2104 results about "Illuminance" patented technology

The invention discloses a visible light and infrared image fusion method based on cross-modal dynamic collaboration. The method comprises the following steps: respectively extracting texture detail features of a visible light image and thermal radiation features of an infrared image through a visible light encoder and an infrared encoder; spatial alignment and channel complementarity optimization of cross-modal features are realized by using a heterogeneous attention collaboration module; and performing layered fusion on deep semantics and shallow detail features through a dynamic gating multi-scale decoder to generate a high-resolution fusion image. According to the method, the problems of feature dislocation, detail loss and unreasonable fusion weight distribution caused by modal difference in the prior art are solved, the detail fidelity, the thermal target saliency and the complex scene adaptability of the fusion image can be remarkably improved, and a high-robustness fusion result is provided for low-illumination environment perception and multi-modal target recognition.

The invention provides an improved YOLOv11s safety helmet wearing detection model and an optimization method thereof, and relates to the technical field of computer vision target detection. According to the improved YOLOv11s safety helmet wearing detection model and the optimization method thereof, the improved YOLOv11s safety helmet wearing detection model comprises the following modules: a multi-modal fusion module, a space-time analysis module, a domain adaptation module, a topological optimization module and a dynamic architecture module; and the multi-modal fusion module is used for realizing feature decoupling by adopting channel separation convolution based on input RGB and near-infrared images, fusing visible light and thermal radiation features through a dynamic weight distribution algorithm, implementing affine transformation alignment on multi-scale features by utilizing a spatial transformation network, and generating a multi-modal feature graph. Through fusion of visible light and near infrared spectrum features and implementation of dynamic weight distribution, complementarity of target texture and thermal radiation features under a complex illumination condition is enhanced, and the problem of feature distortion of single-mode data in a strong backlight or low-illumination scene is solved.

The invention discloses a low-light image enhancement method based on multi-scale frequency domain guidance and a double-branch attention mechanism. According to the method, a low-light image and a normal image corresponding to the low-light image serve as input, and illumination mapping and illumination features are extracted through a layer decomposition network; a reflection image mapping relation is learned in combination with a damage recovery network, and a preliminary enhanced image is generated; a learnable Fourier transform module is provided to realize frequency domain feature extraction, and low-frequency illumination information and high-frequency details are effectively separated; a dynamic double-branch attention mechanism is put forward, spatial domain and frequency domain features are fused, and collaborative modeling of structure and illumination perception is achieved; a multi-scale feature module is proposed to combine channel rearrangement and residual fusion to optimize feature expression; a U-Net framework is combined with a double-branch large kernel activation attention mechanism to complete image reconstruction, cross-scale feature fusion and detail recovery are achieved, and finally an enhanced image is output. According to the method, the problems of uneven illumination, detail loss, noise interference and the like in the low-light image can be effectively solved.

The invention provides a cooperative control method and system for a distributed intelligent lamp network, and the method comprises the steps: obtaining the real-time operation state data of each lamp in the intelligent lamp network, recognizing the failure position of the lamp, generating a fault node coordinate according to the failure position of the lamp, and obtaining the distribution information of the failure lamp; extracting spatial position data and current illumination parameters of surrounding lamps according to the distribution information of the failed lamps, calculating an illumination dead angle range and a local illumination decline region by adopting a geometric algorithm, and determining an affected light environment region range; acquiring communication time delay and response rate between the lamps, and if the communication time delay exceeds a preset time delay threshold, reducing the adjustment frequency and reallocating the compensation task to obtain an optimized cooperative compensation scheme; and extracting residual range data of the illumination dead angle according to the adjusted illumination distribution result, carrying out secondary adjustment on the illumination angle and the luminous intensity through an iterative algorithm, and determining a light environment continuity parameter after the illumination dead angle is eliminated.

