85 results about "Haze" patented technology

The invention discloses a real haze image defogging method based on a haze degradation model. The method comprises the following steps: constructing a haze degradation model fusing multiple scattering effects and multiple image degradation factors; using the haze degradation model to construct a training data set including the clear image and the corresponding pseudo haze image; constructing a defogging network for a real haze scene; training the defogging network by adopting the training data set until a preset loss function is converged; and inputting a to-be-defogged image into the trained defogging network to obtain a defogging result. According to the haze degradation model constructed by the invention, the difference between a synthetic domain and a real domain is effectively relieved; a designed space-frequency hybrid module improves the adaptability of the model to complex degradation characteristics; the prior-guided feed-forward network fully excavates and fuses dark channel prior information, and the sensing and modeling capability of the model to the haze area is effectively enhanced.

The invention discloses a high-transparency PVB intermediate film and a production process and application thereof, and relates to the technical field of PVB intermediate films, the high-transparency PVB intermediate film comprises the following raw materials by weight: 100 parts of PVB resin, 20-30 parts of a composite plasticizer, 0.5-0.9 part of an LDH-based composite antioxidant, 0.5-1 part of an ultraviolet light absorber, 0.1-1.5 parts of nano lanthanum hexaboride, 0.1-1.5 parts of porous hollow nano copper sulphide, and 0.1-2 parts of nano TiO2 / SiO2 core-shell particles. The PVB intermediate film provided by the invention is relatively high in light transmittance, low in haze value, and excellent in oxidation resistance, weather resistance and mechanical strength.

The invention discloses a semi-solid-state complex no-tillage dry direct seeding method for crop seeds. The no-tillage dry direct seeding method comprises the following steps: S1, keeping a water layer of 1cm-5cm in a ditch, and paving straws obtained by harvesting preceding crops on a ditch surface; S2, applying a base fertilizer on the ditch surface paved with the straws and cleaning the ditch; S3, seeding the crop seeds on the ditch surface, and mixing the crop seeds with semi-solid-state biomass materials, a compound fertilizer and a biopesticide before seeding before seeding seeds, wherein the mixing mass ratio is that the crop seeds to the semi-solid-state biomass materials to the biopesticide is (1-10) to (70-800) to (1-3), and the moisture content of the semi-solid-state biomass materials is 40%-80%; and S4, after seeding the seeds, keeping the ditch surface at a dry state for 3-7 days. According to the semi-solid-state complex no-tillage dry direct seeding method, tillage and combustion of winter crop straws are not carried out, a whole land of field immersing water is not used for irrigation and the water immerses after the seeding, and the biological benefits are obvious; the working energy consumption is reduced, stubbles are not killed by pesticides, haze is reduced and the irrigation water is reduced.

The invention provides a power equipment defect detection system and method based on a multi-mode Transform, and the system comprises a double-flow feature extraction module which is used for carrying out the feature extraction of a collected visible light image and an infrared image of power equipment through employing a double-flow feature extraction network based on YOLOv5, and obtaining a visible light feature pattern and an infrared feature pattern; the multi-modal information interaction module is used for carrying out multi-modal information interaction on the visible light characteristic pattern and the infrared characteristic pattern to obtain a multi-modal fusion characteristic pattern; and the defect classification module is used for carrying out defect detection and classification on the power equipment by adopting a two-stage residual full-connection defect classifier based on the multi-modal fusion feature map to obtain a defect classification result. The method can overcome the influence of environmental factors such as heavy fog, rain, haze and the like, is not limited by illumination conditions, and has relatively high anti-interference capability. In addition, the method can also solve the problems of low resolution and fuzzy texture details of the infrared image.

