123 results about "Cloud cover" patented technology

The present invention relates to a solar azimuth estimation method and system based on multi-channel feature enhancement and region-aware attention, belonging to the technical field of intelligent navigation for low-altitude economy unmanned systems. Aiming at the problem of decreased accuracy in solar azimuth estimation based on polarization images under complex cloud cover conditions, the present invention proposes a deep learning framework integrating multi-channel features and direction-aware attention. First, based on polarization light field information acquired by a division-of-focal-plane polarization camera, a three-channel composite input feature composed of a polarization intensity map, an adaptive threshold gradient map, and high-frequency residual edge information is constructed. Second, a ResNet backbone network embedded with a squeeze-and-excitation mechanism is adopted, and a direction-aware polarization attention module is introduced to achieve adaptive fusion of multi-scale features through luminance guidance, deep feature enhancement, and a gradient edge branch.

The invention discloses an intelligent smoke furnace catalysis operation condition suitability grading method. The method comprises the steps that basic parameters of an operation point are obtained; calling a Passive-Toner algorithm to calculate the atmospheric stability grade of the operation point; the continuous cigarette lighting source intensity is calculated according to the used flame bar combustion data; calculating the vertical airflow distribution of the operation point by adopting a Matviyerv vertical velocity theoretical model based on the altitude and vertical airflow observation data of the operation point; based on the continuous cigarette lighting source intensity, calculating the ice nucleus number concentration distribution in the cloud in combination with the vertical airflow distribution and the horizontal wind field; calculating an operation suitability index by adopting a comprehensive index algorithm in combination with the lifting condensation height, the cloud amount and the cloud classification data; and performing grading processing on the job suitability index according to a set threshold value combination so as to output a plurality of job suitability grading results. According to the method, comprehensive analysis is carried out by considering the meteorological condition and the catalytic condition of the operation point, so that the accuracy and the credibility of an operation condition suitability judgment result are improved to a certain extent.

The invention provides an air conditioner indoor environment parameter control method and system, and relates to the technical field of air conditioner control. External environment information and internal heat source information such as sunlight intensity data and cloud cover prediction data outside a building and the operation state and real-time power consumption data of large electronic equipment inside the building are obtained; the preset building physical attribute parameters are combined, the predicted total heat load increment of all the areas in the future preset time can be calculated in advance, an adjusting instruction is generated in advance before the indoor environment parameters actually change, the operation state of an air conditioner system executing mechanism is adjusted, accurate and active control can be achieved, and the control efficiency is improved. According to the method, local supercooling or superheating possibly caused by a traditional method is avoided, the comfort consistency of the indoor environment is remarkably improved, meanwhile, the energy utilization efficiency is optimized, and energy waste is effectively reduced.

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 relates to the technical field of power system grid connection, in particular to an AI-based distributed photovoltaic power station grid connection access capacity evaluation optimization method, system and device and a storage medium. The method comprises the following steps: acquiring topological structure parameters of a power grid, line parameters and historical operation data of a photovoltaic power station, identifying voltage out-of-limit nodes and line overload nodes by simulating the operation state of the power grid under multi-stage access capacity, and determining the bearing capacity boundary of the power grid; time sequence fluctuation characteristics of photovoltaic output are extracted, meteorological data of irradiation intensity, temperature and cloud cover are combined, a photovoltaic output fluctuation range is predicted through an AI algorithm, and an access capacity constraint condition is obtained; analyzing the matching relationship between the power grid regulation resource capacity and the photovoltaic output fluctuation amplitude, calculating the up-regulation margin and the down-regulation margin of the power grid, and determining a capacity safety margin threshold by combining the occupation degree of the photovoltaic access capacity on the regulation resource; and generating an optimal access capacity scheme by integrating a power grid operation state, photovoltaic output prediction and economic benefits through an AI intelligent algorithm.