The invention discloses an optical performance detection system and method for a camouflage material, and relates to the technical field of camouflage material detection, and the system comprises a structure identification and illumination control module which is used for collecting a surface image of a camouflage material sample, identifying the surface structure of the sample, starting a full-spectrum light source, and simulating step-by-step illumination from weak light to strong light; carrying out RGB imaging based on the standard illumination; and the region division module is used for applying a superpixel segmentation algorithm to the image, automatically dividing sub-regions, performing hyperspectral reflectivity scanning on each region, synchronously acquiring a light intensity image in a polarization direction, calculating a Stokes vector and constructing a polarization enhanced image. According to the method, frequency domain quantitative analysis of microcosmic interference fringes and accurate mask extraction of polarization abnormal areas can be realized, optical exposure risks of camouflage materials under complex illumination and visual angle conditions are effectively quantified through multi-angle reflection sampling and scoring function modeling, and the detection precision, the area pertinence and the automation level are remarkably improved.

The invention discloses an image enhancement method for brightness gain self-adaptive regulation and control under low illumination, and relates to the technical field of image data processing. The image enhancement method for brightness gain adaptive regulation and control under low illumination comprises the following steps: S1, acquiring original image data and auxiliary processing data under a low illumination environment for preprocessing, and constructing an image processing database after storing the original image data and the auxiliary processing data; s2, performing illumination distribution optimization and brightness correction operation through brightness deviation analysis, and outputting an illumination enhancement component; s3, performing de-noising and detail enhancement processing according to a noise color deviation result, and outputting a corrected reflectivity component; s4, through image quality enhancement analysis, using a loss function to guide a double U-Net architecture to carry out detail reconstruction and color optimization on the image; and S5, based on the quality feedback and the difficult sample, adjusting processing parameters and driving algorithm closed-loop optimization. The problems of insufficient image brightness, noise interference, color distortion and detail missing multiple degradation in a low-light environment are solved.

The invention discloses a container number tallying identification method, and relates to the technical field of container intelligent tallying. According to the technical key points, the method comprises the following steps: deploying a multi-modal sensing device, carrying out initial calibration, and pre-training an improved YOLOv7-tiny target detection model, a transformer-based cGAN character segmentation model and a lightweight CNN-transformer-capsule network integrated hybrid recognition model; through cooperative collection of multi-modal data of the multi-modal sensing device, the system can effectively integrate texture information of RGB images, a space structure of depth images and three-dimensional coordinates of point cloud data, the problem that traditional single visual recognition is affected by illumination, shielding and the like is solved, a self-calibration mechanism corrects space-time deviation of a sensor in real time, and the accuracy of recognition is improved. The system can still accurately extract container number characteristics in complex scenes such as strong light reflection, low illumination at night, character abrasion and the like.

The invention provides a low-light large dynamic image enhancement method based on dynamic weight and pyramid fusion. The low-light large dynamic image enhancement method is suitable for complex illumination imaging in security monitoring. According to the method, the global weight and the detail weight of an image are obtained according to the global average brightness, the local brightness and the gradient of an image sequence, and the total weight is obtained through weighting. And the halo phenomenon easily occurring in the fusion process is reduced through guide filtering. And combining Gaussian pyramid and ratio pyramid decomposition and weighting to obtain a final fusion image. According to the method, the problems of detail loss and limited dynamic range in the low-light scene are effectively solved. Experiments show that the method is superior to the prior art in information entropy, average gradient, edge strength, spatial frequency and other key indexes. The technology has an all-time adaptive capability, is suitable for the fields of intelligent transportation, urban security and protection, low-illumination monitoring and the like, and has a wide market prospect.

The invention relates to the technical field of virtual modeling, in particular to a lamp arrangement method and system based on virtual modeling and a medium. The method comprises the following steps: obtaining dynamic monitoring data of a target space, wherein the dynamic monitoring data comprises real-time illumination intensity data, people flow density data and ambient light change data; performing prediction modeling on the illumination demand of the target space according to the dynamic monitoring data to obtain illumination demand prediction data including expected illumination requirements and light distribution characteristics in different time periods; the adaptive light propagation grid is constructed in the digital twinborn space based on the illumination demand prediction data, grid division data are obtained, and the adaptive light propagation grid has high grid density in a region with severe illumination change and low grid density in a region with smooth illumination change. According to the method, comprehensive illumination analysis and evaluation can be carried out in the initial stage of design, and blindness and low efficiency in a traditional design method are avoided.

