Integration method of integrated light source device

Abstract

The invention provides an integration method of an integrated light source device, and the method comprises the steps: obtaining the circuit layout data of the integrated light source device and the power demand parameters of a plurality of light-emitting units, extracting the expected current value and spatial position coordinates of each light-emitting unit through a preset tool, and constructing an initial current distribution model; according to the initial current distribution model, an optimization algorithm is adopted to carry out iterative calculation on power requirements, and a potential current distribution scheme is determined; obtaining inter-channel interference data from the potential current distribution scheme, reducing interference influence by adjusting path configuration, and obtaining an optimized channel configuration model; for the optimized channel configuration model, obtaining heat distribution data, adjusting a current path of a high-heat area through a global search algorithm, and determining a current equalization scheme with a low overheating risk; and extracting power supply parameters from the current balancing scheme, and adjusting deviation through feedback circulation to obtain a corrected power supply network.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, and in particular to an integrated integration method of an integrated light source device. BACKGROUND

[0002] In the field of lighting devices, it is of great significance to study how to improve the integration and reliability of light source products. With the development of lighting technology, users have increasingly high demands for installation convenience, product stability and space aesthetics. However, traditional lighting device designs often fail to meet these requirements.

[0003] Therefore, exploring a brand-new light source structure design can not only promote the progress of the industry, but also bring users a better user experience. In the prior art, lighting devices often adopt a split architecture, i.e., the power supply, controller and light source module are independent of each other and need to be connected through external wiring. Although this approach can meet basic functions in the early stage, its limitations gradually emerge as the use scenarios become more complex. In particular, during installation and maintenance, the scattered layout of multiple components leads to cumbersome operations, and the frequent use of external connection points also increases the risk of failure. Such a design often fails to guarantee the overall stability of the system when facing high-frequency vibration or long-term use environments. Further, integrated design becomes the direction to solve the above problems, but there are key technical difficulties behind it. When integrating the power supply, control and light-emitting unit into a single housing, how to achieve accurate current distribution in a limited space becomes the primary challenge. Since different light-emitting units have different power requirements, if the balanced supply of current for each path cannot be ensured, some light-emitting units will be overloaded or the light output will be uneven. In addition, in a compact circuit board layout, the mutual interference between current channels may also affect the overall performance, such as overheating or signal distortion caused by improper design of the wiring on the circuit board during high-current transmission.

[0004] Therefore, how to design a circuit structure in the limited space of an integrated light source device that can accurately distribute current while avoiding interference and overheating problems between channels has become a key problem that needs to be solved in this research. This problem is directly related to the light-emitting effect and service life of the light source device, especially in multi-unit light-emitting scenarios, such as lighting equipment that needs to drive multiple light-emitting units of different power levels simultaneously. If a unit fails prematurely due to unbalanced current distribution, it will directly affect the overall lighting effect and user experience. Through in-depth analysis of this problem, it can be clearly seen that from the traditional split design to the integrated integration, the challenges of current distribution and space layout gradually emerge and become the core contradiction, which urgently needs innovative ideas to solve. SUMMARY

[0005] The present application provides an integrated integration method of an integrated light source device, mainly comprising:

