222 results about "Low level light" patented technology

The invention relates to the technical field of display, and particularly discloses a dynamic ambient light adaptive OLED display optimization method and system, and the method comprises the steps: obtaining an ambient light time sequence data set of an environment where an OLED display screen is located, and synchronously obtaining real-time display parameters and historical brightness adjustment data of the display screen; acquiring ambient light sensing features according to the ambient light time sequence data set; obtaining photo-thermal structure coverage area data of the OLED display screen according to the real-time display parameters; and acquiring preliminary optimization parameters according to the ambient light sensing characteristics and the photo-thermal structure coverage area data. According to the invention, illumination intensity, spectrum drift and contrast change are captured in real time in combination with ambient light perception characteristics, the one-sidedness of adjustment only according to brightness traditionally is solved, reflection can be reduced through photothermal structure deformation under strong light, brightness can be improved to offset interference, and human visual characteristics can be matched under weak light. And the display definition and the visual comfort in different environments are effectively improved.

The invention discloses a weak light image enhancement method fusing noise adaptive diffusion and illumination perception, and the method comprises the steps: firstly designing a reflection-illumination decomposition module RID based on a Retinex theory, and extracting a reflectivity component and an illumination component of an input weak light image; then designing an ADRD (adaptive diffusion reflection denoising) module, performing denoising enhancement on the reflectivity component, and outputting an enhanced reflectivity image; introducing a noise estimation network, and dynamically adjusting the de-noising amplitude of the diffusion model according to the noise intensity of the region in the reflectivity graph; designing an illumination perception enhancement module IEM, fusing an attention mechanism and a local enhancement block, enhancing the illumination image, and outputting an enhanced illumination component; and building a complete neural network framework, carrying out joint training on the provided module, finally testing the stored neural network model, and outputting an enhanced image. According to the method, the problems of detail loss, noise residue and uneven illumination of the enhanced image in the prior art are solved.

The invention discloses an automobile central control screen small target detection method based on YOLOv11 improvement, and the method specifically comprises the steps: S1, generating an image data set, carrying out the preprocessing and enhancement, and dividing the data set; s2, an improved C3k2GCConv module, a WFU module and a CGAFusion module are introduced, and a YOLOv11 network model is constructed; s3, training the model by adopting a cosine annealing learning rate and a mixed precision training strategy; s4, inputting a to-be-detected central control screen image into the improved YOLOv11 detection model, and outputting the category and bounding box coordinates of a target; and S5, performing screening and optimization through a post-processing module, and finally outputting a detection result in the form of a bounding box and a category label. According to the method, by improving the YOLOv11 model, a display target can be effectively recognized in complex environments such as strong light direct incidence, screen reflection and dim light, and the stability and robustness of the model in a complex illumination scene are improved.

The invention discloses a method for detecting and tracking a dynamic obstacle in a weak light environment. The method comprises the following steps: 1) acquiring an image; (2) a U-depth detector and a DBSCAN detector are used for conducting rapid detection on a near obstacle, and after weak light images of a far obstacle are enhanced through a lightweight image enhancement network LPF, a lightweight FastDet model is input to conduct deep learning auxiliary detection; 3) calculating the intersection-to-union ratio of the U-depth detector, the DBSCAN detector and the LPF-FastDet detector, fusing the intersection-to-union ratio results of the U-depth detector, the DBSCAN detector and the LPF-FastDet detector, and generating a set bounding box; 4) performing data association on the detected obstacles through a feature association method, and performing continuous tracking by using Kalman filtering; and 5) identifying a dynamic obstacle from all detected obstacles, and outputting the size of a three-dimensional bounding box of the dynamic obstacle and the center position coordinates of the dynamic obstacle in the unmanned aerial vehicle coordinate system.The method provides a high-precision and low-delay solution for obstacle perception of the unmanned aerial vehicle in a weak light environment, and is suitable for different scene requirements.