The invention discloses an intelligent networking driving recording system based on multi-mode perception. The intelligent networking driving recording system comprises a perception layer, a processing layer, an application layer and a block chain evidence storage module. The invention further discloses a data tamper-proofing method of the intelligent networked driving recording system based on multi-mode perception, which comprises the following steps: S1, synchronously acquiring thermal radiation data, radar point cloud data and video image data in a driving environment through the infrared thermal imaging sensor, the 4D millimeter wave radar and the high-definition optical camera; s2, infrared thermal imaging data are optimized, radar point cloud clustering is optimized through a DBSCAN algorithm, and target tracking is achieved based on UKF. The system can cover the whole temperature range of-30 DEG C to 85 DEG C and severe weather such as rain, snow and haze, effectively reduces the false alarm rate of infrared thermal imaging and the missed detection rate of a millimeter wave radar static target, avoids the loss of accident key evidence caused by environmental factors, greatly improves the evidence obtaining reliability at night and in severe weather, and reduces traffic accident disputes.

This invention discloses a method and system for intelligent weather image acquisition and recognition, specifically relating to the field of weather image recognition technology. It involves continuous polarization imaging of the sky region while simultaneously acquiring spatial attitude and temporal information; preprocessing the polarization image data and extracting Stokes parameters to obtain standardized sky polarization light field data; then using a theoretical Rayleigh scattering model to analyze the polarization degree distribution pattern at different wavelengths to obtain theoretical sky polarization pattern characteristics; based on the polarization light field data, identifying the spatial distribution of actual clouds, aerosols, and haze in the sky to form actual atmospheric composition distribution characteristics; extracting anomalous polarization regions through difference analysis between theoretical and actual characteristics, and accurately classifying halo polarization anomalies and scattering polarization anomalies based on geometric and spectral characteristics; generating ultralocal weather change early warning signals based on the classification results; thus improving the accuracy and real-time performance of early warnings for ultralocal anomalous weather phenomena.

The invention relates to a track defect detection method based on an infrared image under a low-visibility condition. The method comprises the steps that active pulse type thermal excitation is applied to the surface of a track, an infrared image sequence and a visible light image are synchronously collected and preprocessed, feature level fusion is carried out on the preprocessed infrared image and visible light image, track defect recognition is carried out in combination with an improved cross-scale polymorphic self-adaptive detection network, and the track defect recognition accuracy is improved. Outputting a defect bounding box and a corresponding defect category label; and then sub-pixel-level edge contour positioning processing is carried out on the defect area, geometric parameters of the defect area are calculated based on a positioning result, and a track defect detection report is generated by combining the geometric parameters, a defect category label and a track structure health state evaluation result. By adopting the method, the bottleneck of failure of visible light detection in a low-visibility environment can be effectively overcome, and high-precision and high-efficiency detection and evaluation of track defects in severe environments such as night, haze, rain and snow and the like can be realized.

The application discloses a deep learning-based artillery barrel target image defogging method and system, which comprises the following steps: inputting an original artillery barrel target image into a trained defogging enhancement network model for processing, wherein the defogging enhancement network model comprises an improved U-Net module, an AOD-Net module and an atmospheric scattering model layer connected in sequence; the improved U-Net module is used to extract multi-scale features of the original artillery barrel target image, and output the multi-scale feature maps to the AOD-Net module; the AOD-Net module is used to extract features under different receptive fields of the multi-scale feature maps through different convolution layers, realize multi-scale feature fusion, and output input values of the atmospheric scattering model layer; and the atmospheric scattering model layer is used to obtain the defogged artillery barrel target image based on the input values. The model is constructed by connecting the U-Net and the AOD-Net in series, a new model optimizer is proposed, the algorithm of gradient descent is optimized, and a self-adaptive weight loss function is introduced, so that efficient defogging processing under complex fog and haze conditions is realized.

The method for removing fog based on inter-layer multi-scale sequence interaction and Fourier domain frequency-space enhancement comprises the following steps: step S1: collect a public fog data set containing different concentrations of fog and haze, and pre-process the images therein to improve data quality and model training effect; step S2: construct a network framework for removing fog based on inter-layer multi-scale sequence interaction and Fourier domain frequency-space enhancement, which comprises a U-shaped fog removal network, an inter-layer multi-scale sequence interaction module IMSIM and a Fourier domain frequency-space enhancement module FDFSEM; step S3: train the network using the labeled image data set, define a suitable loss function and use an optimizer to train; step S4: evaluate the fog removal effect using two indicators of peak signal-to-noise ratio PSNR and structural similarity index SSIM, optimize the trained model, and deploy it to the actual application for online inference and real-time fog removal; through the above steps, the input feature map can be removed.