[0006] The circuit layout data of the integrated light source device and the power demand parameters of the multiple light emitting units are acquired, the expected current value and the spatial position coordinates of each light emitting unit are extracted by a preset tool, and an initial current distribution model is constructed; according to the initial current distribution model, an optimization algorithm is used to iteratively calculate the power demand, and a potential current distribution scheme is determined; the inter-channel interference data is obtained from the potential current distribution scheme, the interference influence is reduced by adjusting the path configuration, and an optimized channel configuration model is obtained; for the optimized channel configuration model, the heat distribution data is acquired, the current path of the high heat area is adjusted by a global search algorithm, and a low-overheating-risk current equalization scheme is determined; the power supply parameters are extracted from the current equalization scheme, the deviation is adjusted through a feedback loop, and a corrected power supply network is obtained; according to the corrected power supply network, the light emitting effect index is acquired, the stability is verified through the matching degree with the target model, the network parameters are iteratively updated, the final circuit structure description is generated, and the multi-unit driving control is realized by integrating into the device design. Further, the circuit layout data of the integrated light source device and the power demand parameters of the multiple light emitting units are acquired, including: the expected current value and the spatial position coordinates of each light emitting unit are extracted from the circuit layout data by a preset simulation tool, and an initial current distribution model is constructed; the heat distribution simulation between the light emitting units is analyzed from the initial current distribution model, the heat accumulation value of each light emitting unit is mapped by a temperature sensor data, and the heat distribution characteristics are determined; for the heat distribution characteristics, the expected current value is adjusted in combination with the power demand parameters, the interference influence between the units is calculated by a finite element analysis method, a grid model is constructed and the interference equation is solved, and an interference correction coefficient is obtained; according to the interference correction coefficient, the mapping relationship of the spatial position coordinates is optimized, the voltage stability index is extracted from the circuit layout data, if the voltage stability index is lower than a preset threshold, the power demand parameters are re-distributed, and an optimized current distribution is obtained; through the optimized current distribution, the light output uniformity is calculated and iteratively adjusted, the energy efficiency evaluation result is acquired, and an optimized current distribution model is constructed. Further, according to the initial current distribution model, the power demand is iteratively calculated by an optimization algorithm, including: for the power demand data of each channel, a genetic algorithm population with power balance degree as the fitness function is constructed, and an initial potential scheme set is obtained; a crossover operation is performed on the potential scheme set, a new generation of scheme population after the crossover is acquired, and a candidate scheme subset with the smallest power distribution difference is determined; a mutation operation is performed from the candidate scheme subset, if the mutation causes the power balance degree to decrease, the mutation individual is discarded, and an optimized scheme subset is obtained; the change trend of the fitness of each generation is calculated according to the optimized scheme subset, and a converged optimal individual set is determined; the current distribution parameters are extracted from the optimal individual set, and a preliminary version of balanced supply is obtained.Further, the inter-channel interference data obtained from the potential current distribution scheme includes: obtaining inter-channel distance and interference factor data from a preliminary version obtained by a genetic algorithm, supplementing the interference factor data by calculating channel impedance values based on distance and factor to obtain an interference data set containing impedance; determining whether the interference factor exceeds a preset threshold for the interference data set, and if it does, reducing mutual influence by modifying path curvature to obtain an adjusted path data set; obtaining channel impedance changes according to the adjusted path data set to determine a reduced interference factor set; extracting optimization parameters from the reduced interference factor set to obtain an optimized channel configuration model. Further, the thermal distribution data obtained for the optimized channel configuration model includes: obtaining thermal distribution simulation data from the optimized channel configuration model, performing global search and iterating particle position with a particle swarm optimization algorithm with high heat area as input to obtain heat source distribution mapping; extracting load balancing simulation parameters for the heat source distribution mapping, and iteratively adjusting the current path using a position update mechanism to determine a dynamic current distribution set; determining high heat area risk according to the dynamic current distribution set, and if it exceeds a preset threshold, modifying path redundancy configuration to reduce heat concentration to obtain a thermal risk prediction data set; obtaining real-time thermal monitoring indicators from the thermal risk prediction data set, and optimizing the distribution balance of the indicators by global search to determine path adjustment parameter groups. Further, the power supply parameters extracted from the current balancing scheme include: obtaining voltage stability data of multiple light emitting units from a scheme determined by a particle swarm optimization algorithm, and performing item-by-item comparison and detection of the voltage stability data by a deviation threshold judgment mechanism, and if the voltage stability data exceeds a preset threshold, starting a feedback loop to obtain a remaining voltage resource set; reconfiguring the power supply path of the multiple light emitting units using a distribution adjustment method for the remaining voltage resource set to obtain a path redundancy configuration group; extracting heat source distribution mapping parameters according to the path redundancy configuration group, and iteratively optimizing the heat source distribution mapping parameters by global search to determine a dynamic voltage distribution set; obtaining real-time monitoring indicators from the dynamic voltage distribution set, and if the real-time monitoring indicators show heat concentration, modifying the power supply path to obtain a corrected power supply network.Further, the obtaining the light emitting effect index according to the corrected power supply network comprises: obtaining an overall light emitting effect index from the corrected power supply network, determining a deviation set of the index by adopting a balanced comparison method on the heat distribution data; verifying the matching degree of the deviation set with a target life model, if the matching degree is lower than a preset threshold, starting an iterative update of network parameters to obtain an adjusted configuration group; extracting a life enhancement configuration according to the adjusted configuration group, performing path optimization on the configuration by path traversal comparison, and determining a dynamic allocation set; obtaining a real-time stability index from the dynamic allocation set, if the index meets the preset threshold, obtaining a power supply network with enhanced service life; extracting a voltage stability correction value from the power supply network with enhanced service life, detecting the correction value by a deviation threshold comparison mechanism, and obtaining a path adjustment group. Further, the generating the final circuit structure description comprises: obtaining an integrated circuit structure description from the verified network parameters, optimizing the description by path traversal comparison, and determining an adjusted circuit group; extracting a driving configuration set from the adjusted circuit group, detecting the set by a balanced comparison method, and obtaining a multi-cell driving scheme; integrating the multi-cell driving scheme into a device design according to the multi-cell driving scheme, obtaining a real-time control index, and if the index meets the preset threshold, determining a precise control path; extracting light emitting effect data from the precise control path, verifying the data by a deviation threshold comparison mechanism, and obtaining a light source device configuration meeting the life requirement. Further, the adjusting the deviation by a feedback loop comprises: extracting power supply parameters of multiple light emitting cells from the current balanced scheme, detecting whether the power supply deviation exceeds a preset threshold by item-by-item comparison and detection, if the deviation exceeds the preset threshold, starting a feedback loop to re-distribute the remaining current resources, and obtaining an adjusted power supply parameter set; obtaining heat distribution mapping data for the adjusted power supply parameter set, performing global search on the heat distribution mapping data by an iterative optimization method, and determining a dynamic adjustment parameter group; updating the power supply path configuration according to the dynamic adjustment parameter group, obtaining real-time monitoring data, if the real-time monitoring data indicates heat concentration, further adjusting the path redundancy configuration, and obtaining a corrected power supply network. Further, the verifying the stability by the matching degree with the target model comprises: extracting overall light emitting effect data from the corrected power supply network, determining the deviation distribution of the light emitting effect data by a balanced comparison method; calculating the matching degree of the deviation distribution with a preset target life model, if the matching degree is lower than a preset threshold, adjusting the network parameters by an iterative update method, obtaining an optimized configuration set; extracting path stability parameters according to the optimized configuration set, optimizing the parameters by path traversal comparison, and determining a dynamic allocation scheme; obtaining real-time stability data from the dynamic allocation scheme, if the data meets the preset threshold, generating a power supply network configuration with enhanced life.

[0007] The technical scheme provided by the embodiment of the application can include the following beneficial effects:

[0008] The application discloses an integrated method of an integrated light source device, and aims at the comprehensive problems of uneven current distribution, serious interference between channels, local overheating, unstable light-emitting effect and shortened service life caused by power supply deviation when multiple light-emitting units are integrated. BRIEF DESCRIPTION OF DRAWINGS

[0009] Figure 1 The flowchart of the integrated method of the integrated light source device. DETAILED DESCRIPTION

[0010] In order to further understand the content of the application, the application is described in detail in combination with the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related application, and are not limited to the application. In addition, it should be noted that only the parts related to the application are shown in the drawings for the convenience of description.

[0011] As Figure 1 , the integrated method of the integrated light source device can specifically include:

[0012] S101, circuit layout data of the integrated light source device and power demand parameters of multiple light-emitting units are acquired, expected current values and spatial position coordinates of each light-emitting unit are extracted from the data through a preset simulation tool, and an initial current distribution model is obtained.

[0013] The circuit layout data of the integrated light source device and the power demand parameters of the multiple light emitting units are acquired, the expected current value and the spatial position coordinates of each light emitting unit are extracted from the circuit layout data by a preset simulation tool to obtain an initial current distribution model. The heat distribution simulation between the light emitting units is analyzed from the initial current distribution model, and the heat accumulation value of each light emitting unit is mapped by using temperature sensor data, which is acquired from the circuit layout data in advance by the preset simulation tool, and the data is derived from the temperature sensor simulation module embedded in the circuit layout to determine the heat distribution characteristics. In view of the heat distribution characteristics, the expected current value is adjusted in combination with the power demand parameters, the interference influence between the units is calculated by a finite element analysis method, the grid model is constructed and the interference equation is solved by taking the heat distribution characteristics and the adjusted expected current value as inputs, wherein the interference equation is dI / dt=k*(T_i-T_j), wherein dI / dt is the current change rate, k is the interference coefficient, T_i and T_j are the temperatures of adjacent units, to obtain an interference correction coefficient, which reflects the influence ratio of the heat interference between the units on the current. According to the interference correction coefficient, the mapping relationship of the current distribution is optimized, the voltage stability index is extracted from the circuit layout data, which is calculated based on the voltage fluctuation simulation in the circuit layout data, and if the voltage stability index is lower than a preset threshold value 0.95, the power demand parameters are re-distributed to obtain an optimized current distribution. Through the optimized current distribution, a light efficiency optimization adjustment mechanism is integrated, the light output uniformity is calculated based on the optimized current distribution, and the current value is iteratively adjusted by a gradient descent algorithm to obtain an energy efficiency evaluation result, and an optimized current distribution model is constructed.