The invention relates to an indoor lighting rate simulation system for indoor design. The system comprises a dynamic meteorological simulation module which generates a dynamically changing sky model based on real-time meteorological data; the building attribute analysis module analyzes building optical attribute parameters by using the building information model data and the hyperspectral imaging data; the light ray tracing processing module performs light ray tracing based on the dynamic sky model and the building optical attribute parameters to obtain indoor glare distribution data; a reverse parameter optimization module generates window form parameters and sunshade device layout parameters according to the target illumination interval and the indoor glare distribution data; and the real-time visual verification module carries out visual rendering on the window form parameters and the sunshade device layout parameters to obtain an updated lighting rate simulation result for verifying the lighting effect of the design scheme. According to the system, through dynamic meteorological fusion, non-uniform material modeling and real-time optimization feedback, the precision and adaptability of indoor lighting simulation can be improved.

The invention belongs to the field of illumination light control, and relates to a dynamic tunnel illumination optimization method based on driver visual adaptation characteristics, which comprises the following steps of: establishing a relation model between a driver pupil area and ambient illuminance in a tunnel, and calculating a pupil area change rate according to the driver pupil area; establishing a corresponding relationship among the average glancing speed, the pupil area change rate and the ambient light in the tunnel according to the historical data; constructing a calculation model of the total average illumination of the lamps on the road surface in the tunnel based on the natural illumination outside the tunnel and the average horizontal illumination of the lamps on the road surface; an energy consumption and light pollution objective function and a driver psychological load objective function are established based on parameters such as total average illumination of lamps on a road surface in a tunnel and the length from an entrance or an exit, and multi-source data such as illumination, brightness, lamp power, pupil change rate and glancing speed are deeply fused, so that human-vehicle-environment multi-dimensional collaborative optimization is realized. The driving safety and the visual comfort of a driver are improved, and meanwhile, the energy consumption is considered.

The invention provides an infrared and visible light image enhancement fusion method and system for a low-illumination scene, and the method comprises the steps: obtaining an infrared image and a visible light image, and carrying out the preprocessing of the infrared image and the visible light image; inputting the brightness image and the illumination prior image into an encoder, and performing single-mode reconstruction pre-training through a minimization reconstruction loss function; performing deep feature extraction on the dual-channel visible light image and the normalized infrared image by using a pre-trained encoder, performing bidirectional feature guidance and fusion in a fusion module in a manner of exchanging query vectors by using a cross-modal cross attention mechanism, and reconstructing the fused features by using a pre-trained decoder; according to the method, low-illumination image enhancement and cross-modal feature fusion are integrated into a unified framework, brightness improvement, detail reservation and modal consistency synchronous optimization are realized through end-to-end joint modeling, and information loss caused by enhancement and fusion process separation is avoided.

The invention relates to the technical field of liquid crystal display, in particular to a liquid crystal display driving control method and system. Comprising the following steps: SS01, multi-modal sensing and state triggering: fusing a user distance, a fixation point coordinate and ambient light illumination in real time by using an integrated sensor, and triggering an eye protection mode or a self-adaptive sleep mechanism according to the user distance, the fixation point coordinate and the ambient light illumination, SS02, image partitioning and strategy generation: dividing a screen into N * M partitions, and SS03, distinguishing a dynamic region from a static region according to the brightness histogram and the motion vector of each partition, and outputting differentiated refresh rates and backlight strategies, and SS03, performing big data learning and model optimization, and collecting user behaviors. The system has the advantages that the infrared proximity sensor, the eyeball tracking sensor, the distance sensor and the ambient light sensor are integrated through the multi-mode sensing and state triggering module, multi-dimensional data such as user distance, fixation point coordinates and ambient light illuminance are fused in real time, and active intelligent sensing of the user state and the environment is achieved.