The invention relates to the technical field of atmosphere lamp control, in particular to a dynamic atmosphere lamp control method and system based on music rhythms. The method comprises the following steps: collecting a real-time audio signal stream, carrying out multi-scale wavelet decomposition and audio rhythm hierarchical structure perception, and constructing an audio rhythm feature perception map; performing spatial audio azimuth analysis based on the real-time audio signal stream, and performing adaptive dynamic atmosphere lamp parameter control based on the audio rhythm feature perception map to obtain an adaptive dynamic atmosphere lamp control strategy; according to the self-adaptive dynamic atmosphere lamp control strategy, initial atmosphere lamp control is carried out, a real-time image in the area is obtained, weak light intensity area coverage compensation optimization is carried out, and a dynamic atmosphere lamp space lighting effect optimization strategy is constructed; personalized natural physiological rhythm mining and physiological-music resonance frequency fine tuning are carried out, and user physiological-music resonance frequency fine tuning parameters are generated. According to the invention, high-response atmosphere lamp control which dynamically adapts to the light effect preference change of the user is realized.

The invention belongs to the technical field of image processing, and particularly discloses a low-light environment image adaptive enhancement method based on unmanned aerial vehicle inspection, which comprises the following steps: preprocessing a low-light RGB image shot by an unmanned aerial vehicle; the preprocessed low-light RGB image is converted to an HSV color space, and brightness and color components are obtained through separation; performing dual-channel attention mechanism processing: respectively optimizing brightness and color components by adopting a brightness branch and a color branch; wherein the brightness branch enhances the dark part contrast through a convolutional network, and the color branch suppresses a color channel with significant noise based on a channel attention mechanism; and reconstructing the optimized color and brightness components into an HSV color space, and converting the HSV color space into an RGB image format to realize brightness enhancement and color denoising. The problems of overexposure, color distortion, excessive noisy points and the like existing in the image shot by the unmanned aerial vehicle camera in the low-light environment in the prior art can be effectively solved, and the image quality under the night or weak light condition is improved.

The invention discloses a road target detection method and device based on bimodal feature fusion and weak light enhancement, and relates to the field of target detection. And the weak light enhancement target detection model performs enhancement and target detection on the road image under the weak light condition. And the bimodal target detection model performs target detection on the road image under the normal light condition. The weak light enhancement target detection model carries out feature extraction through a multi-scale feature attention module, and carries out image feature brightness enhancement through a brightness enhancement module. The image quality is improved, the image contrast is kept, and the target detection accuracy of the weak light image is greatly improved. The bimodal target detection model adopts a double-branch training strategy, the rich capture capability of visible light on target color and texture features under a normal illumination condition is fully exerted, the advantage of a target contour is highlighted in combination with the penetration characteristic of infrared light, and the target recognition capability under the conditions of complex background, shielding and long-distance blurring is remarkably improved.

The invention relates to the technical field of image recognition, in particular to a horticultural product fungal disease image recognition system and method, and the system comprises an image collection module, an image segmentation module, an anomaly detection module, a feature extraction module, a disease recognition module and a decision output module. In the prior art, the intensity of a light supplementing lamp is manually adjusted, and instantly changing illumination conditions (such as cloud layer shielding and tree shadow shaking) are difficult to deal with in an open-air plantation, so that leaf scab details are often submerged by strong light or dim light is blurred; according to the scheme, a closed-loop linkage system of ambient light perception and an annular LED is innovatively deployed, and the light supplementing intensity is reversely adjusted in a millisecond level by tracking ambient brightness in real time, so that purple gray hyphae of downy mildew in mist in the morning and anthracnose black spots under the noon burning sun present completely consistent color saturation and texture definition in imaging; and the problem of misdiagnosis caused by illumination fluctuation in the traditional method is thoroughly solved.

