CN-122028277-A - Multi-objective optimization method, device and equipment for car lamp based on particle swarm optimization

CN122028277ACN 122028277 ACN122028277 ACN 122028277ACN-122028277-A

Abstract

The application discloses a multi-objective optimization method, device and equipment for a car lamp based on a particle swarm algorithm, and relates to the technical field of photoelectrons. The method comprises the steps of detecting communication distance and ambient illuminance of a front target object in real time, obtaining the lowest emergent luminous flux threshold value under a current vehicle running mode, taking the communication distance and the ambient illuminance as environment variables, taking laser power, a beam divergence angle and a modulation depth as optimizing variables, adopting a particle swarm algorithm to iteratively calculate a comprehensive evaluation function under the forced constraint of meeting the lowest emergent luminous flux threshold value, outputting a globally optimal control parameter combination, generating a high-frequency driving signal according to the optimal modulation depth, driving a blue laser diode to output a blue laser beam carrying communication codes with optimal laser power, dynamically adjusting a projection optical module according to the optimal beam divergence angle, and changing the divergence angle of an emergent white light illumination beam. The method remarkably improves the self-adaptive capacity and the multi-target comprehensive performance of the intelligent car lamp in a dynamic traffic environment.

Inventors

CAO BING
LI JIHONG
SUN DIAO
XU FENG

Assignees

苏州大学

Dates

Publication Date: 20260512
Application Date: 20260414

Claims (10)

1. A multi-objective optimization method for a vehicle lamp based on a particle swarm algorithm, which is characterized by being applied to an intelligent vehicle lamp system, the method comprising: Detecting the communication distance of a target object in front of the vehicle and the ambient illuminance of the current environment in real time, and acquiring the lowest emergent luminous flux threshold value in the current vehicle running mode; The communication distance and the ambient illuminance are taken as environment variables, and laser power, a beam divergence angle and a modulation depth are taken as optimizing variables, under the forced constraint of meeting the minimum emergent luminous flux threshold, a particle swarm algorithm is adopted to iterate and calculate a comprehensive evaluation function, and a globally optimal control parameter combination is output, wherein the globally optimal control parameter combination comprises optimal laser power, optimal beam divergence angle and optimal modulation depth; Generating a high-frequency driving signal according to the optimal modulation depth, and driving a blue laser diode to output a blue laser beam carrying communication codes with optimal laser power; And dynamically adjusting the projection optical module according to the optimal beam divergence angle to change the divergence angle of the emergent white light illumination beam.
2. The particle swarm algorithm-based vehicle lamp multi-objective optimization method of claim 1, wherein the communication distance and the ambient illuminance are used as ambient variables, the laser power, the beam divergence angle and the modulation depth are used as optimization variables, the particle swarm algorithm is adopted to iteratively calculate a comprehensive evaluation function under the forced constraint of meeting the minimum emergent luminous flux threshold, and a globally optimal control parameter combination is output, comprising: Substituting the parameter combination carried by each particle in the particle swarm into a preset physical model, and respectively calculating a blue light hazard assessment value, a communication signal-to-noise ratio and total emergent luminous flux by combining the communication distance and the ambient illuminance detected in real time; Constructing a punishment function according to the blue light hazard assessment value and the communication signal to noise ratio, and dynamically calculating a safety weight and a communication bandwidth weight according to the current communication distance; And constructing an adaptability objective function based on the safety weight, the communication bandwidth weight and the penalty function, performing particle swarm iterative optimization by taking the adaptability objective function as a comprehensive evaluation function, and outputting optimal laser power, an optimal beam divergence angle and an optimal modulation depth.
3. The method for optimizing a vehicle lamp multi-objective based on a particle swarm algorithm according to claim 2, wherein calculating the blue light hazard assessment value comprises: Extracting residual high-frequency blue light power transmitted out of the fluorescent powder layer, and calculating the irradiation surface area of the blue light beam at the target distance according to the beam divergence angle and the communication distance; converting the blue light radiation power density under different test distances to a unified reference distance through a square scale factor, and calculating to obtain the blue light hazard assessment value by combining a non-hazard exemption level environment limit value; The communication signal-to-noise ratio is calculated by: And calculating the communication signal-to-noise ratio according to the effective blue light signal power at the communication distance, the background shot noise caused by the ambient illuminance and the thermal noise.
4. The method for optimizing a vehicle lamp multi-objective based on a particle swarm algorithm according to claim 2, wherein dynamically calculating the safety weight and the communication bandwidth weight according to the current communication distance comprises: , ; wherein L is the current communication distance, Beta is the gradient coefficient when the communication distance is smaller than the preset critical switching distance threshold value When the security weight is Dominant when the communication distance is greater than When the communication bandwidth weight is set Dominant.
5. The particle swarm algorithm-based vehicle lamp multi-objective optimization method of claim 2, wherein the penalty function comprises a blue-light safety penalty term and a communication signal-to-noise penalty term, wherein constructing an fitness objective function based on the safety weight, the communication bandwidth weight, and the penalty function comprises: presetting a signal-to-noise ratio threshold of a forward error correction communication base line; Constructing a first punishment item according to the blue light hazard assessment value; Constructing a second punishment item according to the communication signal-to-noise ratio and the signal-to-noise ratio threshold; the fitness objective function is expressed as: ; Wherein, the As a function of the safety utility, As a function of the bandwidth utility, For the joint penalty term of the first penalty term and the second penalty term, Penalty terms are constrained for luminous flux.
6. The particle swarm algorithm-based vehicle lamp multi-objective optimization method according to claim 1, wherein the iterative optimization process of the particle swarm algorithm comprises: Initializing a particle swarm, and setting a three-dimensional optimizing variable set, wherein the three-dimensional optimizing variable set comprises laser power, a beam divergence angle and a modulation depth; Calculating the fitness value of each particle in the current particle swarm, and updating the historical optimal position and the global optimal position of the individual; Driving the particle swarm to move in the multidimensional solution space, and repeating iteration until the maximum iteration times or convergence conditions are met; and extracting a global optimal solution combination, outputting the optimal laser power and the optimal modulation depth to a communication modulation module, and outputting the optimal beam divergence angle to a projection optical module.
7. A multi-objective vehicle lamp optimization device based on a particle swarm algorithm, comprising: The information detection module is used for detecting the communication distance of the target object in front of the vehicle and the ambient illuminance of the current environment in real time and acquiring the lowest emergent luminous flux threshold value in the current vehicle running mode; The parameter solving module is used for iteratively calculating a comprehensive evaluation function by adopting a particle swarm algorithm under the forced constraint of meeting the minimum emergent luminous flux threshold value by taking the communication distance and the ambient illuminance as environment variables and taking laser power, a beam divergence angle and a modulation depth as optimizing variables, and outputting a globally optimal control parameter combination, wherein the globally optimal control parameter combination comprises optimal laser power, optimal beam divergence angle and optimal modulation depth; The light beam output module is used for generating a high-frequency driving signal according to the optimal modulation depth and driving the blue laser diode to output a blue laser beam carrying communication codes with optimal laser power; And the angle adjusting module is used for dynamically adjusting the projection optical module according to the optimal beam divergence angle and changing the divergence angle of the emergent white light illumination beam.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and running on the processor, characterized in that the processor, when executing the computer program, implements the particle swarm algorithm-based vehicle lamp multi-objective optimization method according to any of claims 1 to 6.
9. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the particle swarm algorithm based vehicle light multi-objective optimization method according to any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements a particle swarm algorithm based vehicle light multi-objective optimization method according to any of claims 1 to 6.