[0014] In an embodiment, the circuit layout data of the integrated light source device generally includes the topology of the circuit board, the impedance value of the connection line, and the arrangement mode of the light emitting units, which can be acquired from the design drawing or the CAD file. The power demand parameters of the multiple light emitting units include the rated voltage, the maximum current threshold and the heat dissipation coefficient of each unit, for example, in an LED array, these parameters are used to ensure the consistency of the overall brightness. Information is extracted from these data by a preset simulation tool, such as a circuit simulation software, to construct an initial model.

[0015] Specifically, the preset simulation tool can be a simulation environment based on the SPICE framework, which simulates the current flow path by analyzing the circuit layout data.

[0016] For example, when dealing with multiple light-emitting units, the power requirement parameters are first imported, and then the tool automatically calculates the expected current value of each unit at a given voltage, for example, by deriving the current distribution based on Ohm's law and the power formula, avoiding direct numerical calculations and focusing on parameter mapping. This extraction process ensures the accuracy of the model and provides a foundation for subsequent optimization. Furthermore, the extraction of expected current values ​​involves analyzing node voltages and branch resistances in the circuit layout. Specifically, the circuit layout data is converted to a netlist format, and then the simulation tool iteratively solves for the current of each light-emitting unit, considering, for example, current deviations caused by impedance unevenness in parallel LED units. In this way, the tool generates an estimated current value for each unit, typically expressed in milliamperes. The key to this step is handling the interactions between multiple units to ensure that the initial model reflects the actual operating conditions.

[0017] In one possible implementation, for high-density light source devices, the tool can integrate thermal effect simulations to adjust current values ​​based on temperature changes, thereby improving the robustness of the model. This extraction process is not limited to static data but can be extended to dynamic load scenarios, such as simulating current variations at different brightness levels in tunable light sources.

[0018] It should be noted that the initial current distribution model is thus formed, which serves as a multi-dimensional array that stores the current and location information of each cell, supporting further analysis.

[0019] Preferably, the spatial coordinates are extracted directly from the circuit layout data, for example, by mapping the light-emitting units to a two-dimensional or three-dimensional coordinate system. In integrated light source devices such as display screens, the x and y coordinates of each LED correspond to its position in the array. The tool extracts these coordinates through geometric analytical algorithms to ensure the spatial accuracy of the model.

[0020] For example, in a curved surface light source device, the coordinates may include a z-axis to accommodate a non-planar layout.

[0021] In one embodiment, the initial current distribution model obtained can be applied to lighting system optimization.

[0022] For example, based on the extracted current values ​​and coordinates, the model is visualized as a heatmap, displaying areas of uneven current distribution, thereby guiding circuit adjustments. The model's construction process emphasizes efficient data integration, enabling rapid iteration within a simulation environment.

[0023] Understandably, this method is applicable to various integrated light source devices, such as stage lighting or automotive headlights. In these scenarios, the extraction process remains consistent, but parameters can be adjusted according to specific power requirements; for example, in headlights, where durability is emphasized, current margins are increased. Furthermore, the logic of the entire process, starting with data acquisition and progressing step-by-step to extraction and modeling, ensures coherence. In practice, the preset configurations of the simulation tools can be customized to match the circuit complexity of different devices.

[0024] For example, in small light source devices, extracting the expected current value can simplify the main circuit analysis, while in large-scale arrays, it is necessary to calculate the coordinates and currents of multiple units in parallel to achieve efficient modeling.

[0025] In one embodiment, the initial current distribution model can be output as a file format, which facilitates integration into the design software and supports subsequent power balancing adjustments.

[0026] S102. Based on the initial current distribution model, a genetic algorithm is used to iteratively calculate the power demand of each channel to determine potential current allocation schemes. The genetic algorithm selects a preliminary version of balanced supply from multiple alternative schemes through crossover and mutation operations.

[0027] Based on the initial current distribution model, a genetic algorithm is used to iteratively calculate the power demand for each channel to determine potential current allocation schemes. The genetic algorithm uses crossover and mutation operations to select a preliminary version of balanced supply from multiple candidate schemes. Power demand data for each channel is obtained from the initial current distribution model, and a genetic algorithm population is constructed. The fitness function is defined as the root mean square error of power allocation, F = ∑(Pi - Pavg)^2 / N (where Pi is the power of the i-th channel, Pavg is the average power, and N is the number of channels). The calculation process involves first calculating the average power of all channels, then calculating the sum of squares of the deviations for each channel and dividing by the number of channels to obtain an initial set of potential schemes. A crossover operation is performed on this set of potential schemes to obtain a new generation of schemes. The subset of candidate schemes with the smallest power allocation difference is determined. A mutation operation is performed on this subset of candidate schemes. If the mutation leads to a decrease in power balance, the mutated individual is discarded, resulting in an optimized subset of schemes. The fitness change trend of each generation is calculated based on the optimized subset of schemes to determine the optimal set of individuals after convergence. Current allocation parameters are extracted from the optimal set of individuals to obtain a preliminary version of balanced supply.

[0028] In one implementation, a genetic algorithm is used to iteratively calculate the power requirements of each channel in the integrated light source device based on an initial current distribution model, in order to determine a potential current allocation scheme.

[0029] Specifically, the genetic algorithm first uses the expected current value and spatial coordinates of each light-emitting unit in the initial current distribution model as basic data to construct an initial population. Each individual represents a possible current allocation configuration; for example, a channel is defined as a grouped set of light-emitting units on a circuit board, with each channel corresponding to specific power requirement parameters such as rated current range and load balancing requirements. This ensures that the algorithm input reflects the actual circuit layout of the device. Furthermore, the genetic algorithm begins its iterative process by initializing a population containing multiple alternative schemes.

[0030] For example, when processing LED array light source devices, the population size can be set to 50 to 100 individuals, each encoded as a vector representing the current distribution ratio of all channels. Random mutations are generated based on the current estimates in the initial model to cover diversity. The fitness function calculates the balance of the scheme according to the power demand of each channel, such as evaluating the mean square error of the current deviation, to ensure that the scheme prioritizes the uniformity of the overall power supply rather than the optimization of a single channel.

