6602 results about "Light intensity" patented technology

A non-invasive physiological sensor system implemented as a smart watch or other wearable device includes an emitter for emitting light and a detector for collecting light after the light interacts with a tissue of a wearer. The system can measure light intensity at one or more radial distances to estimate attenuation parameter values that can be used to correct physiological parameter bias across subjects. In addition or alternatively, the system can determine whether a detector is positioned sufficiently proximate to an obstructing tissue (for example, an artery or vein) so as to cause inaccurate measurements by the detector.

The invention relates to the technical field of water pollution detection, in particular to a water quality pollution multi-dimensional analysis system which comprises the following steps: synchronously measuring an ultraviolet-visible absorption spectrum of a water sample to be detected and scattered light intensity in an orthogonal direction, and deducting spectrum baseline interference caused by suspended particulate matters in the water sample through a correction algorithm; the interferents in the water sample are selectively converted and degraded by applying a programmed potential to the inflowing water sample to be detected; the reverse pulse is periodically applied, so that the online regeneration function of the electrode is realized; analyzing the spectral data by adopting a multivariate correction algorithm, and calculating the concentration of the target pollutant; the calculation residual error of the multivariate correction algorithm is analyzed, new interference and system performance drift of current measurement are diagnosed, and a corresponding system state code is generated; and responding to the system state code, automatically starting and carrying out state checking and multi-point calibration on the instrument, and using a calibration result to update the analysis model. Through multi-dimensional perception and closed-loop operation, accuracy and reliability of water quality monitoring are guaranteed.

The invention provides a highway tunnel intelligent illumination control method and system, and the method comprises the steps: collecting a real-time perception data set of a plurality of monitoring nodes in a target tunnel, covering a light source operation parameter, an illumination intensity parameter and an environment state parameter sequence, carrying out the feature extraction of the real-time perception data set, and carrying out the feature extraction of the real-time perception data set; generating a global feature set including light source operation stability, regional illumination coordination and environmental coupling influence features, and then inputting the global feature set into a preset dynamic decision model for strategy decision to obtain a multi-stage illumination regulation and control strategy set covering different regions, including target brightness, dimming time sequence and equipment cooperation parameters; and finally, generating an executable instruction set according to the multi-stage illumination regulation strategy set, issuing the executable instruction set to each partition illumination control terminal, and triggering adaptive dimming of the illumination equipment, thereby realizing intelligent management and control of tunnel illumination, improving an energy-saving effect and illumination quality, and ensuring driving safety.

The invention relates to a hydraulic engineering concrete crack intelligent identification and quantitative analysis method and system based on machine vision. The method comprises the following steps: firstly, acquiring an original image sequence of the surface of a hydraulic engineering concrete structure, classifying according to illumination intensity, shooting angle and shooting distance, extracting crack edge features through a convolutional neural network, and fusing to obtain a crack feature set; correcting illumination through adaptive histogram equalization, correcting angles and distances through geometric transformation, and combining edge detection and scale invariant feature transformation to obtain standardized geometric parameters including crack length, maximum width and the like; if the parameter exceeds the engineering safety standard threshold value, tracking a crack track through an optical flow method to calculate increment, and inputting a neural network to output a damage trend; and finally, generating a three-color risk distribution diagram by using a finite element based on the trend, extracting high-risk data to calculate a real-time evaluation value, and dynamically adjusting the monitoring frequency to generate an optimization strategy. By adopting the method, the reliability and economy of engineering safety monitoring can be remarkably improved.

The invention discloses a lighting data management system and method for street lamp networking, relates to the field of lighting data management, and aims to solve the problem of waste of lighting resources. The technical scheme is characterized in that all street lamp nodes are networked according to physical positions, and a geographic information database is constructed; collecting motion data of a moving target and environment illumination intensity data; when the moving target is detected to be a pedestrian or a vehicle, switching the current street lamp node and the adjacent street lamp node to a normal mode; predicting the trajectory of the moving target, and calculating the number of street lamps needing to be turned on in advance according to the moving target speed and the safety response time; when the moving target approaches the intersection, the street lamp nodes of the intersection branches are converted from an energy-saving mode to a normal mode; and when the moving target leaves the detection range of the sensor, the street lamp nodes of the branch paths which are not actually passed are recovered to the energy-saving mode, so that on-demand illumination is achieved, and the effect of reducing the energy consumption of the street lamp is achieved.