The invention discloses a multi-scene fire early warning and identification method and system. The method comprises the following steps: acquiring a fire monitoring video stream, and decoding the video stream into a continuous frame sequence; performing multi-scale preprocessing on the frame sequence; according to the preprocessed frame sequence, fire feature time sequence enhancement processing is carried out, and a standardized feature set D is obtained; performing multi-level feature coding and aggregation on the standardized feature set D to obtain a fire feature vector set; performing fire detection of different scenes according to the fire feature vector set V; wherein the different scenes comprise a closed space, an open space, a semi-open space, a strong light environment, a weak light environment and a normal light environment; in order to solve the problem that early warning cannot be realized due to the fact that existing fire detection is difficult to effectively recognize weak features such as spark splashing and rarefied smoke in the early stage of a fire, the method and the device specially recognize and enhance the features of flames and rarefied smoke in the early stage, fully utilize time sequence information to enhance weak signals and the like, and improve the detection precision of small targets in the early stage of the fire.

The invention provides an automatic illumination adjusting method and system based on seed and seedling cultivation, and relates to the technical field of illumination control and intelligent agriculture, the method comprises the following steps: firstly, collecting seedling chlorophyll fluorescence parameters to construct a light stress index, and accurately identifying a light inhibition risk; then calculating a physiologically permissible light intensity threshold adaptive to the dark response capability of the plant by combining the environment temperature and the CO2 concentration; positioning a shielded weak light area by using three-dimensional point cloud voxelization and a ray tracing technology, and calculating a shadow compensation gain coefficient; and finally, generating a control matrix in combination with a dynamic left-rotation light strategy, and driving an independent addressing LED array and a variable-angle lens to execute local precise light supplement and light vector adjustment. Through physiological feedback and spatial light field reconstruction, the problem that traditional illumination control neglects individual tolerance difference and canopy shielding is solved, photooxidation damage is effectively prevented, and the growth uniformity and the light energy utilization rate of seedling groups are remarkably improved.

The invention discloses an aerial target detection method based on dynamic attention and double-frequency feature enhancement. The method comprises the following steps: 1) extracting multi-scale features by using a backbone network in combination with a low-light feature fusion module, and enhancing detail expression in a low-light and low-contrast scene; 2) sparse modeling is carried out on deep features through a dynamic sparse attention module, key attention connection is reserved, and the global semantic ability is improved while the calculation amount is reduced; 3) inputting the deep enhanced features and the shallow features into a double-frequency feature enhancement module, and respectively modeling low-frequency background and high-frequency details to realize foreground highlighting and background suppression; according to the unmanned aerial vehicle aerial image target detection method, the problems of low illumination, complex background and small target detection are effectively solved, and the precision and robustness of unmanned aerial vehicle aerial image target detection are remarkably improved.

The invention relates to the technical field of electric connector weak light assembly vibration compensation alignment methods, particularly discloses an electric connector weak light assembly vibration compensation alignment method, and aims to solve the problems of low electric connector assembly positioning precision and high pseudo soldering rate caused by vibration in a weak light environment. The method comprises the following steps: monitoring the thermal radiation change of a vibration source in real time through an infrared thermal imaging sensor, and pre-judging a vibration acceleration vector in combination with a lightweight long-short-term memory neural network; synchronously triggering dual polarization angle imaging to respectively capture a housing reference mark and an enhanced terminal metal feature; the vibration data and the polarization image flow are fused in the field programmable gate array, a self-adaptive optical flow algorithm is adopted to resolve the displacement compensation amount, and a piezoelectric ceramic actuator is driven to carry out submicron order reverse displacement compensation; when the compensation amount exceeds a threshold value, a low-delay correction instruction is sent to the robot through an Ethernet control automation technology bus, and the steady-state error is ensured to be smaller than 0.3 micrometer by combining a strain gauge fine tuning feedback loop. According to the technical scheme, the assembling precision, efficiency and production line adaptability are remarkably improved.