The invention relates to the field of high polymer materials, in particular to a high-definition perspective TPEE film, a preparation method and application. The invention discloses a high-definition perspective TPEE film which is prepared from the following components in percentage by weight: basic resin TPEE, a high-definition additive, a weather-resistant stabilizer, an antioxidant, an antistatic agent, an anti-scraping agent and a compatilizer, and further provides a preparation method of the TPEE film, the preparation method comprises the following steps: pre-mixing the components, performing melt blending through a twin-screw extruder, and finally forming the film through a tape casting method or a film blowing method, so as to obtain the high-definition perspective TPEE film. Meanwhile, the invention discloses application of the TPEE film in a high-definition display screen, a high-end packaging material or special optical equipment. The prepared TPEE film has high-definition perspective performance, the film thickness is 50-200 microns, the light transmittance is larger than or equal to 91%, the haze is smaller than or equal to 1.2%, the yellowing index delta b is smaller than or equal to 0.6, and the TPEE film has the effects of weather resistance, oxidation resistance, static electricity resistance, scraping resistance and the like.

The invention discloses an experimental device for simulating surface dirt accumulation and detection of an intelligent coal caving camera, and relates to the technical field of coal mine engineering, the experimental device comprises an air duct model group, a dust generator, an air draft mechanism, a power supply, a controller, a sensor assembly, a haze meter and a plurality of camera type dust collectors; the air duct model group with the transparent side wall is provided with a simulation air duct for simulating a coal caving space at the rear part of an underground fully mechanized caving face; the air draft opening of the air draft mechanism is connected with the outlet of the simulation air duct and has adjustable power; the sensor assembly comprises a wind speed sensor and at least one concentration sensor; the camera type dust collectors are respectively arranged at different positions of a dust flowing path; the haze meter is used for detecting the haze of each simulation lens for at least three times, the controller is in communication connection with the dust generator, the air draft mechanism, each sensor and the haze meter, and the power supply is used for powering on each component. The system can simulate the coal dust attachment characteristics on the surface of the camera, provides support for the research of reducing the dust attachment of the camera, and improves the precision of a visual system of an underground intelligent coal caving machine.

The application discloses an automatic haze detection device and relates to the technical field of haze detection devices. The device comprises two sets of mechanical hands, a tightening system, a control system and a bottom plate. The bottom plate is provided with a support frame, and the support frame is provided with two sets of conveying lines. The two sets of conveying lines convey a film therebetween. The film is connected with the tightening system at both ends. A support plate is installed on one side of the two sets of conveying lines. A driving motor is installed on the support plate. The output shaft of the driving motor penetrates through the support plate and is provided with a driving gear. A driven gear is engaged on one side of the driving gear. A spraying cylinder is installed on one side of the support plate. Two sets of haze meters are installed on one side of the spraying cylinder. The two sets of mechanical hands are respectively located on one side of the two sets of haze meters. The two sets of mechanical hands are both provided with auxiliary diaphragms. Air is sprayed on the surface of the film through air blowers, dust on the film is cleaned, and composite cleaning is formed with the brush, so that the cleaning effect of the film at the position to be detected is improved.