The invention discloses an image denoising and self-adaptive enhancement method based on pixel discretization and illumination guidance, and relates to the technical field of image processing, and the method comprises the steps: carrying out the down-sampling and noise reduction of an input image, and obtaining a preliminarily denoised image through the deconvolution operation, fuzzy pixel discretization and residual serialization; estimating an illumination component from the preliminarily denoised image by using an illumination estimation network; calculating a reflection image based on the estimated illumination component and the preliminarily denoised image according to an image enhancement theory; based on a dynamic splicing mechanism, splicing the reflection image and the illumination component in the channel dimension to obtain a spliced image; and with keeping of image details and brightness information as a criterion, denoising and enhancing are carried out on the spliced image through a reflection denoising network, and a denoised and enhanced image is output. According to the invention, high-quality recovery of low-illumination images is realized, and the intelligent level of an electric power inspection system is improved.

The invention provides an intelligent blind person navigation system, and relates to the technical field of intelligent wearable blind person navigation, the intelligent blind person navigation system comprises a multi-mode sensing unit, an A I decision center, a bidirectional feedback module and an emergency response system.According to the intelligent blind person navigation system, through a three-layer sensing architecture of a dual-light fusion camera, a laser radar and an ultrasonic array, 94% of obstacle detection rate is still kept under 5lux low illumination, and the blind person navigation efficiency is improved. And the effective detection distance reaches 8 meters. The semantic segmentation algorithm enables the detection rate of the suspended obstacle to rise from 12% to 92%, and the recognition error of the glass door in the underground garage scene is only 2.1 cm. And a multi-sensor space-time alignment technology is adopted, so that the false alarm rate in a complex scene is reduced from 25% to 3.2%, and all-weather and full-height accurate sensing of obstacles is realized. A biological characteristic + environmental risk multi-stage triggering mechanism is adopted, automatic alarming and shared positioning are performed within 2.1 seconds when a user falls down, and the success rate of rescue for falling down of the underground garage is increased from 62% to 100%. An environment video with positioning is automatically recorded without operation timeout, and a'prevention-early warning-rescue 'closed-loop safety net is constructed.

The invention relates to a light sensation self-adaption and white balance cooperative adjustment method and device for an LED display screen, and the method comprises the steps: collecting the illumination value of current ambient light and the spectral distribution of the ambient light, and calculating the color temperature value of the ambient light; according to the color temperature value, inquiring a pre-stored color temperature-gain parameter corresponding relation library to determine a corresponding white balance gain parameter; according to the illumination value, a backlight brightness adjustment amount is calculated through a pre-constructed logarithm compensation model; associating and binding the white balance gain parameter and the backlight brightness adjustment amount, and outputting the white balance gain parameter and the backlight brightness adjustment amount to a pulse width modulation signal generation unit and a digital-to-analog converter through a microcontroller; and outputting the white balance gain parameter to a digital-to-analog converter, outputting the backlight brightness adjustment amount to a pulse width modulation signal generation unit, generating a synchronous adjustment signal to drive an LED display unit, and carrying out cooperative adjustment of backlight brightness and white balance so as to achieve the purpose of unified regulation and control of visual comfort and color rendition authenticity.

The invention relates to the technical field of intelligent maintenance of tunnel electromechanical facilities, in particular to a tunnel lamp recognition and spatial positioning method based on binocular vision, which comprises the following steps: synchronously acquiring tunnel environment images through left and right cameras subjected to polar horizontal correction, inputting the images into a lightweight tunnel lamp detection model, and detecting an area image containing a lamp bounding box; extracting feature points in the bounding box by adopting a multi-scale adaptive FAST feature extraction algorithm, describing the feature points by rotating an invariant binary descriptor, and generating a disparity map in combination with an adaptive Hamming distance matching algorithm; calculating three-dimensional space coordinates of the lamp according to the disparity map by using a binocular camera triangulation principle and combining camera parameters; when the ambient illumination is lower than a threshold value, a multispectral image fusion module is started to enhance the feature matching robustness; and when continuous matching fails, visible light communication assisted positioning is started. According to the invention, the problem of low positioning precision of the lamp in a complex tunnel environment is solved, and the reliability and efficiency of identification and positioning are improved.