The invention relates to a gradient migration and low light enhancement collaborative infrared and visible light image fusion method. The method comprises the following steps: preprocessing an infrared image and a visible light image, converting the preprocessed visible light image into an HSV color space, and extracting a brightness component; performing low-light enhancement and high-light suppression on the brightness component by using a complementary logarithmic gamma function to obtain an enhanced brightness component; carrying out sharpening processing on the enhanced brightness component by adopting a brightness protection dynamic histogram equalization technology to obtain an optimized brightness component; and based on a gradient transfer fusion algorithm, fusing the component with the infrared image to obtain a fused brightness component, merging the fused brightness component with a hue component and a saturation component of the visible light image, and converting into an RGB color space to obtain a fused image. According to the method, through collaborative optimization of gradient migration and low light enhancement, the quality of the fused image under the condition of low light at night is remarkably improved, and the target saliency and the detail retention capability are enhanced.

A self-adaptive dynamic sunshade PV skylight is provided with a window body assembly of a hollow cavity structure, the window body assembly is composed of an outer side glass slide and an inner side glass slide, and a thermochromic layer is arranged on the inner surface of the outer side glass slide and used for automatically reducing the light transmittance when the environment temperature rises so as to achieve passive sunshade. An electrically controllable optical layer is arranged on the outer surface or the inner surface of the inner side glass slide and is used for realizing active and partitioned optical performance adjustment under the driving of the controller and acquiring environmental parameters such as external illumination intensity, incident angle, indoor temperature and humidity and cavity condensation risk in real time. The strip-shaped micro photovoltaic array is arranged on the edge of the window frame, the segmented bypass wiring and the weak light MPPT technology are combined, solar energy can still be efficiently collected even in the local shielding or cloudy day low-illumination environment, and it is ensured that the system stably runs for a long time under the condition that no external power source is connected in cooperation with the super-capacitor energy storage unit.

The invention discloses a wavelet Mama weak light image enhancement method based on illumination prior, and the method comprises the following specific steps: 1, constructing an illumination estimation module, taking a weak light image as input, and obtaining illumination quaternary prior; step 2, constructing a wavelet Mama coding and decoding network, and inputting the weak light image and the illumination quaternary priori into the wavelet Mama coding and decoding network to obtain an enhanced image; step 3, training an overall network composed of the illumination estimation module and the wavelet Mama coding and decoding network to obtain a trained overall network; and step 4, inputting a to-be-enhanced weak light image into the trained overall network to obtain a final enhanced image. According to the method, the naturalness and details are kept while the image is enhanced, and the quality of the enhanced image is high.

The invention discloses an additive manufacturing process high dynamic range imaging method and system based on a multi-view light field. Firstly, an original light field image in the additive manufacturing process is collected through a light field camera; extracting R, G and B single-wavelength channel images from the original light field image by the established light field Bayer model; then, taking the R-channel image as a reference, and adopting a K-means clustering algorithm for segmentation to obtain a low-light area and a high-light area in the R-channel image; and finally, fusing the pixel value of the G channel image corresponding to the low-light area with the R channel image to enhance weak light details, and fusing the pixel value of the B channel image corresponding to the highlight area with the R channel image to suppress highlight, thereby reconstructing to obtain an HDR image. According to the method, the image quality and the information integrity monitored in the additive manufacturing process are effectively improved, and high-quality visual data are provided for additive manufacturing process optimization and defect recognition.

The invention relates to a 12M large-wide-angle security monitoring lens which sequentially comprises a diaphragm and first to ninth lenses from an object side to an image side, the first and second lenses have negative diopters, the third, fourth, sixth to ninth lenses have positive diopters, and the fifth lens has negative diopter. The aspheric lens is matched to correct aberration and suppress distortion. The working wave band of the lens covers 435-1000 nm, the F number is about 1.3, the maximum imaging circle diameter is about 16.8 mm, the TTL does not exceed about 22 mm, the relative illumination can reach about 39%, the main light angle is about 23 degrees, the lens is adaptive to a 12M large-size sensor, full-view-field high resolution, low distortion and clear imaging under weak light / infrared supplementary light are achieved, and the lens is suitable for security and protection monitoring equipment.