The application relates to the technical field of image cleaning, and particularly discloses a power transmission and transformation equipment image enhancement and defect identification method for severe weather, the image enhancement method comprising the following steps: collecting power transmission and transformation equipment image quality data and environment data at each image acquisition time through a data acquisition module; combining the environment data at each image acquisition time to clean and correct the quality data of the power transmission and transformation equipment image, so that the reliability and rationality of the power transmission and transformation equipment image quality data are improved, the accuracy of calculating the haze influence index of the power transmission and transformation equipment image at each image acquisition time is improved, and the rationality of the haze influence degree evaluation result at each shooting time is improved; on the basis, the contrast of the image is compensated by combining the haze influence index with high accuracy, so that the distinguishing degree of the power transmission and transformation equipment and the background is enhanced, the accuracy of subsequent image defect identification is improved, and the power transmission and transformation equipment image is enhanced.

This invention belongs to the field of photovoltaic-storage-charging microgrid technology, and provides an anomaly monitoring method and system for photovoltaic-storage-charging microgrid systems. It addresses the problem of inability to accurately detect or detect the impact of haze on power generation. This invention performs a first data correction on photovoltaic panel power generation based on haze intensity; a second data correction based on haze values ​​influenced by wind speed; a third data correction based on haze values ​​influenced by humidity; and a fourth data correction based on haze values ​​influenced by temperature. Anomalies are then detected using the four corrected photovoltaic panel power generation figures. This invention achieves anomaly detection when haze causes a decrease in power generation, and considers multiple factors such as environmental wind speed, temperature, and humidity that cause data changes, determining the power generation correction values ​​under the influence of each factor, greatly improving the accuracy of anomaly monitoring and detection.

The invention discloses a vehicle driving control method and device, a mine car, a storage medium and a product, and the method comprises the steps: obtaining a dust recognition result and a haze recognition result of an environment in a vehicle driving process through image collection equipment and an Internet of Things platform; through a rainfall sensor and an Internet of Things platform, obtaining a rainfall identification result of an environment in which the vehicle is located in a driving process; according to the sand and dust recognition result, the haze recognition result and the rainfall recognition result, weather information of the environment where the vehicle is located in the running process is determined, and a control strategy matched with the weather information is adopted to control the speed limiting value and the braking force of the vehicle. According to the technical scheme, the safety coefficient of the mine car under different weather conditions can be improved, and the operation efficiency of the mine car is guaranteed.

The invention discloses a dust and haze multivariable collaborative regional dynamic comprehensive evaluation method based on site observation and grid live analysis products, which combines the difference of physical and chemical properties of meteorological elements and particulate matters in dust, fog and haze weather, and introduces a Mie scattering theory to calculate an aerosol extinction coefficient to diagnose fog and haze. A scientific key characteristic index threshold value is adopted, and a multivariable cooperation and regional dynamic comprehensive evaluation method is used for diagnosis; finally, the three diagnosis results of direct diagnosis and site and grid point indirect diagnosis are comprehensively judged, the uncertainty of single data source diagnosis is reduced to the maximum extent, the evolution process, the influence range and the intensity change characteristics of sand, dust, fog and haze weather can be well represented, the temporal-spatial resolution is 5 km / 1 h, and the time efficiency lags for 55 min.

The application discloses a kind of high hardness, high weather-resistant TPU material and preparation method thereof, the TPU material includes by weight fraction: 30~60 parts cyclohexane polyol, 30~60 parts cyclohexane diisocyanate, 0.3~2 parts antioxidant, 0.05~1 parts lubricant, 0.05~0.5 parts wax powder, 0.01~0.5 parts catalyst.The application adopts double symmetry cyclohexane aliphatic ring structure, molecular chain arrangement is neat, phase region size is less than visible light wavelength, with high hardness, high transmittance and low haze;Pure aliphatic ring structure eliminates yellowing from the source, and has excellent weather resistance and aging resistance;Double cyclohexane skeleton forms steric barrier, has outstanding hydrolysis resistance, acid and alkali resistance, fuel resistance, while high temperature mechanical retention rate, creep resistance and dimensional stability are excellent, and can be widely used in diesel filter oil cup, high temperature sealing element, outdoor transparent structural member and other high-end scenes.