The present application relates to the technical field of aviation meteorological support, and provides a meteorological display and alarm method, system, device and medium for aircraft navigation, which comprises receiving an aircraft route plan and collecting numerical weather forecasts of each place at the takeoff time of the aircraft; extracting wind speed, temperature, relative humidity, liquid water content and cloud droplet particle number density, and calculating the horizontal visibility, aircraft icing index, total cloud cover and aircraft turbulence index at the forecast time through adaptive attenuation interpolation based on feature perception; constructing an information box and a floating box of the route points on the aircraft route map; and performing meteorological display and alarm for aircraft navigation through the information box and the floating box according to the aircraft route plan. The present application uses regional meteorological forecast results to form meteorological forecast results for flight routes, can intuitively display the comprehensive meteorology on the route and form an alarm prompt, reduces the risk of information omission, increases the safety margin, and reduces the meteorological information processing workload of flight support personnel.

The invention relates to the technical field of irradiance estimation, in particular to an irradiance estimation method and device, electronic equipment and a storage medium, and the method comprises the following steps: obtaining ground site historical data of a research region, reanalysis data of the research region and an aerosol public data set; dividing weather types into clear sky samples and cloudy sky samples according to the obtained historical ground station data of the research area; inputting the clear sky sample, the reanalysis data and the aerosol public data set into a solar irradiance model to calculate clear sky expected irradiance GHIcs; obtaining observation data of a geostationary orbit meteorological satellite, and estimating a cloud cover coefficient CI by using satellite reflectivity; inputting the multi-cloud sky sample and the cloud cover coefficient CI into a cloud index-based semi-empirical model, and calculating a clear sky coefficient Kc; and multiplying the clear sky expected irradiance GHIcs by the clear sky coefficient Kc to obtain an irradiance estimation value under the all-sky condition. Through the improvement of the method, the regional adaptability and the estimation accuracy of irradiance estimation are improved.

The invention relates to the technical field of photovoltaic power generation power prediction, and discloses a photovoltaic power generation system output prediction system and method. According to the method, historical power generation data of a target photovoltaic power station and environment monitoring data including illumination intensity, environment temperature, assembly temperature, cloud coverage and the like are collected; time alignment processing is carried out on the data to form a synchronous data sequence, and then meteorological features and power generation features in the synchronous data sequence are extracted; the method comprises the following steps: constructing a dynamic correlation model of meteorological characteristics and power generation characteristics, calculating an instantaneous influence weight of meteorological fluctuation on power generation power, dividing sensitive time periods and non-sensitive time periods of the power generation power according to the instantaneous influence weight, extracting high-fluctuation meteorological characteristics in the sensitive time periods, and extracting steady-state meteorological characteristics in the non-sensitive time periods; and inputting the high-fluctuation meteorological characteristics into a first prediction network to generate a short-term power prediction value, inputting the steady-state meteorological characteristics into a second prediction network to generate a long-term power prediction value, and fusing the short-term power prediction value and the long-term power prediction value to generate a complete output power prediction curve.

An LSTM-Attention-OOA photovoltaic power prediction method based on similar daily clustering comprises the following steps: firstly, integrating historical photovoltaic power data and meteorological monitoring data (including irradiance, temperature, cloud cover and the like), and analyzing and screening key meteorological characteristics by using grey relational degree; similar daily clustering analysis is carried out through K-means, and an optimal clustering number is determined by adopting an elbow rule. And for different weather clusters, similar days are selected by using cosine similarity. And an LSTM-Attention-OAA prediction model is constructed: an LSTM layer captures a time sequence dependency relationship, an Attention mechanism dynamically allocates each time step weight to highlight an important meteorological period, an OOA is introduced to automatically adjust and optimize hyper-parameters, and autonomous optimization of model performance is realized. According to the method, through a progressive architecture of'feature screening-data clustering-intelligent prediction ', the prediction precision under different weather conditions is remarkably improved, and the method is of great significance in improving stable operation of a power system.

The invention discloses a remote sensing image target recognition method based on an improved YOLOv8 model, and relates to the technical field of remote sensing image target recognition. Data enhancement is implemented by simulating a physical imaging degradation process of atmospheric scattering, cloud layer shielding and solar incident angle change, and a training sample is generated; inputting a training sample into a backbone part of the improved YOLOv8 network, and extracting initial feature maps under different receptive fields by using a C2fMDSC module formed by multi-scale depth separable convolution to realize spatial context capture; based on the initial feature map, introducing a channel and space dual attention mechanism, generating a channel weight according to global statistical features and completing channel re-calibration, generating a space weight based on cross-space dimension analysis, performing element-by-element weighting on the re-calibrated feature map, and outputting an enhanced feature map; and performing multi-scale feature fusion on the enhanced feature map.