[0031] Preferably, in the iterative computation, the genetic algorithm performs a selection operation, choosing individuals with higher fitness from the current population as parents. Specifically, this process includes using a roulette wheel selection mechanism, where the probability of an individual being selected is proportional to its fitness. In integrated light source devices such as stage lighting systems, this selection helps to quickly converge to an allocation scheme that meets high brightness requirements, avoiding the propagation of low-fitness schemes.

[0032] In one possible implementation, the crossover operation is applied to the selected parent individual to generate a new offspring.

[0033] It should be noted that the intersection point can be randomly selected at the middle of the vector, for example, by exchanging the current allocation values ​​of the latter half between two parent schemes, thereby combining the optimization characteristics of different channels. In high-density light source devices such as automotive headlights, this operation ensures that the new scheme inherits the equalization characteristics of the parent, while introducing diversity to explore a wider solution space. Furthermore, the mutation operation introduces new mutations by randomly adjusting some elements of the offspring individuals, such as changing the current ratio value of a certain channel with a small probability, considering the influence of adjacent units based on the spatial position coordinates of the initial model. This mutation prevents the algorithm from getting trapped in local optima and is particularly useful when dealing with curved surface light source devices, because the non-planar distribution of coordinates requires additional adjustments to maintain power consistency.

[0034] In one embodiment, the selection, crossover, and mutation processes described above are repeated iteratively until a preset number of iterations or a convergence condition is reached, such as a fitness change being less than a threshold.

[0035] For example, in display applications, after 100 iterations, the scheme with the highest fitness is selected from the population as an initial version, which represents a balanced current distribution that ensures that the power requirements of each channel are met without significant deviation.

[0036] Understandably, the logic of the entire process begins with the import of the initial model and gradually progresses to population evolution, ensuring consistency. In small-scale light source devices, iteration can simplify the process and reduce computational load, while in large-scale arrays, multiple channel mutation operations need to be processed in parallel to achieve efficient scheme generation. In another implementation, for tunable light source devices, the genetic algorithm can integrate dynamic power demand adjustment, such as reinitializing the population under different brightness modes to adapt to load changes. Through this extension, the initial version generated by the algorithm supports current balancing under various operating scenarios. Furthermore, this initial version can be output as a file for easy integration into design software, supporting subsequent circuit verification. In the optimization of integrated light source devices, this scheme selection emphasizes parameter flexibility, allowing adjustment of parameters such as crossover probability according to specific channel requirements.

[0037] S103. From the preliminary version obtained by the genetic algorithm, obtain the distance and interference factor data between channels, and determine if the interference factor exceeds the preset threshold. Then, reduce the mutual influence by adjusting the routing path to obtain an optimized channel configuration model.

[0038] The initial version obtained from the genetic algorithm acquires channel distance and interference factor data. This interference factor data is supplemented by calculating channel impedance values ​​based on distance and factors. The impedance Z is calculated using the formula Z = k * d / i, where k is a proportionality constant, d is the channel distance, and i is the interference factor. This impedance represents the equivalent resistance of electromagnetic interference between channels, improving the completeness of the dataset and obtaining an interference dataset containing impedance. If the interference factor in the interference dataset exceeds a preset threshold of 0.5, the path curvature is modified using the Bezier curve algorithm. Specifically, control points are iteratively adjusted to minimize the radius of curvature, reducing mutual influence and obtaining an adjusted path dataset. Based on the adjusted path dataset, channel impedance changes are obtained to determine the reduced interference factor set. Optimization parameters are extracted from the reduced interference factor set to obtain an optimized channel configuration model.

[0039] In one implementation, the distance and interference factor data between channels are obtained from a preliminary version derived from the genetic algorithm.

[0040] Specifically, this initial version includes the current distribution ratio and spatial coordinates of each channel. By analyzing this data, the physical distance between channels is first calculated. For example, in the circuit board layout of an integrated light source device, the Euclidean distance formula is used to determine the spacing between adjacent channels based on coordinate points. The interference factor is defined as a potential mutual influence index based on distance, considering factors such as electromagnetic interference or thermal conduction, and quantified into a numerical value, such as the interference factor being equal to the reciprocal of the distance multiplied by the current density weighting factor. This acquisition process ensures that the data reflects the layout characteristics of the actual device, and in LED array light sources, it helps to identify potential problems in high-density areas. Furthermore, if the interference factor exceeds a preset threshold, further optimization is required.

[0041] It should be noted that the preset threshold can be set according to the device type. For example, it can be set to 0.5 in a stage lighting system to avoid uneven brightness caused by excessive interference. The judgment process involves comparing the interference factor values ​​of each pair of channels. If the value exceeds the threshold, the pair of channels is marked as needing adjustment. This judgment logic starts from the initial version of the vector data and iterates through it one by one to ensure comprehensive coverage of all channel combinations.

[0042] In one possible implementation, for small display devices, the decision can be simplified to checking only adjacent channels, while in large-scale arrays, parallel processing is required to improve efficiency.

[0043] Preferably, mutual interference is reduced by adjusting the wiring path.

[0044] For example, after determining that the interference exceeds the threshold, a path optimization algorithm is used to replan the circuit routing. This could involve changing the routing from a straight line to a more circuitous path to increase distance, or introducing a shielding layer to reduce electromagnetic coupling. The specific process includes first identifying interference channel pairs, then simulating various routing schemes, such as prioritizing a layout that minimizes intersections in automotive headlight devices. The interference factor under the new path is iteratively tested to ensure it is reduced below the threshold. This adjustment emphasizes the flexibility of the path, allowing for dynamic modification based on spatial coordinates, and is particularly suitable for curved light source devices, as non-planar layouts require additional consideration of bending effects.

[0045] In one embodiment, the optimized channel configuration model, once obtained, can be used as output for subsequent verification.

[0046] For example, the adjusted trace paths and current distribution are integrated into a single model file and loaded into the design software of the integrated light source device to simulate overall performance. This model ensures minimal inter-channel interference and supports stable operation in various brightness modes.

[0047] Understandably, the entire process, from data acquisition to model generation, forms a coherent optimization chain. In adjustable lighting devices, this method can be extended to dynamic scenarios, such as reassessing interference based on real-time power changes to adapt to different operating conditions. Through these steps, the technical solution demonstrates versatility within the same field, such as various applications ranging from stage lighting to automotive headlights.

[0048] S104. For the optimized channel configuration model, obtain thermal distribution simulation data, and use the particle swarm optimization algorithm to perform a global search and position update of the current path in the high-heat area to initially determine the current balancing scheme.