The invention provides a port container loading and unloading positioning method, system and equipment and a medium, and belongs to the technical field of port automation and machine vision positioning. The method comprises the following steps: acquiring a container surface image and synchronous sling motion data; the jitter amplitude is evaluated, the image is combined to analyze the illumination condition, and the multispectral image is adaptively selected or fused to generate an anti-interference image; angular point coordinates are extracted, the angular point coordinates and motion data are input into a vision-inertia tight coupling optimization model together for state estimation, and the real-time pose is solved; driving a lifting appliance-load dynamic model by using the motion data, and predicting pose deviation generated by inertia and swinging in the future; and the feedback control quantity based on the real-time pose and the feed-forward compensation quantity based on the prediction deviation are fused, and an accurate control instruction for driving the lifting appliance is generated. The problems of image quality degradation and feature loss caused by strong light, weak light and sling shaking are solved, and high-precision and high-robustness container positioning and alignment in a complex dynamic environment are achieved.

The invention discloses a track video inspection method and system based on deep learning, and relates to the technical field of video inspection, and the method comprises the steps: obtaining map information, analyzing the effective shooting width of unmanned aerial vehicle inspection, defining track boundary constraint, and analyzing the effective width of track shooting, so as to determine different flight path paths; the fitness value of the flight path is evaluated to determine the optimal path, the turning energy consumption and the stable flight energy consumption of the optimal path are analyzed, and step-by-step coordinate points are added to determine the track segment of the inspection task. According to the method, the segmented task set is further optimized and combined with the real-time energy consumption to form the track segmented task set, so that the effect of converting a long path into a small-scale inspection task of a plurality of sub-paths is achieved; it is guaranteed that the track collection task can still be efficiently executed under the condition that the illumination condition is insufficient, and definition of the weak light collection task set provides clear guidance for follow-up height adjustment and infrared light supplement optimization.

The application discloses a gas heater control system capable of automatically adjusting the size of a flame, comprising a main controller, an infrared sensing device, a gas control valve, a burner and an ambient temperature sensor and the like. The infrared sensing device senses the presence state, distance and height information of a human body, and the ambient temperature sensor monitors the ambient temperature; the main controller generates a multi-stage or continuous gas flow instruction based on the above information, controls the gas control valve to adjust the size of the flame, and can also adjust the total flow according to the number of human bodies. The system is equipped with a lifting device, which can automatically adjust the height of the burner according to the sitting or standing posture of the human body; a wind speed sensor is arranged, and when the flame is extinguished, the wind speed is combined to judge whether to delay ignition; multiple protections such as a tilt switch, an audible and visual alarm and the like are arranged, and an auxiliary illuminating lamp is started to enhance the sensing performance in weak light. The system realizes intelligent and accurate temperature control, improves the safety and scene adaptability, is energy-saving and convenient.

The application discloses a low-light target detection method based on a double-layer optimization theory, which significantly improves the enhancement quality and detection accuracy. The method first adopts a double-layer learning architecture to bridge the enhancement and detection tasks, and organically combines the low-light image enhancement task and the target detection task. Secondly, the enhancement network parameters are taken as the main optimization target, and the detection network parameters are taken as the constraint condition. The enhancement network generates images that are conducive to machine perception. Finally, in order to improve the training efficiency, a method based on the implicit function theory is proposed to approximately solve the corresponding gradient. The training cost is effectively reduced, and the training speed is improved. The application significantly improves the target detection accuracy in weak light scenes, and can be applied to various target detection tasks in weak light scenes, such as night pedestrian recognition, automatic driving, security monitoring and the like.

The utility model provides septic tank identity card code acquisition and identification equipment, which comprises an equipment main body, a display screen is arranged on the front side of the equipment main body, a scanning assembly is arranged on the rear side of the equipment main body, the scanning assembly comprises a camera module, an auxiliary front sight module and a light supplementing module, and a light shielding plate is detachably arranged on the rear side of the equipment main body. According to the utility model, shielding can be flexibly adjusted according to illumination conditions, the problem of screen light reflection caused by direct sunlight is effectively avoided, the operation time cost in a strong light environment is greatly reduced, and the continuity of a code scanning process is ensured; meanwhile, through a built-in light supplementing module and an auxiliary front sight module, a precise recognition system under a low light condition is constructed, the problem that a code scanning area is difficult to position under a weak light environment is solved, low-efficiency operation of traditional equipment depending on experience exploration is avoided, code scanning time consumption is shortened, and operation efficiency under a complex light environment is remarkably improved.

