3. The constant current light source driver power control method according to claim 1, characterized in that: The dynamically analyzing the basic control parameters of the driver according to the real-time light source driving parameters to obtain the corresponding power adjustment trend includes: Performing correlation decomposition and matrix construction on the real-time light source driving parameters to obtain a parameter decoupling mapping relationship; Reconstructing the parameter decoupling mapping relationship and performing nonlinear encoding conversion to obtain a parameter feature space; A dynamic logic network is constructed based on the parameter feature space, and a correlation analysis is performed on the basic control parameters of the driver to obtain a parameter correlation determination matrix; Performing discretization and segmented prediction on the parameter association determination matrix to obtain a power variation discrete sequence; Performing an inflection point sensitivity analysis on the power variation discrete sequence, identifying critical transition points of power regulation, and obtaining key nodes of power regulation trends; Feature matching and association verification are performed on the key nodes of the power regulation trend to obtain the power regulation trend.

4. The constant current light source driver power control method according to claim 1, characterized in that: The performing system control analysis on the power regulation trend to obtain a corresponding initial power control solution includes: Calculating the characteristic gradient of the power regulation trend to obtain a power change slope sequence; Performing discrete point interpolation processing on the power regulation trend based on the power change slope sequence to obtain a continuous power change curve; Performing differential extraction on the continuous power variation curve to obtain comprehensive derivative features of first-order derivatives and second-order derivatives; Performing a loss function calculation on the comprehensive derivative feature gradient according to a preset gradient descent method to obtain a sensitivity coefficient; Performing threshold layering processing on the sensitivity coefficients to obtain a multi-dimensional power regulation control mapping relationship; performing constraint optimization on the power regulation trend according to the regulation control mapping relationship to obtain the initial power control scheme; Among them, the formulas in the loss function construction and calculation process include: ； represents the actual value, f(xi) represents the predicted value, sign() is the sign function, θ is the model parameter, α is the learning rate, and t is the iteration round.

5. The constant current light source driver power control method according to claim 1, characterized in that: The identifying the light source working state according to the basic control parameters of the driver to obtain corresponding working mode information, and predicting the working mode information based on the power regulation trend to obtain a corresponding protection strategy includes: Performing multi-dimensional feature parameter extraction on the basic control parameters of the driver to obtain a feature vector of the light source working state; Performing light source cluster analysis based on the light source working state feature vector to obtain the working mode information; Establishing a light source working mode conversion probability matrix based on the working mode information, and performing probability distribution calculation to obtain a working mode conversion rule; Probabilistically analyzing the power regulation trend according to the working mode conversion rule to obtain a corresponding risk assessment indicator; Construct a multi-dimensional risk weight matrix based on the risk assessment indicators, and conduct graded warnings to obtain corresponding strategy classification information; Comprehensively analyze the policy classification information to obtain the protection policy.

6. The constant current light source driver power control method according to claim 5, characterized in that: The step of establishing a light source working mode conversion probability matrix based on the working mode information and performing probability distribution calculation to obtain a working mode conversion rule includes: Segmentally sampling the working mode information to obtain a working mode time slice sequence; Performing phase decomposition calculation according to the working mode time slice sequence to obtain a mode conversion phase spectrum; Reconstructing the mode conversion phase spectrum to obtain a working mode conversion vector field; A function is constructed based on the working mode conversion vector field and a preset Markov state transfer equation to obtain a state transfer probability function; Performing Fourier transform on the state transition probability function to obtain a frequency domain characteristic spectrum; Extract extreme points according to the frequency domain characteristic spectrum to obtain a working mode conversion topology structure; A graph theory analysis is performed on the working mode conversion topology to obtain a working mode conversion rule.

7. The constant current light source driver power control method according to claim 1, characterized in that: The comprehensive analysis of the initial power control scheme and the protection strategy to obtain a corresponding global power regulation scheme includes: Performing power feature extraction on the initial power control scheme to obtain a power feature vector; Performing working data mapping on the protection strategy to obtain a working information mapping sequence; Performing a multi-dimensional correlation analysis based on the power source characteristic vector and the working information mapping sequence to obtain power control related parameters; Dynamically calculating the weight coefficients of the power control related parameters to obtain a comprehensive evaluation index of power control; The initial power control scheme is iteratively optimized according to the comprehensive evaluation index of power control to obtain the global power control scheme.

8. A constant current light source driver power control device, characterized in that: The constant current light source driver power control method applied to any one of claims 1 to 7 above comprises: An acquisition module is used to obtain the light source type, driving parameters and working environment information of the constant current light source driver, and integrate them to obtain the corresponding basic control parameters of the driver; An analysis module, configured to obtain power supply data and temperature data of the constant current light source driver, and associate the data with light source characteristics to obtain corresponding real-time light source driving parameters; an association module, configured to dynamically analyze the basic control parameters of the driver according to the real-time light source driving parameters to obtain a corresponding power regulation trend; a processing module, the processing module being configured to perform system control analysis on the power regulation trend to obtain a corresponding initial power control solution; a control module configured to identify the working state of the light source according to the basic control parameters of the driver, obtain corresponding working mode information, predict the working mode information based on the power regulation trend, and obtain a corresponding protection strategy; An execution module, configured to comprehensively analyze the initial power control scheme and the protection strategy to obtain a corresponding global power regulation scheme; The light source type, driving parameters and working environment information of the constant current light source driver are obtained and integrated to obtain the corresponding basic control parameters of the driver, including: Acquiring light source emission spectrum data of the constant current light source driver, performing feature recognition analysis on the light source emission spectrum data, and obtaining the light source type; Acquiring electrical parameters and optical parameters of the constant current light source driver, and performing feature analysis on the electrical parameters and the optical parameters based on the light source type to obtain the driving parameters; Acquiring physical environment parameters and mechanical environment parameters of the constant current light source driver, performing multi-dimensional mapping processing on the physical environment parameters and the mechanical environment parameters to obtain the working environment information; Performing constraint matching analysis on the driving parameters and the working environment information to obtain corresponding environmental adaptability evaluation results; Correcting the driving parameters according to the environmental adaptability assessment result to obtain corresponding initial control parameters; Performing entropy weight calculation on the initial control parameters to obtain a corresponding parameter weight configuration matrix; Performing cluster optimization on the initial control parameters based on the parameter weight configuration matrix to obtain a corresponding parameter optimization set; The initial control parameters are normalized according to the parameter optimization set to obtain the basic control parameters of the driver.

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