[0049] Heat distribution simulation data is obtained from the optimized channel configuration model. A particle swarm optimization algorithm is used to perform a global search and iterative particle position analysis with high-heat regions as input to obtain a heat source distribution map. Load balancing simulation parameters are extracted from this heat source distribution map, and a position update mechanism is used to iteratively adjust the current path to determine a dynamic current allocation set. The risk of high-heat regions is assessed based on this dynamic current allocation set. If the risk exceeds a preset threshold, path redundancy configuration is modified to reduce heat concentration, resulting in a heat risk prediction dataset. Real-time heat monitoring indicators are obtained from this heat risk prediction dataset, and the distribution balance of these indicators is optimized through a global search to determine a path adjustment parameter set. The channel configuration model is updated based on this path adjustment parameter set to obtain a current balancing scheme with minimal overheating risk.

[0050] In one implementation, thermal distribution simulation data is obtained from an optimized channel configuration model.

[0051] Specifically, the model includes the routing paths and current distribution information of the channels. Simulation software is used to analyze heat generation and diffusion; for example, in an LED array of an integrated light source device, the finite element method is used to simulate the heat distribution of each channel. The heat distribution simulation data includes temperature field maps and heat flow vectors, which reflect the influence of heat conduction between channels.

[0052] It should be noted that the acquisition process first loads the model coordinates, then inputs current parameters for iterative calculations to ensure data coverage of the entire device layout. In stage lighting systems, this simulation helps identify heat accumulation points in high-power channels. Furthermore, a particle swarm optimization algorithm is used to perform a global search and position update of the current paths in high-heat areas. Particle swarm optimization is a swarm intelligence-based optimization method that simulates the foraging behavior of a flock of birds, where each particle represents a possible current path scheme, its position represents the path coordinates, and its velocity represents the update direction.

[0053] For example, during the algorithm initialization phase, a swarm of particles is randomly generated, with each particle carrying an initial configuration of the current path, such as the path length and bend points. Then, a global search is performed, evaluating the overheating risk of each particle by calculating a fitness function that quantifies the peak temperature based on thermal distribution data. Position updates are then performed according to the rules of the particle swarm optimization algorithm: each particle adjusts its velocity and position based on its own optimal position and the global optimal position; for example, the velocity update formula considers inertia weights, cognitive factors, and social factors, thus iteratively converging towards a lower overheating direction.

[0054] In one possible implementation, for automotive headlight devices, the high-heat region is defined as a cluster of channels with temperatures exceeding 80 degrees Celsius. The algorithm searches for path adjustments through multiple iterations (e.g., 50 times), such as extending the wiring to disperse the heat source. This global search ensures the exploration of multiple path combinations, while position updates achieve incremental optimization. The algorithm emphasizes convergence and can be completed quickly in small LED arrays, while in large-scale devices, parallel computation is required to improve efficiency.

[0055] Understandably, the entire algorithm is applied only to the current management of the light source device to avoid local optima traps.

[0056] Preferably, a current balancing scheme with the lowest risk of overheating is determined.

[0057] Specifically, based on the output of the particle swarm optimization algorithm, the path configuration corresponding to the globally optimal particle is selected, and then the current distribution is balanced, for example, the current ratio of the high-heat channel is reduced by 10% and transferred to the low-heat channel.

[0058] In one embodiment, in a stage lighting device, this scheme generates an equalization vector containing the final current value and path coordinates of each channel to ensure uniform overall temperature distribution. In another embodiment, for curved light sources in automotive headlights, the influence of the curved surface on heat conduction is additionally considered when acquiring thermal distribution simulation data, and the thermal gradient is accurately captured by adjusting the simulation mesh density. Furthermore, in this scenario, the particle swarm optimization algorithm is set to a particle count of 30, iterating until fitness stabilizes, with position updates focusing on minimizing thermal peaks.

[0059] For example, the resulting current balancing scheme supports multi-mode operation, such as further reducing risk in low-power mode.

[0060] It should be noted that this method is applicable to integrated lighting devices, such as applications from stage lights to headlights.

[0061] In one possible implementation, analog data acquisition is integrated with real-time sensor feedback to improve accuracy.

[0062] Preferably, the optimized solution is output as a model file for device verification.

[0063] S105. From the scheme determined by the particle swarm optimization algorithm, extract the power supply parameters of the multi-light-emitting units. If the power supply deviation of any unit exceeds the preset threshold of 5%, then fine-tune the remaining current resources through finite feedback loop to obtain the corrected power supply network.

[0064] Voltage stability data of multiple LEDs is obtained from the scheme determined by the particle swarm optimization algorithm. This data is then compared and checked item by item using a deviation threshold judgment mechanism. If the voltage stability data exceeds a preset threshold, a feedback loop is initiated to obtain a set of remaining voltage resources. The power supply paths of the multiple LEDs are reconfigured using an allocation adjustment method for this set of remaining voltage resources, resulting in a path redundancy configuration group. Heat source distribution mapping parameters are extracted from this path redundancy configuration group, and iterative optimization is performed on these parameters using a global search method to determine a dynamic voltage allocation set. Real-time monitoring indicators are obtained from this dynamic voltage allocation set. If the real-time monitoring indicators show heat concentration, the power supply path is modified to obtain a corrected power supply network.

[0065] The power supply parameters of the multi-emitting unit are extracted from the scheme determined by the particle swarm optimization algorithm.

[0066] Specifically, the scheme includes current path and distribution information. By parsing the data output by the algorithm, the voltage value, current intensity, and power consumption of each light-emitting unit are obtained. These power supply parameters reflect the load distribution between units. For example, in an LED array of an integrated light source device, the extraction process first loads the optimized path coordinates, and then calculates the power supply indicators of each unit to ensure that the data covers the entire array layout.

[0067] It should be noted that the power supply parameters are extracted using a standardized format, such as vector storage, for ease of subsequent evaluation. Furthermore, the system checks if the power supply deviation of any unit exceeds a preset threshold. Power supply deviation is defined as the difference between the actual power supply parameters and the ideal equilibrium value. The preset threshold is set according to the device specifications; for example, in a stage lighting system, the threshold is 5% current deviation.

[0068] For example, the parameters of all units are iterated through, and the deviation values ​​are compared one by one. If the deviation exceeds a threshold, the unit is marked as needing correction. This judgment process helps identify potential imbalance points and ensures system stability. In one implementation, the residual current resources are reallocated through a feedback loop. The feedback loop is an iterative mechanism that simulates closed-loop control. It first calculates the total residual current, i.e., the unallocated or redundant portion in the optimized scheme, and then reallocates it according to the priority of the units with deviations.