The invention discloses a bridge side falling behavior recognition method and system in a haze environment, and the method comprises the steps: carrying out the training of a pre-constructed RA-M fusion coding and decoding recognition network model based on a bridge side falling behavior data set and a model loss function in the haze environment; the trained RA-M fusion coding and decoding recognition network model is adopted to recognize the bridge side falling behavior in the haze environment; wherein the RA-M fusion coding and decoding identification network model comprises an RA-M encoder module and an RA-M decoder module, the RA-M encoder module is used for carrying out multi-scale feature extraction and fusion on images, and the RA-M decoder module is used for pedestrian masking and position recovery based on a reverse attention mechanism. According to the invention, the position and behavior state of a falling pedestrian can be accurately identified in a haze environment, bridge side falling behavior monitoring under a severe weather condition is realized, early warning information is provided for rescue workers in time, and the rescue response speed and success rate are improved.

The present application relates to a kind of micro-light image enhancement method suitable for haze climate, belong to digital image processing field.First, the method can effectively learn the denoising information in image pair in the process of learning through GCA (Group Channel Attention) group channel attention structure, can solve the interference caused by external noise;Second, the transformer block of the method makes the network have better window adaptive ability through self-attention mechanism, can solve local overexposure phenomenon with better effect;Third, the method can be fine-tuned by a small amount of field samples, greatly improve the adaptability to the required environment, and can learn new tasks on the task of low-light enhancement, solve the influence of non-illumination factors on image quality.

The invention discloses a multi-mode single-photon radar system and an environment adaptive imaging method based on the same. The multi-mode single-photon radar system comprises a laser scanning module and a photon detection module. The environment detection module and the photon detection module are electrically connected with the control module, and the control module is electrically connected with the laser scanning module, the photon detection module and the environment detection module; the image reconstruction module is electrically connected with the environment detection module and the control module; and the upper computer interaction module is electrically connected with the image reconstruction module. Therefore, the multi-mode single-photon radar system can dynamically adapt to and perform high-resolution imaging in complex environments such as haze, strong background light, rain and snow and the like, and the multi-mode single-photon radar system is modularized in structure, has good expansibility and engineering realizability and is suitable for the fields of automatic driving, complex scene perception, target surveying and mapping and the like.

The present application relates to the technical field of PVB film preparation, in particular to a high-barrier low-haze PVB interlayer and its preparation method and application. To solve the technical contradiction that the heat insulation performance, optical performance and mechanical performance of the PVB interlayer are difficult to be considered in the prior art, the present application adopts the following technical scheme: the PVB interlayer has a three-layer symmetrical structure, the skin layer is a high-molecular-weight PVB adhesive layer; the core core layer comprises a molecular weight gradient blending matrix formed by ultra-high molecular weight PVB and low molecular weight PVB. The matrix cooperatively disperses absorbing infrared barrier agent, reflective infrared barrier agent, core-shell structure ultraviolet stabilizer, toughening agent and flame retardant, and realizes high-efficiency interface bonding through silane coupling agent. Through the ingenious structure function partition and material composite design, the present application realizes high infrared barrier rate, maintains low haze, and has excellent mechanical performance and super-long weather resistance stability, and has excellent comprehensive performance.

The invention discloses a wind driven generator blade surface defect identification method for severe weather, which comprises the following steps: preprocessing a left polarization image group and a right polarization image group, representing a polarization characteristic mapping graph through a two-dimensional characteristic vector, and carrying out defect instance segmentation. Performing instance matching verification on the left and right groups of defect instances, and judging whether the instances correspond to the same physical entity or not; and if the instances are matched, calculating the world coordinate of the defect centroid through the pixel coordinates of the left and right groups of centroids. And if the instances are not matched, performing homologous region search, and calculating the world coordinates of the defect centroid according to the centroid of the homologous region and the centroid of the defect instance region. The method is suitable for routing inspection and maintenance in severe weather with low visibility such as fog, haze and dust, and the identification and positioning reliability of the surface defects of the wind driven generator blade can be effectively improved.