The invention relates to the technical field of three-dimensional imaging, in particular to a four-eye structured light stereoscopic vision imaging method, which comprises the following steps of: arranging four cameras and synchronously acquiring multi-view image data with structured light stripes; carrying out image preprocessing and stripe code identification on the obtained four-view-angle structured light image data, and extracting structured light stripe center line positions and corresponding space projection information under each view angle; generating three-dimensional point cloud data of the high-precision target sample by using parallax calculation and a three-dimensional reconstruction algorithm, and completing spatial registration and filtering optimization of point cloud; fusing the point cloud and the light intensity data by combining the reflection intensity information of the multi-view structured light stripes to generate a composite data set containing space and spectral information; and constructing a dense parallax field and executing three-dimensional consistency verification, and generating a high-fidelity three-dimensional reconstruction model with a complete topological relation and sparse shielding compensation capability. According to the invention, the problems of insufficient view angle coverage, shielding area information loss, difficult edge structure matching and the like of traditional stereoscopic vision imaging can be solved.

The invention discloses a switch cabinet discharge detection method and system, and relates to the technical field of power equipment state monitoring and fault diagnose.The light intensity attenuation rate and the vibration frequency are collected through a photonic crystal fiber, the concentration of decomposed gas is detected in combination with a Raman spectrum, and a space-time joint feature matrix is generated; performing space weight distribution on the space-time joint feature matrix based on the switch cabinet structure, and inputting the space weight to an RBF-SVM (Radial Basis Function-Support Vector Machine) to generate discharge type probability distribution; based on a high-frequency energy parameter and a gas decomposition concentration parameter in the spatio-temporal joint characteristic matrix, inputting the parameters into a multiple linear regression model, calculating an insulation aging index, and judging an insulation degradation grade according to a threshold value; the photonic crystal fiber is used for collecting the light intensity attenuation rate and the vibration frequency, the Raman spectrum is combined for detecting the decomposition gas concentration to generate the space-time joint characteristic matrix, synchronous monitoring of various physical and chemical parameters in the switch cabinet is achieved, and the accuracy and comprehensiveness of recognition of different types of discharge phenomena are improved.

The invention relates to the technical field of sensor demodulation, in particular to a dynamic demodulation method and system for an MEMS optical fiber multi-parameter sensor. The method comprises the following steps: deploying a high-speed parallel acquisition sensor for an MEMS optical fiber multi-parameter sensor, acquiring spectral reflection and spectral time domain data, and constructing an original multi-parameter spectral data set; performing signal separation processing on the original multi-parameter spectral data set based on a preset hybrid network to obtain a wavelength-light intensity matrix, an input layer based on the preset hybrid network being the original multi-parameter spectral data set, and an output layer being the wavelength-light intensity matrix; therefore, by constructing an integrated data flow of original spectrum data acquisition, feature decoupling, parameter fusion and performance evaluation, the problems of large coupling error, weak noise suppression and uncontrollable demodulation precision in a traditional optical fiber multi-parameter demodulation model are solved; and the demodulation accuracy and practicability of the MEMS optical fiber sensor under the multi-physical-quantity complex working condition are improved.

The invention relates to the technical field of optical fiber sensing and fault diagnosis, in particular to an optical fiber gyroscope fault monitoring method and system. The method comprises the following steps: extracting four-dimensional characteristics of phase difference, light intensity fluctuation, polarization state drift and temperature drift from an output signal of an optical fiber gyroscope, and constructing a high-dimensional characteristic matrix after unifying a time reference; establishing a dynamic prediction model based on historical data to generate a residual matrix, and orthogonally separating the residual into an internal structure abnormal signal and an environment disturbance signal through covariance characteristic decomposition; mapping the structure residual error sequence into a topological graph, calculating an evolution index in real time, and capturing a fault gradient trend in combination with sliding window gradient analysis; a multi-dimensional vector is constructed by fusing topological features, an adaptive classifier of an online Gaussian mixture model is adopted to identify a fault mode and quantify a health index, and meanwhile, a health measurement result is fed back to a prediction model and topological analysis parameters to realize closed-loop optimization. According to the invention, full-process adaptive monitoring from anomaly detection to health measurement is realized.