[0069] Specifically, at the start of the loop, the allocation queue is initialized, and the remaining current is injected into the deviation unit proportionally. Simultaneously, changes in the overall network load are monitored. For example, in an automotive headlight system, the current step size is adjusted by 0.1 amperes in each iteration to avoid overload. The number of loop iterations is set according to the number of deviations, such as 10 times, to ensure convergence to an equilibrium state.

[0070] It should be noted that this mechanism achieves resource optimization through multiple feedbacks, executes quickly in small LED arrays, and can be integrated into parallel processing in large-scale devices to improve efficiency.

[0071] Understandably, the core of a feedback loop lies in dynamic adjustment, which can handle changing lighting environments.

[0072] Preferably, a corrected power supply network is obtained.

[0073] Specifically, based on the output of the feedback loop, a new network configuration is generated, including the final power supply parameters and connection paths for each unit.

[0074] For example, in stage lighting installations, this network is represented in matrix form to ensure that the power supply deviation of all units is within a threshold.

[0075] In one possible implementation, for automotive headlights with curved light sources, the calibration process additionally considers the influence of surface geometry on current flow, and distributes power supply evenly by adjusting network nodes.

[0076] In one embodiment, this method is applied to an integrated light source device. If a deviation is detected after parameter extraction, a feedback loop reallocates resources, ultimately enabling the network to support multi-mode operation, such as maintaining balance in high-brightness mode. The formation of this correction network provides a reliable current management framework for practical deployments.

[0077] S106. Based on the corrected power supply network, obtain the overall luminous effect index, verify the stability by comparing the matching degree with the target lifetime model, and determine if the matching degree is lower than the preset threshold, then iteratively update the network parameters to enhance the lifetime.

[0078] The overall luminous efficacy index is obtained from the corrected power supply network. A set of deviations for this index is determined by using a balanced comparison method on the thermal distribution data. The matching degree between this deviation set and the target lifetime model is verified. If the matching degree is lower than a preset threshold, iterative updates to the network parameters are initiated to obtain an adjusted configuration group. Lifetime enhancement configurations are extracted from the adjusted configuration group, and path optimization is performed on these configurations using path traversal comparison to determine a dynamic allocation set. Real-time stability indices are obtained from the dynamic allocation set. If the indices meet a preset threshold, a power supply network with enhanced lifetime is obtained. Voltage stability correction values ​​are extracted from the power supply network with enhanced lifetime, and these correction values ​​are detected using a deviation threshold comparison mechanism to obtain a path adjustment group.

[0079] Based on the corrected power supply network, the overall luminous effect index is obtained.

[0080] Specifically, this indicator includes parameters such as luminous uniformity, brightness distribution, and thermal distribution, which are calculated from network parameters through simulation or actual measurement.

[0081] For example, in an LED array of an integrated light source device, the corrected voltage and current data are first loaded, and then the photoelectric conversion model is used to calculate the overall brightness value to ensure that the index reflects the performance status of the entire array.

[0082] It should be noted that the luminous effect index is stored in vector form for easier subsequent comparison and processing. In one implementation, for stage lighting systems, this acquisition process integrates sensor data feedback and calculates the index in real time to support dynamic adjustments. The target lifetime model is a predictive framework built based on historical data and physical simulations to estimate the operating time that a light source device can sustain under specific conditions. Specific input parameters include historical operating time, ambient temperature, and power level, etc.; the output format is the estimated operating time in hours; the construction process involves data cleaning, feature extraction, and training using linear regression methods, and integrates physical simulations such as the Arrhenius equation k=A*exp(-Ea / (R*T)), where k represents the decay rate, A is the pre-exponential factor, Ea is the activation energy, R is the universal gas constant, and T is the absolute temperature.

[0083] Specifically, the model considers factors such as current load, temperature effects, and material degradation, and calculates the expected lifetime curve using formulas. For example, network parameters are input into the model to generate a predicted distribution vector, which is then compared with the actual luminescence performance index to calculate vector similarity. The matching degree is defined as the cosine similarity between two vectors (formula: cosθ=(A·B) / (|A||B|), where A and B are vectors, · represents the dot product, and || represents the magnitude). A higher value indicates better stability. If the vectors are not normalized, Euclidean distance (formula: d=sqrt(∑(Ai-Bi)^2), where Ai and Bi are vector components, and sqrt represents the square root) can be used to adapt to different data conditions.

[0084] For example, in an automotive headlight system, the model first imports standard lifetime data, such as the decay rate of LED chips, and then compares the thermal distribution parameters of the calibration network one by one. If the similarity is close to 1, the verification is successful. This comparison process helps identify potential lifetime risks and ensures the reliability of the device in practical applications.

[0085] In one possible implementation, the model is built using a statistical regression method, with input variables including average current intensity and voltage fluctuation, and the output being a lifespan hour prediction, thereby quantitatively matching it with the acquired indicators.

[0086] It should be noted that this verification mechanism is data-driven and can handle varying lighting environments, such as adjusting model parameters to improve accuracy under high humidity conditions. If the matching degree is lower than a preset threshold, the network parameters are iteratively updated to enhance its lifespan. The preset threshold is set according to the device specifications; for example, a similarity threshold of 0.8 is used in a stage lighting system.

[0087] Specifically, the matching degree values ​​are iterated, and if they are lower than the threshold, an update mechanism is triggered. This mechanism is a cyclic optimization process that first calculates the deviation vector and then gradually adjusts the current distribution and voltage nodes in the network.

[0088] Preferably, in the integrated light source device, the update step size is initialized at the beginning of the iteration, such as a current increment of 0.05 amperes. After each cycle, the effect index is reacquired and the matching degree is compared until it reaches above the threshold.

[0089] In one embodiment, for automotive headlights with curved light sources, the update process additionally considers the influence of geometric curvature on heat conduction, and modifies the network connection path to achieve uniform parameter distribution, thereby avoiding local overheating that could shorten the lifespan.

[0090] Understandably, the number of iterations is set based on the initial deviation, such as 5 to 15 times, to ensure convergence to a stable state. This update enables resource re-optimization, can be executed quickly in small arrays, and can be processed in parallel in large-scale devices to improve efficiency. In one implementation, the iterative update generates a new power supply network configuration, including the final parameter matrix and path information.

[0091] For example, in multi-mode operation of stage lighting installations, this configuration supports balanced lifetime in high-brightness mode, ensuring consistent attenuation rates across all units. Furthermore, the application of this method provides a reliable lifetime management framework in practical deployments, maintaining installation performance through continuous verification.