The utility model discloses a water level monitoring mark device for water conservancy engineering investigation relates to water level monitoring technical field. The utility model discloses a water level monitoring mark device for water conservancy engineering investigation, including. Including base, the lower fixed joint of base has fixed part, the upper fixed joint of base has connecting seat, the side fixed joint of connecting seat has the water inlet pipeline, the upper fixed joint of connecting seat has water level monitoring cylinder, the utility model scale mark ring's scale groove is pasted with the reflecting film, can strengthen light reflection, promote long -distance observation definition, and the reflecting display cylinder of buoy top is transparent material, is equipped with reflecting film and annular lamp group in the inside, and annular lamp group can be active light compensation after electrification, and light is diffused through the cooperation of transparent part and reflecting film, even in the weak light environment, and the survey personnel still can read the scale corresponding to the reflecting display cylinder through the remote equipment accurately, realizes all -weather stable observation.

The invention discloses a low-light night vision device, which comprises a battery power supply module, an individual soldier system power supply module, a power supply switching module, an image enhancement module and a controller, and is characterized in that the individual soldier system power supply module is connected with an individual soldier system power supply in a plugging manner, and the individual soldier system power supply module is also connected with the controller and the power supply switching module; the battery power supply module is connected with the controller and the power supply switching module, and the power supply switching module is respectively connected with the image enhancement module and the controller; the image enhancement module is connected with the controller; the single-soldier system power supply module is used for providing a single-soldier system power supply, the battery power supply module is used for providing a battery power supply, the power supply switching module is used for switching from the battery power supply module to the single-soldier system power supply module to supply power when the single-soldier system power supply is connected, and the image enhancement module is used for enhancing images under weak light and detecting light intensity. And the controller is used for controlling the opening and closing of the image enhancement module according to the light intensity. The problem that power supply is insufficient when the low-light night vision device is used for a long time is solved.

The invention discloses a TSTFP-Net-based semantic segmentation method for a low-illumination superposition rain image, and the method aims at an image segmentation scene shot in a weak light and rainy environment, and solves a problem that the semantic segmentation precision is reduced because of insufficient illumination, raindrop interference and a significant distribution difference between a source domain and a target domain. The method comprises the following steps: 1) processing an existing data set; 2) designing a semantic segmentation network based on low illumination and rain superposition of TSTFP-Net; 3) training the RIPLM network; 4) training a trunk segmentation network; 5) training a space domain and frequency domain double-teacher network; the method comprises the following steps of (1) training an FSFM module, (2) training a comparison learning module, (3) training a domain discriminator network, and (4) testing a semantic segmentation network with low illumination and rain overlay. The method can still accurately identify the contour and category of an object in complex weather, improve the domain generalization performance, reduce the domain gap, and further improve the precision of semantic segmentation in the low illumination and rain overlay environment.

The utility model discloses an anti-explosion movable LED illuminating lamp, relates to the technical field of illuminating lamps, solves the problems that an existing device is poor in placing stability and poor in using effect, and adopts the following scheme that three sets of fixing frames opposite in position are fixedly installed on the top face of a base. Through the arrangement of the rotating sleeve, the telescopic foot stand and the stable feet, the rotating sleeve is rotated firstly, the telescopic foot stand is lengthened from the folding foot stand, the stable feet make contact with the ground, then the folding foot stand is limited and fixed through the foot stand locking buckles, the three sets of stable feet and the telescopic foot stand form a stable triangle, and the placing stability of the device is improved; meanwhile, through the arrangement of the control box, the arrow indicating lamp and the bulb, the arrow indicating lamp can be controlled to be turned on and the brightness of the bulb can be adjusted through the control button, the LED explosion-proof lamp has a strong-light and weak-light illumination mode, an arrow direction indicating mode and a warning lamp flickering mode, the functionality of the LED explosion-proof lamp is improved, and therefore the using effect of the LED explosion-proof lamp is improved.