The invention relates to the technical field of display, and particularly discloses an intelligent self-adaptive eye protection display system which sequentially comprises a content analysis module, an ambient light detection module, an eye protection parameter calculation module, a display parameter adjustment module and a user feedback module. The system identifies the screen content type and the dynamic degree in real time, synchronously detects the ambient light intensity and the color temperature, obtains the optimal combination of the blue light proportion, the brightness, the contrast ratio, the color temperature and the sharpness through a multi-target optimization model, and smoothly adjusts the display through driving; a user can score, finely adjust and mark a scene, and feedback data enters a self-learning engine to iteratively update a parameter weight so as to form a content-environment-user closed loop. The system can significantly reduce blue light harm and improve visual comfort without external hardware.

The invention discloses a machine learning-based weld defect intelligent identification system, relates to the technical field of weld defect intelligent identification, solves the technical problems of multi-modal data fusion precision and robustness optimization and defect shielding or overlapping feature deficiency, and provides a machine learning-based weld defect intelligent identification method based on PSNR dynamic parameter adjustment and gradient weight optimization. The limitation of existing fixed parameter denoising is solved, the edge feature retention rate of cracks, air holes and other defects is improved, the omission ratio is reduced, improved DeepLabv3 + segmentation semantic masks are introduced and mapped to point cloud voxels, geometric + semantic double-attribute enhanced point clouds are formed, the defect area positioning accuracy is improved, and through a cross-modal attention module, the defect area positioning accuracy is improved. Weights are dynamically distributed according to illumination intensity and workpiece materials, feature waste caused by fixed weights is avoided, depth mutation and a shielding area with semantic defects are positioned by utilizing depth information of enhanced point cloud, real overlapping and projection overlapping can be effectively distinguished by combining an improved Poisson fusion algorithm, and the overlapping defect recognition accuracy is improved.

The invention relates to a multichannel intelligent control method and system for UV LED light, and the method comprises the following steps: carrying out the spectrum scanning of a light irradiation region of a UV LED light source array, and obtaining a spectrum intensity distribution diagram; based on the spectral intensity distribution diagram, light wavelength segmentation division is carried out on a UV LED light source array, and a plurality of wavelength subintervals are obtained; performing dynamic power adjustment on the UV LED light source array according to the plurality of wavelength subintervals to obtain an optimal power configuration scheme; according to the optimal power configuration scheme, performing synchronous control on each channel of the UV LED light source array to obtain a control signal sequence of multi-channel cooperative work; and on the basis of the control signal sequence, real-time output of the UV LED light source array is adjusted, stable multi-channel UV LED light output is obtained, and the technical problems that due to the fact that spectrum output of the UV LED light source array has non-uniformity and temperature sensitivity, light intensity distribution is not uniform and wavelength drifts possibly occur under different working conditions are solved.

The invention discloses an optical imaging and coding hole technology fused optical imaging system and method, and aims to integrate scattered polarization components in a reflected light signal into coding light field data with clear layers by constructing a polarization coding modulation system, so as to highlight a coupling relationship among optical elements. A refocusing sequence and a focal plane chromatography technology are adopted, spatial geometric information and light intensity distribution characteristics of different depth layers are fully excavated, and a three-dimensional distribution rule of surface topography is disclosed; wavefront correction and deformable mirror control technologies are introduced, imaging parameters of an optical system are adjusted in a self-adaptive mode, and the application range of measurement precision is expanded; light intensity modulation analysis and phase unwrapping window processing are combined, the change trend of the surface curvature is depicted, and key feature points of morphology measurement are grasped; finally, multi-dimensional and multi-level optical measurement information is fused, three-dimensional measurement output with rich content and reliable precision is formed, and comprehensive and high-quality measurement technology resources can be provided for application scenes such as precision optical detection and surface quality evaluation.