[0092] S107. From the verified network parameters, generate the final integrated circuit structure description, and integrate it into the device design to achieve precise control of multi-unit drive, thereby obtaining a light source device configuration that meets the requirements of light emission effect and lifespan.

[0093] An integrated circuit structure description is obtained from the verified network parameters. This description is then optimized using path traversal comparison to determine the adjusted circuit group. The path traversal comparison employs a depth-first search to ensure balanced distribution of circuit nodes. The input to the path traversal comparison is the circuit node graph. The process involves a depth-first search of paths starting from the root node, calculating the distribution balance of nodes along each path, and outputting the optimized node adjustments to balance the load. A driver configuration set is extracted from the adjusted circuit group. A balanced comparison method is used to detect the set and obtain a multi-unit driver scheme. This balanced comparison method compares the balance by calculating the node load difference, specifically calculating D = |L_i - L_avg| (where L_i is the load of node i, and L_avg is the average load). If D is less than 0.1, it is considered balanced. The multi-unit drive scheme is integrated into the device design. Real-time control indicators are obtained, and if the indicators meet a preset threshold, a precise control path is determined. These real-time control indicators are derived from the device's simulated operation data. The determination uses a threshold comparison mechanism to verify the stability of the indicators, with the mechanism being C=|IT|<0.05 (where I is the current indicator and T is the preset threshold of 0.9). Luminescence effect data is extracted from the precise control path, and the data is verified using a deviation threshold comparison mechanism to obtain a light source device configuration that meets the lifespan requirements. This deviation threshold comparison mechanism uses absolute difference calculation to compare against a preset deviation threshold, specifically A=|D_e-D_p|<5 (where D_e is the expected luminescence data and D_p is the actual data).

[0094] The final integrated circuit structure description is generated from the verified network parameters. This process first extracts key elements from the network parameters, such as voltage distribution, current paths, and connection nodes, and then constructs the circuit topology using a mapping algorithm. The mapping algorithm takes the key elements as input and outputs a circuit topology graph. Specifically, it constructs an adjacency matrix A (A[i,j] represents the connection strength from node i to j), and then uses a depth-first search algorithm to traverse the matrix to generate the topology.

[0095] Specifically, network parameters are represented in matrix form, where rows correspond to cell positions and columns correspond to electrical attributes. These parameters are input into a structure generation framework called CircuitGen Model. This framework constructs the circuit through parameter parsing and topology optimization algorithms, taking the electrical attribute matrix as input and outputting a description document containing node connections and component specifications. In an integrated light source device, this description includes the series and parallel configuration of LED units to ensure the overall circuit supports uniform power supply. In one implementation, for a stage lighting system, the generation process integrates compatibility checks, first verifying the stability of the network parameters, and then constructing the circuit hierarchy layer by layer, from power input to output, forming a hierarchical structure description. This description is represented graphically for easy import into subsequent designs.

[0096] Understandably, this generation mechanism, achieved through parameter aggregation, can handle multi-scale arrays, such as rapid output in small devices and block processing in large-scale systems to maintain efficiency. Precise control of multi-unit drives is achieved through integration into the device design. The integration steps include embedding the circuit structure description into the design software to generate the control logic for the drive modules.

[0097] Specifically, the drive module allocates pulse width modulation signals to each unit based on the path information in the description, enabling independent or group control. In automotive headlight systems, the structural description is loaded first, followed by the configuration of the microcontroller interface to ensure zero-delay signal transmission, thereby precisely adjusting the brightness level.

[0098] In one possible implementation, the integration process takes into account inter-cell interference by adding filtering components to the circuit description to avoid signal crosstalk.

[0099] It's important to note that the core of precise control lies in the design of the feedback loop. Monitoring points are extracted from the structural description, and driving parameters are adjusted in real time to maintain consistent light output. This control can adapt to dynamic environments, such as automatically balancing the load under high-intensity lighting.

[0100] For example, in the case of a curved surface light source device, a geometric model is additionally incorporated during integration to align the circuit structure description with the physical layout, ensuring that the drive signals are uniformly distributed along the curved surface. This process helps improve control accuracy and avoids local unit overload. The resulting light source device configuration meets the requirements for luminous efficacy and lifespan. This configuration is the final output generated based on the integrated design, including circuit diagrams, parameter tables, and control protocols.

[0101] Specifically, the configuration's luminous uniformity and thermal management performance are verified using simulation tools; if they meet the requirements, the configuration is then solidified. Under the multi-mode operation of the stage lighting installation, this configuration supports switching between different drive schemes to ensure balanced lifespan.

[0102] In one embodiment, for an LED array, the configuration generation process calculates expected lifetime metrics and adjusts the drive threshold based on performance parameters to form a complete device blueprint. This configuration generation provides a reliable deployment foundation and maintains performance stability in practical applications.

[0103] Understandably, this method optimizes the device by converting parameters into configurations. For example, in automotive headlights, the configuration emphasizes durability, ensuring consistent illumination over extended periods. In one implementation, the configuration output can be extended to the testing phase, where measured data is used to fine-tune the circuit description, further enhancing control precision. This process emphasizes versatility and is applicable to variations of similar lighting devices.

[0104] The above description is only a preferred embodiment of the present invention. It should be noted that those skilled in the art can make several improvements and additions without departing from the principle of the present invention, and these improvements and additions should also be considered within the scope of protection of the present invention.

Claims

1. An integrated method for an integrated light source device, characterized in that, include: The process involves acquiring circuit layout data and power requirement parameters for multiple light-emitting units of an integrated light source device. Using pre-defined tools, the expected current value and spatial coordinates of each light-emitting unit are extracted to construct an initial current distribution model. Based on this model, an optimization algorithm is used to iteratively calculate the power requirements, determining potential current allocation schemes. Inter-channel interference data is obtained from these potential schemes, and path configuration is adjusted to reduce interference, resulting in an optimized channel configuration model. For this optimized model, heat distribution data is acquired, and a global search algorithm is used to adjust the current paths in high-heat areas, determining a current balancing scheme with low overheat risk. Power supply parameters are extracted from the current balancing scheme, and deviations are adjusted through feedback loops to obtain a corrected power supply network. Based on the corrected power supply network, the light emission effect index is obtained, the stability is verified by the matching degree with the target model, the network parameters are iteratively updated, the final circuit structure description is generated, and it is integrated into the device design to realize multi-unit drive control.