The invention relates to the technical field of illumination design, and discloses a transformer substation indoor illumination design method, system and device and a storage medium, and the method comprises the steps: obtaining real-time data, spatial feature data and meteorological data of a transformer substation, inputting the data into an illumination prediction model, and obtaining the illumination demand value of each region of the transformer substation; inputting the illumination demand value and the environmental parameters into a lamp selection model to obtain a lamp layout initial scheme; establishing a three-dimensional simulation model of the transformer substation according to the transformer substation design drawing and the lamp layout initial scheme, and performing iterative simulation to obtain a lamp layout optimization scheme; performing clustering analysis on the lamp layout optimization scheme by adopting a clustering algorithm to obtain a plurality of lamp groups, and setting an independent power distribution loop for each lamp group; and generating a control strategy corresponding to each lamp group according to the illumination demand value. According to the invention, the design precision, the energy efficiency level and the operation and maintenance convenience of the lighting system are remarkably improved by fusing dynamic prediction, multi-objective optimization and intelligent control technologies.

The invention provides a low-illumination perception method and system based on space-frequency fusion, and relates to the technical field of image processing, and the method comprises the steps: obtaining a normal light image and a low-illumination image corresponding to the normal light image; performing feature extraction on the normal light image and the low-illumination image through an encoder to obtain multi-scale spatial features; decomposing the multi-scale spatial features through stationary wavelet transform to generate a low-frequency feature component and a high-frequency feature component; performing optimization processing on the low-frequency characteristic component and the high-frequency characteristic component based on a parameter learnable frequency domain adaptive filtering mechanism; fusing the optimized low-frequency feature component and the optimized high-frequency feature component by introducing a cross attention learning mechanism to obtain a frequency domain feature; performing adaptive complementary fusion on the frequency domain features and the multi-scale spatial features to generate fusion features; step-by-step up-sampling is carried out on the fusion features through a decoder; and outputting a segmentation result of the low-illumination image through a sensing head according to the fusion features after up-sampling.

The invention relates to the technical field of underwater image enhancement, in particular to an underwater image enhancement method based on balance, correction and deblurring and application. The balance module is cascaded by color balance and illumination balance, and the color balance performs compensation balance on RGB (Red Green Blue) color channels of the underwater image based on a white balance algorithm of red channel compensation; according to illumination balance, an improved contrast-limited adaptive histogram enhancement method is used for enhancing illumination of a low-illumination area of an underwater image, and meanwhile illumination of a high-illumination area is restrained, so that illumination balance is achieved. In the correction module, a color and illumination composite correction method combining color constancy and an illumination diagram is provided so as to correct the color and illumination of the underwater image at the same time. And in the deblurring module, the image is deblurred by adopting a multi-scale anti-sharpening mask, so that the definition of the underwater image is improved.

The invention belongs to the technical field of photo-thermal performance detection of building envelope structures, and particularly relates to a light-transmitting roof photo-thermal performance detection device under various meteorological conditions and a detection method of the light-transmitting roof photo-thermal performance detection device. The invention discloses a light-transmitting roof photo-thermal performance detection device under various meteorological conditions. The device comprises a heat metering device, an air supply and return system, a water supply and return system, a control system and an external environment simulation device, wherein the external environment simulation device comprises an external environment box and an artificial light source; the light-transmitting roof test piece is installed on the lower side wall of the external environment box, and the external environment simulation device and the spraying device are used for simulating the environment conditions of different steady-state meteorological conditions and dynamic meteorological conditions under the spraying condition; the spraying device comprises a nozzle; a first light source radiation intensity sensor, a second light source radiation intensity sensor and a first illuminance sensor are further arranged in the outer environment box. According to the invention, the photo-thermal performance of the light-transmitting roof under the spraying condition can be accurately evaluated.