Description

Multi-objective optimization method, device and equipment for car lamp based on particle swarm optimization Technical Field The invention relates to the technical field of photoelectrons, in particular to a method, a device and equipment for optimizing a plurality of targets of a car lamp based on a particle swarm algorithm. Background With the rapid development of intelligent transportation and internet of vehicles technology, automobile headlamps are being transformed from a single night illumination tool to a multifunctional intelligent node integrating illumination, environment sensing and visible light communication. The system based on blue laser excited yellow fluorescent powder becomes an ideal hardware platform for realizing long-distance workshop/vehicle road optical communication due to high brightness, long irradiation distance and high-frequency modulation characteristics of the laser diode. However, the existing car light design method faces the technical bottleneck that firstly, in a car light communication system of blue light excited fluorescent powder, the inherent contradiction between illumination, communication and biosafety exists. To obtain high luminous flux and high color rendering, it is generally necessary to increase the blue light component, but this easily causes blue light hazard to the retina of a pedestrian or driver ahead. Meanwhile, because the afterglow effect of the fluorescent powder can filter out high-frequency modulation signals, the high-speed communication bandwidth of the system is highly dependent on the residual direct blue light which is not absorbed by the fluorescent powder and is directly transmitted. Therefore, increasing the communication bandwidth (requiring an increase in residual blue light) and ensuring eye biosafety (requiring a decrease in blue light) constitute a strong nonlinear physical conflict. Second, in real traffic scenes, severe fluctuations in ambient illuminance may lead to a dramatic deterioration in the communication signal-to-noise ratio, and changes in the target distance may cause an exponential change in the blue light radiation density. The traditional static design or the conventional linear weighted optimization method cannot perform self-adaptive optimization according to the detection distance and the ambient illuminance which change in real time, and is difficult to meet the requirements of basic illumination compliance, blue light safety limit and visible light communication bandwidth in the dynamic driving process. Therefore, there is a need for a multi-objective optimization method for vehicle lamps based on particle swarm optimization that overcomes the above drawbacks. Disclosure of Invention The invention aims to provide a multi-target optimization method, device and equipment for a car lamp based on a particle swarm optimization, which are used for realizing self-adaptive collaborative optimization of car lamp illumination, blue light safety and visible light communication performance by detecting communication distance and ambient illuminance in real time and taking laser power, beam divergence angle and modulation depth as joint optimization variables and adopting the particle swarm optimization to dynamically output a global optimal control parameter combination on the premise of meeting basic illumination forced constraint. Compared with the traditional static design or linear weighting method, the method can adjust the beam divergence angle and the modulation depth in real time according to the distance between the front target and the near target and the severe change of the ambient illuminance, effectively balance the blue light biosafety in a near scene and the communication bandwidth requirement in a far scene, and simultaneously ensure the compliance of basic illumination. Therefore, the method remarkably improves the self-adaptive capacity and the multi-target comprehensive performance of the intelligent car lamp in a dynamic traffic environment, and breaks through the physical performance barriers between illumination, communication and safety. In order to achieve the above purpose, the present invention provides the following technical solutions: in a first aspect, the present invention provides a multi-objective optimization method for a vehicle lamp based on a particle swarm algorithm, the method comprising: Detecting the communication distance of a target object in front of the vehicle and the ambient illuminance of the current environment in real time, and acquiring the lowest emergent luminous flux threshold value in the current vehicle running mode; Under the forced constraint of meeting the minimum emergent luminous flux threshold, adopting a particle swarm algorithm to iteratively calculate a comprehensive evaluation function and outputting a globally optimal control parameter combination, wherein the globally optimal control parameter combination comprises optimal laser power, optimal beam divergenc