2. The integrated method for the integrated light source device as described in claim 1, characterized in that, The process of acquiring circuit layout data and power requirement parameters of multiple light-emitting units for the integrated light source device includes: extracting the expected current value and spatial coordinates of each light-emitting unit from the circuit layout data using a preset simulation tool to construct an initial current distribution model; analyzing the thermal distribution simulation between light-emitting units from the initial current distribution model, mapping the heat accumulation value of each light-emitting unit using temperature sensor data to determine the thermal distribution characteristics; adjusting the expected current value based on the thermal distribution characteristics and power requirement parameters, calculating the interference effect between units using finite element analysis, constructing a mesh model and solving the interference equation to obtain the interference correction coefficient; optimizing the mapping relationship of spatial coordinates based on the interference correction coefficient, extracting voltage stability index from the circuit layout data, and if the voltage stability index is lower than a preset threshold, reallocating the power requirement parameters to obtain an optimized current distribution; calculating the light output uniformity and iteratively adjusting it using the optimized current distribution to obtain energy efficiency evaluation results and construct an optimized current distribution model.

3. The integrated method for the integrated light source device as described in claim 1, characterized in that, The step of iteratively calculating power demand using an optimization algorithm based on the initial current distribution model includes: constructing a genetic algorithm population with power balance as the fitness function for the power demand data of each channel to obtain an initial set of potential solutions; performing a crossover operation on the set of potential solutions to obtain a new generation of solutions, and determining the subset of candidate solutions with the smallest power allocation difference; performing a mutation operation on the subset of candidate solutions, and discarding the mutated individuals if the mutation leads to a decrease in power balance, thereby obtaining an optimized subset of solutions; calculating the fitness change trend of each generation based on the optimized subset of solutions to determine the optimal set of individuals after convergence; and extracting current allocation parameters from the optimal set of individuals to obtain a preliminary version of balanced supply.

4. The integrated method for the integrated light source device as described in claim 1, characterized in that, The step of obtaining inter-channel interference data from the potential current allocation scheme includes: obtaining inter-channel distance and interference factor data from the preliminary version obtained by the genetic algorithm; supplementing the interference factor data by calculating channel impedance values ​​based on distance and factors to obtain an interference dataset containing impedance; determining whether the interference factor exceeds a preset threshold for the interference dataset; if it does, reducing mutual influence by modifying the path curvature to obtain an adjusted path dataset; obtaining channel impedance changes based on the adjusted path dataset to determine the reduced interference factor set; and extracting optimization parameters from the reduced interference factor set to obtain an optimized channel configuration model.

5. The integrated method for the integrated light source device as described in claim 1, characterized in that, The process of obtaining heat distribution data for the optimized channel configuration model includes: obtaining heat distribution simulation data from the optimized channel configuration model; performing a global search and iterating particle positions using a particle swarm optimization algorithm with high-heat regions as input to obtain a heat source distribution map; extracting load balancing simulation parameters from the heat source distribution map; iteratively adjusting the current path using a position update mechanism to determine a dynamic current allocation set; judging the risk of high-heat regions based on the dynamic current allocation set; modifying path redundancy configuration to reduce heat concentration if the risk exceeds a preset threshold to obtain a heat risk prediction dataset; obtaining real-time heat monitoring indicators from the heat risk prediction dataset; optimizing the distribution balance of the indicators through a global search to determine a path adjustment parameter set.

6. The integrated method for the integrated light source device as described in claim 1, characterized in that, The step of extracting power supply parameters from the current balancing scheme includes: obtaining voltage stability data of multiple light-emitting units from the scheme determined by the particle swarm optimization algorithm; comparing and detecting the voltage stability data item by item through a deviation threshold judgment mechanism; if the voltage stability data exceeds a preset threshold, initiating a feedback loop to obtain a set of remaining voltage resources; reconfiguring the power supply path of the multiple light-emitting units using an allocation adjustment method for the set of remaining voltage resources to obtain a path redundancy configuration group; extracting heat source distribution mapping parameters based on the path redundancy configuration group; iteratively optimizing the heat source distribution mapping parameters using a global search method to determine a dynamic voltage allocation set; obtaining real-time monitoring indicators from the dynamic voltage allocation set; if the real-time monitoring indicators show heat concentration, modifying the power supply path to obtain a corrected power supply network.

7. The integrated method for the integrated light source device as described in claim 1, characterized in that, The step of obtaining luminous efficacy indicators based on the corrected power supply network includes: obtaining an overall luminous efficacy indicator from the corrected power supply network; determining a set of deviations for the indicator by using a balanced comparison method on thermal distribution data; verifying the matching degree between the deviation set and the target lifetime model; if the matching degree is lower than a preset threshold, initiating iterative updates to network parameters to obtain an adjusted configuration group; extracting lifetime enhancement configurations based on the adjusted configuration group; optimizing the path of the configurations through path traversal comparison to determine a dynamic allocation set; obtaining a real-time stability indicator from the dynamic allocation set; if the indicator meets a preset threshold, obtaining a power supply network with enhanced lifetime; extracting voltage stability correction values ​​from the power supply network with enhanced lifetime; detecting the correction values ​​through a deviation threshold comparison mechanism to obtain a path adjustment group.

8. The integrated method for the integrated light source device as described in claim 1, characterized in that, The process of generating the final circuit structure description includes: obtaining an integrated circuit structure description from the verified network parameters; optimizing the description through path traversal comparison to determine the adjusted circuit group; extracting a set of driving configurations for the adjusted circuit group; detecting the set using a balanced comparison method to obtain a multi-unit driving scheme; integrating the multi-unit driving scheme into the device design to obtain real-time control indicators; determining a precise control path if the indicators meet a preset threshold; extracting luminous effect data from the precise control path; verifying the data through a deviation threshold comparison mechanism to obtain a light source device configuration that meets the lifespan requirements.

9. The integrated method for the integrated light source device as described in claim 1, characterized in that, The step of adjusting the deviation through feedback loop includes: extracting the power supply parameters of the multiple light-emitting units from the current balancing scheme; determining whether the power supply deviation exceeds a preset threshold by comparing each item; if it exceeds the threshold, initiating a feedback loop to redistribute the remaining current resources to obtain an adjusted power supply parameter set; acquiring heat distribution mapping data for the adjusted power supply parameter set; performing a global search on the heat distribution mapping data through iterative optimization to determine a dynamic adjustment parameter group; updating the power supply path configuration according to the dynamic adjustment parameter group; acquiring real-time monitoring data; if the real-time monitoring data indicates heat concentration, further adjusting the path redundancy configuration to obtain a corrected power supply network.

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