The invention provides a method for realizing carbon fiber defect detection by using image recognition, which comprises the following steps: acquiring a depth-diameter ratio of a hole according to an angle change correction coefficient matrix, and if the depth-diameter ratio is greater than a preset threshold, introducing an angle-related attenuation parameter to obtain a corrected light intensity depth mapping relationship; constructing three-dimensional shape data of the hole through the corrected light intensity depth mapping relation, identifying an internal space structure of the hole, performing voxelization discretization processing, obtaining a voxel unit and a light intensity response characteristic thereof, and determining an internal geometric boundary of the hole; and establishing a hole defect quality evaluation index according to a volume measurement result and measurement uncertainty evaluation, comprehensively evaluating the structural integrity of the carbon fiber composite material in combination with grid quality evaluation, and establishing a carbon fiber defect grade classification standard.

The invention provides a multi-scene-oriented intelligent light adaptive control method and system, and the method comprises the steps: recognizing the current behavior state of a user based on visual sensor data, predicting the behavior state of the user, and outputting a light control behavior tag; combining the behavior label with a scene type and ambient light intensity to generate a target illumination intention vector; according to the target illumination intention vector, in combination with an illumination control conflict situation, real-time lamp parameters and user space state data, calculating an illumination weight of each lamp to a user, distributing brightness and color temperature parameters, and in combination with the target dimming process duration, generating a lamp control parameter set; and converting the lamp control parameter set into a dimming instruction sequence, performing time synchronization and delay compensation, and issuing the dimming instruction sequence to each lamp for execution. According to the method, accurate mapping from behavior intention to physical illumination is realized, and the method has extremely high universality, deployability and engineering controllability.

The invention provides a plate edge sealing quality detection method and system based on machine vision, and the method comprises the steps: obtaining image data streams continuously collected in a plate edge sealing processing process, carrying out the light intensity change feature extraction of the image data streams, and obtaining a time sequence light variable feature matrix and a space light variable gradient map of an edge sealing region in an edge sealing image frame sequence; carrying out relevance enhancement on the time sequence optical variation characteristic matrix and the spatial optical variation gradient map through a preset characteristic enhancement model, and generating an edge sealing quality characteristic map with a space-time constraint relation; performing defect mode identification based on the edge sealing quality characteristic spectrum to obtain defect types existing in the edge sealing area of the plate and position distribution characteristics of the defects in the edge sealing image frame sequence; and generating a quality optimization instruction containing a parameter adjustment instruction according to the defect type and the position distribution characteristic, and sending the quality optimization instruction to the plate edge sealing control equipment. According to the invention, the overall precision and stability of plate edge sealing quality detection can be improved.

The invention discloses a slant path atmospheric turbulence phase screen generation and light field transmission simulation method. Belongs to the technical field of satellite-ground laser communication. The method comprises the following steps: firstly, establishing a three-layer height spectrum model in a slant-range atmospheric turbulence environment, then generating an initial field suitable for vacuum and turbulence environments by using an angular spectrum method, and generating n corresponding random dynamic phase screens according to atmospheric turbulence characteristics of different heights on a slant-range path and distribution characteristics of atmospheric wind speed. Superposing the initial field and the first phase screen, performing two-dimensional Fourier transform, and performing inverse Fourier transform on the transform field by using an angular spectrum method to obtain field distribution in front of the next phase screen; and repeating the process to pass through n phase screens in sequence, and finally obtaining the output field distribution of the uplink and the downlink. The method can effectively simulate the effects of refractive index fluctuation, wavefront distortion, light intensity flicker and the like caused by oblique-range atmospheric turbulence, and provides important theoretical support and application value for researching oblique-range laser transmission characteristics.