The application discloses a haze-removing special vehicle and a haze-removing control method, wherein the haze-removing special vehicle comprises a vehicle body, a wind channel arranged in the vehicle body, a haze-removing module arranged in the wind channel and used for removing haze from air flowing through the wind channel, a wind channel opening and closing driving module used for controlling opening or closing of the wind channel, an environmental data detecting module used for detecting air quality data in the vehicle in real time, and a control module electrically connected with the environmental data detecting module, the wind channel opening driving module and the haze-removing module, used for receiving the air quality data, controlling opening or closing of the wind channel opening and closing driving module and the haze-removing module according to the air quality data. The haze-removing special vehicle realizes sequential starting of multiple haze-removing units by means of a reasonable control circuit, so as to reduce the impact of starting current on the vehicle power supply, and the haze-removing module cannot be opened when the top window is not opened, and the opening state of the haze-removing module and the air outlet is electrically interlocked.

This application relates to the field of image recognition technology, and provides a method and system for reconstructing recorder images under severe weather conditions. The method includes: acquiring the type of severe weather, including rain, snow, haze, fog, and hail; performing interference feature analysis to determine multiple image interference information; performing interference intensity analysis to determine multiple interference information intensity thresholds; setting image denoising filtering ranges to obtain multiple denoising filtering ranges; selecting a first denoising filtering range as the optimization interval among the multiple denoising filtering ranges, with the goal of minimizing image interference, and performing filtering optimization to obtain a first optimized filtering value; constructing an image denoising database based on the first optimized filtering value; and performing denoising processing on the recorder image based on the image denoising database to complete the recorder image reconstruction. This method can solve the technical problem of low accuracy in recorder image recognition under severe weather conditions and can improve the accuracy of recorder image recognition under severe weather conditions.

The invention provides a cloud and cloud shadow information detection and extraction method based on a Landsat8 remote sensing image. Compared with a traditional cloud and cloud shadow information extraction method, the method innovatively constructs a land brightness temperature threshold empirical linear model according to the three spectral characteristics of high brightness, high whiteness and low brightness temperature of a cloud layer, refines a land brightness temperature threshold, extracts thick cloud, and improves the cloud shadow extraction efficiency. Extracting thin cloud by using a haze optimization transformation method and a cirrus cloud waveband threshold method, and performing superposition processing on the extracted thick cloud information and thin cloud information to obtain cloud information C; the method comprises the following steps: extracting cloud shadow information CS by using a threshold method by using an operation relationship between a darkness index (DI) and a spectrum, constructing an empirical linear model between a mountain shadow threshold and a solar altitude, and removing false information in the cloud shadow information CS caused by the mountain shadow to obtain refined cloud shadow information CS '. And finally, standardizing the shape of the extracted cloud and cloud shadow information by using a corrosion and expansion method, and eliminating holes, thereby improving the precision of the cloud C and cloud shadow information CS '. Compared with a traditional method, the method has the advantages that the extraction precision of thick cloud is improved by constructing the land brightness temperature threshold empirical linear model, and the extraction precision of cloud shadow is improved by constructing the mountain shadow threshold empirical linear model. Therefore, the method has the advantages of rapidness, high efficiency, convenience and high precision.

The application discloses a kind of night low light condition under fog and haze visibility detection method and system, the method includes: night image data is decomposed into light effect layer and background layer, and light effect inhibiting network is used to carry out light effect area positioning and removal, obtain refined background image;Multi-scale feature extraction is carried out to refined background image, adaptive focusing is carried out to key area using two-way self-attention module, and image visibility visual feature is obtained by cross-scale feature fusion, cross-scale feature interaction and multi-head self-attention calculation;Meteorological factor data is extracted, and high-dimensional meteorological factor feature is obtained;Image visibility visual feature and high-dimensional meteorological factor feature are dynamically fused, then the obtained cross-modal deep fusion feature is input into visibility prediction regression head, and the visibility prediction value is calculated.The application realizes accurate and reliable night visibility detection by deep fusion night video image information and real-time related meteorological factor.