The invention discloses a landslide disaster satellite image data generation method and device based on cloud shielding, equipment and a medium, and relates to the technical field of satellite landslide disasters, and the method comprises the steps: obtaining multi-source remote sensing, weather, terrain and cloud-shielded multi-temporal optical satellite image data of a target region; calculating a multi-dimensional landslide physical feature vector in combination with the terrain; fusing multi-source data to finely classify cloud layers, and determining a priority processing area according to the cloud layers, weather and topographic data; the area image and the physical feature vector are input into a landslide feature enhancement generation network containing a double-attention module, through feature extraction, enhancement, fusion and up-sampling recovery, a cloud-free landslide satellite image is generated, credibility evaluation is completed, the interference of cloud layer shielding is effectively inhibited, and the reliability of the landslide satellite image is improved. And the accuracy of the satellite landslide image data generated under the cloud layer shielding condition is improved.

The invention provides an angle control method and device of a photovoltaic panel, electronic equipment and a storage medium, and relates to the technical field of photovoltaic power generation of a power distribution network. The method comprises the steps of obtaining a current inclination angle of a photovoltaic panel, real-time meteorological monitoring data of an area where the photovoltaic panel is located and a sun angle; according to the real-time meteorological monitoring data, determining predicted meteorological monitoring data of the region, the predicted meteorological monitoring data including predicted wind speed, predicted cloud cover and predicted illumination intensity; when the predicted wind speed is greater than a set wind speed threshold value, determining a target inclination angle of the photovoltaic panel according to the current inclination angle of the photovoltaic panel, the wind load corresponding to the predicted wind speed and a wind load threshold value; determining a target azimuth angle of the photovoltaic panel according to the predicted cloud amount, the predicted illumination intensity and the sun angle; and controlling the mechanical adjusting mechanism to adjust the inclination angle of the photovoltaic panel to the target inclination angle and adjust the azimuth angle of the photovoltaic panel to the target azimuth angle. The method is used for achieving the effects of improving the angle control precision and guaranteeing the safety of the photovoltaic panel and the photovoltaic power generation efficiency.

The application discloses a kind of based on unmanned aerial vehicle auxiliary land type identification management method and system, it is related to land type identification technical field, including through unmanned aerial vehicle collection land remote sensing image, and processing and feature extraction, strengthen the land type features in image;Land type identification model is constructed, land type identification standard is set, input is according to the land type feature image after strengthening training and analysis, obtain land type identification result;The identification result is rechecked and corrected, according to the land type after correction is authenticated and managed.The application obtains high-resolution ground surface image using unmanned aerial vehicle, can clearly show the detailed features of ground surface, improve identification accuracy, can obtain image at any time and is not affected by factors such as cloud cover, night lighting etc.Improve identification precision, ensure the accuracy of identification result, help land management and planning, have important application value for disaster emergency, environmental protection and other tasks.

The application relates to an ARMS-based satellite microwave humidity data all-weather assimilation method, which comprises the following steps: collecting YH4DVAR global background field data; calculating the effective particle radius of water condensate; establishing a symmetric error model for ARMS scattering characteristics according to the forecast deviation of cloud and precipitation intensity and spatial position, wherein the symmetric error model expands the observation error into a segmented function of cloud amount or precipitation amount in the mode and observation, and assigns an assimilation weight to the microwave observation data under all-weather conditions; and using the model to assimilate and forecast the all-weather satellite microwave humidity data. The application has good all-weather microwave radiation simulation capability, provides a new observation operator for YH4DVAR all-weather assimilation, and has a wide application prospect in the field of satellite microwave data assimilation.