The invention relates to the technical field of camera control, and discloses a megapixel camera control method based on multifunctional fusion. The method comprises the following steps: acquiring original image data of an image sensor, and preprocessing to generate an initial brightness distribution diagram; and calculating a scene dynamic range to determine a target exposure parameter set containing exposure time, a gain coefficient and an aperture value, synchronously analyzing color temperature information, matching a preset white balance model to output a correction coefficient, and combining the two to generate a first control instruction to be sent to the image sensor. Receiving a touch screen focusing signal, and extracting a focus area coordinate to adjust a stroke parameter of a focusing motor; ambient light intensity is monitored in real time, and when the ambient light intensity is lower than a preset threshold, a flash lamp compensation module is activated and a compensation light quantity value is calculated. In the photographing mode, the output power of the flash lamp is adjusted according to the compensation light quantity value, the target exposure parameter set is recalibrated based on the focus area coordinates, and the camera complex scene adaptation and imaging quality are improved.

The invention provides a high-reflection plane metal part defect detection method and system based on a composite polarized light source, and a medium. The method comprises the following steps: illuminating a high-reflection plane metal part based on the composite polarized light source, adjusting a light source polarization angle and an LED wave band combination to construct a shooting environment, and collecting an image sequence; calculating a polarization intensity distribution parameter for the image sequence based on the relationship between the polarization intensity and the Stokes parameter to obtain a polarization intensity distribution image; analyzing the polarization intensity distribution image based on a polarized light reflection model, and outputting a specular reflection component and a diffuse reflection component; fitting the specular reflection component and separating the diffuse reflection component to obtain a reflection-removed image; based on an image enhancement algorithm, edge enhancement is carried out on the reflection-removed image to obtain an enhanced image, and based on a defect identification algorithm, the enhanced image is analyzed to obtain defect information; by removing the diffuse reflection component and reconstructing the image, the mirror interference is effectively stripped, and the defect identification precision is improved.

The invention discloses a laser channel atmospheric turbulence fading real-time compensation device and method based on a galvanometer system, and the method comprises the steps: enabling an incident light beam to irradiate a transparent PCB integrated with a photodiode array after the incident light beam is focused through a convex lens, obtaining the light intensity distribution of a current light spot through the photodiode array, and transmitting the light intensity distribution to a signal processing unit; the signal processing unit coarsely adjusts the deviation direction of the light spots according to the received light intensity distribution data, and aligns the part with the strongest light intensity in the light spots to the end face of the optical fiber; and based on the roughly adjusted light spot deviation direction, the signal processing unit dynamically adjusts the deflection angle of the galvanometer by using a gradient descent method according to the power measurement value of the receiving coupling module, so that the fine adjustment of the light spot deviation direction is realized, and the receiving coupling power is maximized. The objective of receiving optical power maximization is achieved, optical design, electronic control and algorithm optimization are combined, and atmospheric turbulence compensation with high cost performance is achieved.

The invention discloses a method for clearly imaging a mask under an optical objective, and relates to the technical field of optical detection and image processing, and the method comprises the following steps: S1, constructing a light intensity distribution model based on the boundary condition of the field of view of the optical objective in combination with the reflection characteristic and incident angle change rule of a metal edge material, and determining the coverage range of metal edge reflection crosstalk, generating a mask boundary interference prediction map; s2, according to the mask boundary interference prediction map, performing region division on the micro-reflectivity image, extracting brightness gradient characteristics of reflectivity lifting in an interference region, and generating a brightness gradient parameter set required by image filtering; the method is based on light intensity modeling, fusion direction filtering, pixel stripping, gradient reconstruction and credibility weighted fusion, realizes closed-loop control from interference prediction to pixel restoration, has high resolution and adaptivity, can accurately strip edge reflection artifacts and restore a real image structure, remarkably reduces misjudgment and rework rate, and is suitable for large-scale popularization and application. And the mask yield and the stability of the detection system are improved.

The invention relates to the technical field of vehicle vision calibration, and discloses a DMS camera calibration method based on computer vision. The method comprises the following steps: acquiring a multi-view original image sequence in a target vehicle cockpit, and synchronously acquiring a camera mounting position, a view angle, an inclination angle and an environment illumination intensity parameter; after distortion correction is carried out on the original image sequence, edge features of a calibration reference object in each frame of image are extracted through image recognition, and an initial calibration matrix containing a mapping relation between pixel coordinates and physical coordinates is generated; constructing a three-dimensional space projection model based on the initial calibration matrix, and adjusting rotation and translation parameters of the model by using an iterative optimization algorithm to enable the error between a virtual imaging position and an actual edge feature to be minimum; and extracting an internal parameter matrix and an external parameter matrix of the optimized model, fusing the internal parameter matrix and the external parameter matrix into a final calibration parameter, and writing the final calibration parameter into a DMS camera firmware storage area. According to the method, the DMS camera calibration accuracy and environmental adaptability are improved, and stable technical support is provided for driver state monitoring.