The invention provides an unmanned aerial vehicle real-time obstacle avoidance navigation method and system based on acoustic perception, and relates to the technical field of unmanned aerial vehicle autonomous navigation and flight safety. By using acoustics to perceive obstacles and navigate, an unmanned aerial vehicle is not influenced by light conditions, acoustic signal detection is adopted, and dependence on visible light or infrared light is not needed; the system can still work stably at night and in dark / dark scenes such as tunnels and underground garages, and the problem that a visual sensor cannot be used in a dark state is solved; the radar is resistant to severe weather and particulate matter interference, sound wave propagation is slightly affected by particulate matter such as rain fog, haze and smoke dust, normal detection distance and precision can still be kept even in a dense smoke environment of fire rescue, a dust environment of agricultural plant protection or an outdoor scene in a rainy season, the defect that a laser radar and a millimeter-wave radar are shielded and attenuated is overcome, and the radar is high in detection precision. Omnibearing target coverage is realized, static obstacles can be detected, dynamic targets can be identified, and cooperative targets and non-cooperative targets can be distinguished.

The invention discloses an energy-saving and environment-friendly haze detection device, and relates to the technical field of haze detection.The energy-saving and environment-friendly haze detection device comprises a device body, an air conveying pipe fixedly communicates with the device body, a shielding plate is installed at the upper end of the air conveying pipe in a sealed clamping mode, and a detection assembly is arranged at the lower end of the shielding plate and used for detecting the haze concentration; and a driving assembly is arranged in the device main body. The haze detection device has the advantages that the haze concentration can be detected in real time through the cooperation of the membrane and the sensing assembly, and the power required for sampling during operation of the device is adaptively adjusted according to the detection result, so that the detection effect of the device on different haze concentrations can be effectively improved; meanwhile, the energy-saving and environment-friendly effects of the operation of the device can be improved, and the sensitivity of the device for continuously monitoring the haze concentration and the sensitivity of adaptively adjusting the operation sampling power of the device according to a detection result can be improved through the cooperation of a transmission part and a plurality of scrapers.

The invention discloses a high-precision sky cloud cover detection method, system and sensor, relates to the technical field of cloud cover detection, and solves the problems that a part of detection methods do not construct a standardized reference standard, region calibration is carried out only through discrete RGB numerical value comparison, and data support and verification links are lacked. According to the method, the sky and cloud areas are quickly locked through comparison of the reference basis and the to-be-verified triangle, and misjudgment of a basic area is reduced; secondly, for undetermined areas with similar RGB features, thin cloud image points are accurately screened through double judgment of a double-ratio difference value and a numerical value difference, and the problem that thin cloud and other areas are difficult to distinguish is solved; and a third step of judging a haze area through comprehensive gradient standard deviation by means of graying processing and Sobel gradient analysis, effectively splitting texture feature difference of thin cloud and haze, avoiding cloud amount statistical deviation caused by confusion of the thin cloud and the haze, finally remarkably improving recognition precision of the cloud and the thin cloud area, and guaranteeing accuracy of cloud amount proportion calculation.

The application relates to the technical field of deodorization control, in particular to an air dust suppression, haze reduction and deodorization control system and method based on the Internet of Things. The method comprises the following steps: obtaining three-dimensional point cloud data and point cloud echo signals of a target area; analyzing echo intensity abnormal weakening trends of the point cloud echo signals to calculate dust concentration of air in the target area; calibrating the dust concentration by using the three-dimensional point cloud data to obtain dust concentration three-dimensional gradient distribution data; obtaining wind speed vectors of the target area; constructing an air vortex field model based on the wind speed vectors; using the air vortex field model to predict dust migration paths of the dust concentration three-dimensional gradient distribution data to generate dust migration path prediction data; and determining mobile positioning data of a fog gun based on the dust concentration three-dimensional gradient distribution data. The application realizes fine and dynamic air dust suppression, haze reduction and deodorization control through point cloud modeling, air vortex field prediction and intelligent spraying optimization.