The application relates to the technical field of safety monitoring in an industrial environment, and discloses an image processing method and system for an industrial environment, which trains an input image enhancement network framework by taking a weak-light image in a video frame as the input image; in the training, the weak-light image is input into an initialization module to obtain initial reflectivity and initial illumination output by the initialization module; the initial reflectivity, the initial illumination and normal illumination are input into an optimization module to iteratively refine a reflection layer and an illumination layer, so that target illumination is obtained; the target illumination is input into an image enhancement module to obtain an output enhanced image; and an image frame of the industrial environment collected in real time is input into the image enhancement model to obtain an enhanced image. The method can solve the problem that, due to poor lighting conditions and excessively high dust and smoke concentration in some industrial environments, the real-time monitoring technology causes low video image quality of a monitoring video, thereby affecting the final recognition result.

The invention provides a self-powered wireless sensor node energy efficiency control method and system for an indoor weak light scene, and belongs to the technical field of self-powered wireless sensors of the Internet of Things. Comprising the following steps: constructing an energy collection and energy perception calculation model which comprises an energy collection module, an energy perception calculation module, an energy prediction module and an energy management module; the energy sensing calculation module dynamically adjusts the capacitor capacity by using a temperature correction model in the process of calculating available energy according to the super-capacitor voltage; the energy prediction module performs prediction by adopting a mixed entropy weight rolling adaptive energy prediction algorithm based on low energy density and high-frequency fluctuation characteristics of an indoor weak light scene, and dynamically adjusts a prediction weight according to an energy fluctuation condition by distinguishing situations, controlling dormancy and quantifying energy fluctuation. Balancing the indoor energy prediction precision and the energy consumption in the energy collection process; and the energy management module performs energy efficiency control based on the model, calculates an energy prediction value of the next time slot and performs working mode control of the node.

The application discloses a pedestrian target recognition method in a weak light environment based on multi-modal data fusion, and relates to the technical field of intelligent vehicle automatic driving, to solve the problem that the target detection effect of the prior art is poor in a weak light environment. The method comprises the following steps: a traffic scene database containing pedestrians and vehicles is constructed, the database comprising infrared images and visible light images; the infrared images and the visible light images are subjected to feature registration and space-time alignment, and are divided into a training set, a verification set and a test set in proportion; a double-branch target detection network based on multi-modal data fusion is provided, the double-branch target detection network is trained with the training set as input, a Dark RGBT -YOLO network model suitable for a weak light environment is obtained; the test set is input into the Dark RGBT -YOLO network model to detect targets and obtain detection results; and position risk scores and distance risk scores of each pedestrian target in the images are analyzed according to the detection results, the position risk scores and the distance risk scores are added together to obtain a total risk assessment result.

The invention discloses a photovoltaic panel tracking system, which relates to the technical field of photovoltaic power generation, and is characterized in that illumination intensity difference is sensed in real time through a four-quadrant photosensitive element so as to judge the real-time azimuth of the sun and set a photoelectric tracking mode, and then in specific tracking, when the illumination intensity is higher than or equal to a preset threshold value, the photoelectric tracking mode is started, and the photovoltaic panel tracking system is started. A photovoltaic panel is driven to be aligned with the sun in real time, when the illumination intensity is lower than a preset threshold value, a time control tracking mode based on preset sun trajectory characterization is switched, in the process, a photoelectric tracking mode can be started when cloudy, cloudy and rainy weather changes suddenly, and the time control tracking mode can be started when light intensity distribution is uneven due to weak light or local cloud layer shielding. According to the invention, the mode switching is dynamically tracked under different environmental conditions, so that all-weather and high-precision tracking of the sun trajectory is realized, finally, sufficient energy conversion is carried out according to the self-adaptive light intensity change of the environmental conditions, and the energy conversion efficiency of the photovoltaic panel is greatly improved.