The invention discloses a photovoltaic panel self-adaptive cleaning system and method based on sand and dust optical thickness monitoring. The photovoltaic panel self-adaptive cleaning system comprises a sand and dust optical thickness monitoring module, a data processing and control module, a cleaning execution module, a power supply module and a remote monitoring module. The sand and dust optical thickness monitoring module is used for monitoring the sand and dust optical thickness AOD of the environment where the photovoltaic panel is located in real time, calculating the AOD by measuring the light intensity attenuation of a specific wavelength, and correcting the atmospheric Rayleigh scattering optical thickness and the gas absorption optical thickness at the same time; the data processing and control module is in communication connection with the sand and dust optical thickness monitoring module and used for receiving the AOD data and comparing the AOD data with a preset dust retention threshold value, and when the AOD value is larger than or equal to the threshold value, a cleaning instruction is generated; and the cleaning execution module is used for receiving the cleaning instruction and executing cleaning operation. Intelligent and self-adaptive control of photovoltaic panel accumulated dust cleaning is achieved, the cleaning opportunity can be adjusted in real time according to the actual sand and dust optical thickness, and the cleaning pertinence and effectiveness are improved.

The invention relates to the technical field of vehicle-mounted display, and discloses a vehicle-mounted HUD environment illumination adaptive brightness adjusting system, which comprises an illumination acquisition module, a parameter analysis module, a model construction module, a brightness calculation module, an error evaluation module, a calibration correction module, a threshold setting module, a result output module, a feedback optimization module, a system regulation module and the like. The illumination acquisition module analyzes the illumination parameter stability and interference difference and generates an illumination perception state value; the parameter analysis module screens an optimal brightness and light intensity combination; the model construction module is used for training samples to establish a brightness and light intensity relation model; the brightness calculation module generates a brightness threshold by combining real-time illumination data mapping; the error evaluation and calibration correction module optimizes a calculation rule; and the feedback optimization module operates feedback adjustment parameters to finally generate an environment illumination adaptive brightness adjustment scheme. The system realizes accurate and dynamic adjustment of the brightness of the vehicle-mounted HUD, effectively adapts to the environment illumination change, improves the display effect and the driving safety, and has the characteristics of self-learning and self-optimization.

The invention relates to the field of intelligent table lamps, and discloses an LED personalized eye protection lighting method based on AI learning, and the method comprises the steps: fusing the myopia degree manually input by a user, real-time ambient light intensity and desktop reflection characteristics, and generating an eye load adaptation matrix; performing cross-domain convolution recursion modeling on the eye load adaptation matrix, and constructing a user personalized visual tolerance tensor; based on the visual tolerance tensor, utilizing an adaptive threshold drift model to extract an illumination comfort trajectory, and identifying potential visual fatigue risk candidate points; mapping the potential asthenopia risk candidate points to an LED dimmable unit cluster, and performing iterative coupling optimization on brightness gradient, color temperature offset and an illumination incident angle to obtain light field adjustment solutions facing different myopia degrees; generating a non-uniform distributed control instruction among the brightness, the color temperature and the refresh frequency based on the light field adjustment solution; the method has the advantage of intelligently adjusting the light brightness and the color temperature by combining the myopia degree of the user and the ambient brightness.