The invention relates to the technical field of intelligent dimming, in particular to an intelligent projector light source power dynamic adjustment method, which comprises the following steps of: acquiring an image through an image sensor, constructing a pyramid, dividing grids, aggregating HSV-V values by adopting a sliding window, outputting a brightness variation rate, and comparing a dynamic threshold value to generate an adjustment priority; and calculating a compensation coefficient in combination with an environment illumination interpolation to generate a correction weight parameter, extracting a main peak region span fusion output brightness structure stability, performing weighted fusion after normalization to obtain a driving current proportion, performing scaling to generate an adjustment parameter, and sending and executing the adjustment parameter. According to the method, the light source power is regulated and controlled through multi-dimensional data fusion, brightness variation is extracted through an image pyramid and a sliding window, a dynamic threshold value is used for screening an adjusting area, linear interpolation is used for fusing environment illumination, a histogram is used for detecting a main brightness interval, abnormal fluctuation is restrained, driving current is normalized and balanced, the response speed is increased, and the energy efficiency is optimized; and the picture stability and the visual comfort are enhanced.

The invention discloses a lamplight simulation test control system for landscape lighting, relates to the technical field of lamplight control, and is used for solving the problem of poor optimization of intelligent lamplight control. Structured time series data are constructed through holiday and festival identification functions, weather states, sunshine information and people flow density, nonlinear mapping is performed after unified sampling and sliding window alignment, and a tensor type input data set is generated; state recursion and context adjustment are carried out by combining a gating circulation unit with a space situation embedding vector, and illumination, color temperature and control level prediction sequences at multiple moments in the future are output; and constructing a control fragment path according to an illumination change trend and a color temperature sign function, collecting current illumination, flow density and an equipment response state to generate deviation information, executing a feedforward and feedback fusion strategy, outputting a control instruction set including the illumination, the color temperature, a lamp on-off state and a mode label, and realizing adaptive and dynamic light control.

A system for automatically adjusting the lighting parameters of a space is disclosed herein. The control module sets the target lighting parameters in the space and drive the smart lamp groups to illuminate by using the preset light recipe and to control the smart lamp groups to perform the illumination. The control module compares an ambient light detection value in the actual environment with the target value. When the ambient light detection value is not the same as the target value, the control module drives the ambient light sensor module performs a dimming procedure to the smart lamp groups. Wherein, the dimming procedure includes the step of adjusting an illuminance uniformity of the space according to the actual illuminance detected by the ambient light sensor module. After the calculation of the illuminance uniformity formula, the illuminance uniformity is adjusted to be the same as a target illuminance uniformity.

The invention discloses an illumination adaptability rock crack identification and calibration method based on an image identification algorithm, and relates to the technical field of geotechnical engineering intelligent image identification, and the method comprises the following steps: collecting an illumination incident angle, a polarization state and an illumination gradient corresponding to an image frame, building an illumination time sequence observation layer, and obtaining an illumination time sequence observation layer; forming an illumination feature baseline for crack boundary tracking; and performing coherent phase decomposition on the image sequence based on the illumination feature baseline, stripping mirror reflection and enhancing diffuse reflection texture, and generating a crack drift suspicion image. Through construction of an illumination time sequence observation layer and coherent phase decomposition, illumination interference stripping in a crack image is realized, false drift is eliminated by combining anti-fact playback and time reconstruction, and a dynamic threshold control mechanism is constructed by introducing an anti-illumination mask and an anti-phase pattern and linking a polarized light diaphragm and a shading array. And high robustness of crack identification and closed-loop regulation and control of false early warning are realized.

The invention relates to the technical field of artificial intelligence, is suitable for financial and medical scenes, and particularly relates to a low-illumination physical examination image noise reduction enhancement method, device and equipment and a storage medium, the low-illumination physical examination image noise reduction enhancement method comprises the following steps: obtaining a to-be-processed low-illumination physical examination image; based on a Retinex decomposition model, acquiring a preliminarily estimated illumination component of the low-illumination physical examination image, acquiring an accurately estimated illumination image according to the preliminarily estimated illumination component, and enhancing the accurately estimated illumination image through gamma transformation to obtain an enhanced image; performing noise reduction on the enhanced image based on a BM3D algorithm to obtain a noise-reduced image; and combining the enhanced image and the noise reduction image to obtain a noise reduction enhanced image. The problem that in the prior art, when enhancement processing is carried out on the low-illumination physical examination image through an image enhancement algorithm, synchronous processing is not carried out on noise and amplified noise in the enhanced physical examination image is solved, and the accuracy and efficiency of physical examination image recognition are greatly improved.