The invention discloses a high geological disaster chain risk quantitative assessment method adapted to a high-cold and high-altitude environment, and relates to the technical field of geological disaster monitoring and prevention and control, and the method comprises the following steps: S100, building an irradiation and reflection combined observation layer, collecting the dynamic intensity of direct light and reflected light, generating an exposure time sequence fingerprint baseline, and obtaining an exposure time sequence fingerprint baseline; and determining a critical threshold value of the direct light intensity according to the base line. The method comprises the following steps: constructing an irradiation and reflection combined observation layer to extract light intensity characteristics, and generating an exposure time sequence fingerprint baseline; a trigger point boundary is extracted by adopting coherent phase decomposition and anti-fact playback, and a continuous track is reconstructed; the overexposure risk is predicted based on the time-frequency score, and spectrum gating and polarization rotation are linked to regulate and control the exposure rhythm; and reverse phase traction and phase conjugate intervention are implemented in a steady-state window, so that boundary information is maintained and the risk identification precision is improved.

The technical scheme of the invention discloses a photovoltaic power generation and greenhouse agriculture integrated system. According to the photovoltaic agricultural integrated technology disclosed by the invention, photovoltaic power generation and greenhouse planting are innovatively combined, and a solar panel with adjustable light transmittance of 30%-90% is mounted at the top of an agricultural facility, so that sunlight is utilized for power generation, and illumination required by crop growth is reserved. The system disclosed by the invention is provided with an intelligent sensor and an AI algorithm, environment data is monitored in real time, the angle, light supplementing intensity, temperature and humidity of the photovoltaic panel are dynamically adjusted, and different crop requirements (such as low illumination of leaf vegetables and medium and strong illumination of fruits and vegetables) are accurately matched. Residual electricity is stored or connected to a grid, photovoltaic waste heat is recycled in winter for heat preservation, sun shading and cooling are conducted in summer, and efficient circulation of energy is achieved.

A video generation framework that is controllable, unsupervised and based on events (CUBE) includes an event camera, which captures changes in light intensity at each pixel of a scene asynchronously and generates event camera data. A text-to-image diffusion model that is conditioned on textual descriptions integrates the event camera data to control video synthesis. Further, an edge extraction module translates event data into a format usable by the text-to-image diffusion model, whereby the diffusion model synthesizes detailed and contextually accurate videos based on textual prompts. Further, an improved system (CUBE Plus) includes a content frame identification module which selectively identifies and uses only the most information-rich event segments of the event camera data to drive cross-frame attention, and an event driven attention mechanism that allows the framework to focus on event-dense moments.

The invention relates to an unmanned aerial vehicle imaging control method and device based on AI and flight control parameter fusion, and the method comprises the steps: carrying out the scene feature extraction of historical imaging data, obtaining image features under multiple scales, constructing an AI scene adaptation strategy according to the image features, solving an illumination intensity prediction model, and obtaining an optimal imaging parameter set of a corresponding camera; and acquiring original image data shot by the camera under the optimal imaging parameter set, optimizing the original image data based on an AI scene adaptation strategy, predicting the flight state of the unmanned aerial vehicle based on the flight control parameter to generate an imaging preset parameter, and using the imaging preset parameter to control shooting of the unmanned aerial vehicle. The AI scene adaptation strategy is constructed by using AI in combination with historical imaging data, the problem of poor scene adaptability is solved, in addition, the illumination intensity prediction model is constructed by combining flight control parameters, the illumination intensity prediction model is solved, the optimal imaging parameter set of the corresponding camera is obtained, and the defect of weak illumination adaptability is overcome.

The invention relates to the technical field of building intelligent control and energy saving, and discloses a building equipment intelligent control optimization management system based on a green low-carbon building. The system comprises five subsystems, wherein an environmental parameter sensing subsystem is used for acquiring temperature and humidity gradients, illumination intensity distribution and personnel activity thermodynamic diagram data inside and outside a building through a distributed sensing network to form a multi-dimensional environmental state vector; the equipment operation state acquisition subsystem acquires an operation power curve, an energy efficiency conversion rate curve and an equipment health degree index of heating ventilation air conditioning, illumination and renewable energy equipment in real time; the dynamic load prediction subsystem establishes an energy consumption load space-time distribution prediction model according to historical data time sequence relevance; the control strategy generation subsystem is used for generating an optimized instruction set containing an equipment start-stop time sequence, a power regulation gradient and an energy distribution weight in combination with the current environment vector and a predicted load; the execution terminal adapts the subsystem to convert the instruction into a device compatible signal and calibrate a response delay.