The invention discloses a nighttime image enhancement method based on multidirectional information extraction and color loss optimization, which comprises the following steps: firstly, collecting nighttime and normal light road scene images, constructing a nighttime road image enhancement model based on deep learning, and adopting an image enhancement framework based on GAN and a superficial layer semantic enhanced U-Net network as a generator; the five-layer convolutional network is used as a discriminator, then horizontal-vertical bidirectional differential convolution is used to extract multi-directional information of the image, a local illumination attention module is used to balance dark area brightness and bright area overexposure, and finally an adaptive color loss function is used to recover original colors of the image. The method can effectively solve the problems of image quality reduction, detail loss, color distortion and the like under the low-illumination condition, and is particularly suitable for night scene visual perception improvement in the field of vehicle-assisted automatic driving.

The invention provides a lamp layout method and a lamp, and relates to the technical field of intelligent lighting. Personnel activity data are generated by acquiring real-time signal data acquired by a sensor in a target space and performing clustering analysis and behavior recognition. And generating target illumination and color temperature data of each region through biological rhythm correlation modeling and time sequence prediction based on personnel activity data and historical rule data. And performing multi-target weight dynamic optimization and improved genetic algorithm solution by adopting the generated data and personnel movement characteristics to obtain a lamp control strategy. And performing priority division and differential driving adjustment in combination with the real-time distribution of the personnel and the aging state of the lamp, and generating illumination feedback data. And iteratively executing adjustment based on the feedback data, the target illuminance, the color temperature data and the adjacent lamp collaborative error optimization control parameters. Illumination distribution is optimized by dynamically adapting to personnel activity rules, real-time illumination requirements can be accurately matched to reduce energy consumption and improve utilization efficiency, and scene adaptability can be considered.

The invention discloses an illumination intensity self-adaptive tunnel illumination photovoltaic power supply system and an implementation method, and the system comprises a photovoltaic power generation panel which outputs a DC voltage to supply power to tunnel LED lamps, the illumination of each LED lamp is adjusted through a respective LED light modulator, the LED light modulators are connected with a PLC through a 3 # bus unit CAN3, the PLC transmits an illumination adjustment signal to the LED light modulators through the 3 # bus unit CAN3, and the LED light modulators are connected with the photovoltaic power generation panel. The output power can be adjusted from 0% to 100%, so that the illumination of the LED lamp is changed from 0% to 100%; an illuminometer is mounted outside the tunnel to detect the ambient illuminance outside the tunnel; a plurality of illuminometers are installed in each illumination area in the tunnel, and the actual illuminance of each area can be detected; all the illuminometers are connected with the PLC through the 1 # bus unit CAN1, data of each illuminometer is transmitted to the PLC in real time through the 1 # bus unit CAN1, the characteristics of photovoltaic power generation and tunnel illumination are combined, the illumination intensity outside a tunnel is detected, the illuminance of lamps in all areas in the tunnel is dynamically adjusted, and the illumination of all the areas is switched on and off according to the traffic flow and the vehicle position.

The invention discloses an image edge feature enhancement correction fusion method based on a guide filter, and aims to solve the problems that detail features of a low-illumination visible light image and an infrared image are not obvious, focusing edge information is not clear, and registration of a multi-focus image is wrong. The invention provides an edge feature enhancement correction fusion method based on a guide filter. In the illumination enhancement stage, a visible light image is divided into a base layer and a detail layer through a guide filter, and the image contrast and detail information are enhanced. For an infrared image, an infrared background is reconstructed by using a quadtree decomposition and Bezier interpolation method, unclear focusing edge feature information is extracted, and the visibility of image details is enhanced. And finally, reconstructing the two processed images by using a multi-scale weighted gradient method, and performing fusion by calculating large-scale and small-scale weight scales.