The invention relates to the technical field of multi-source remote sensing data processing, and discloses a tea whole-process digital early warning and block chain responsibility tracing method, which comprises the following steps: firstly, constructing a terrace planting micro-unit distribution map, and analyzing terrace landform repeated spacing; then acquiring multi-source heterogeneous sensing data, determining a comprehensive spatial resolution and a comprehensive time acquisition interval, and calculating a space-time sampling aliasing index according to the comprehensive spatial resolution and the comprehensive time acquisition interval; and generating a monitoring reliability attenuation factor by using the index, and recovering the anti-aliasing signal by using the monitoring reliability attenuation factor as a regular constraint to obtain a reliability correction risk value. The early warning event is generated according to the corrected risk value, the responsibility traceability allocation weight is calculated by using the monitoring reliability attenuation factor, and finally the early warning event, the responsibility weight and the recovery parameter are stored in the block chain system. According to the method, the problem of observation distortion of the mountain tea garden caused by cloud and mist shielding and terrain breakage is effectively solved, and the accuracy and credibility of risk early warning and responsibility determination are ensured.

This invention relates to the field of satellite remote sensing image processing and meteorological big data analysis technology, specifically a satellite sea surface temperature (SST) reconstruction method based on a spatiotemporally constrained diffusion model. First, it acquires missing Himawari L3 satellite data and OSTIA L4 global data covering a global sea area. Then, it preprocesses the missing Himawari L3 and OSTIA L4 data to obtain several slice data points. Finally, it inputs the preprocessed slice data into a trained spatiotemporally constrained diffusion model, which reconstructs the slice data using a weight file. The reconstructed slice data undergoes texture enhancement and large-scale reconstruction optimization to obtain complete reconstructed data covering the entire sea area. This invention significantly improves the texture restoration accuracy and physical consistency of satellite SST under continuous, large-scale cloud cover.

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.

The invention relates to the technical field of meteorological data processing and visualization, aims to solve the problems of low format conversion efficiency, rigid fusion strategy, cross-source collaborative fault and insufficient interaction in existing multi-source meteorological cloud cover forecast data processing, and provides a fusion processing and interaction analysis method. The fusion processing method comprises the following steps of: receiving a GFS and ICON double-source compressed package (0.25-degree resolution, one file per hour and 7-day data), decompressing and backing up in parallel, directly analyzing a binary file to generate a single-source cloud picture, fusing according to a configurable proportion alpha (0 < = alpha < = 1, default 0.5, and alpha automatically complemented to 1 or 0 when a certain source is missing), generating a visual cloud picture, and storing the visual cloud picture in a corresponding database. The interactive analysis method comprises the following steps: receiving a user space, time and data source / alpha three-dimensional query request, generating a 168-hour three-source comparison curve when point query is less than or equal to 1 second, displaying a fusion cloud picture and a double-source transparent adjustment picture by region query, and supporting single-interface synchronous comparison. The method improves the processing efficiency and scene adaptability, and improves the decision-making efficiency.

The invention provides a water bloom multi-source remote sensing monitoring method and system, electronic equipment and a readable storage medium, and relates to the technical field of water environment monitoring, and the method comprises the steps: obtaining a plurality of first multi-source remote sensing images, carrying out the data processing of each first multi-source remote sensing image, and obtaining a plurality of multi-source data images, each multi-source data image comprises spatial features and spectral features; performing water bloom monitoring according to the spatial features and the spectral features to obtain a water bloom monitoring result, and generating a water bloom report according to the water bloom monitoring result according to a preset first time point; performing cloud cover detection evaluation on each multi-source data image, and acquiring a plurality of stored second multi-source remote sensing images under the condition that the cloud coverage ratio is greater than a preset cloud cover threshold value; and at a preset second time point, updating the water bloom report by using the second multi-source remote sensing image to obtain an updated water bloom report, and displaying a water bloom monitoring result according to the updated water bloom report, so that high frequency and high precision of water bloom monitoring can be met.

This invention discloses a direct snow depth inversion method based on a spaceborne photon-counting lidar, comprising: extracting the target snow-covered area, removing areas with thick cloud cover and non-target surface point clouds, aligning the surface, and performing noise correction; performing deconvolution to obtain a corrected snow attenuation backscattering profile; setting an initial snow absorption coefficient, inverting the backscattering path length distribution of non-absorbing snow, and calculating the first and second moments of this distribution; based on the first and second moments, simultaneously inverting snow depth, snow albedo, and diffuse scattering coefficient; combining the snow albedo and diffuse scattering coefficient to calculate an updated snow absorption coefficient; if the updated snow absorption coefficient does not meet the set convergence threshold, using the updated snow absorption coefficient as the new initial snow absorption coefficient, and repeating the iteration; if it meets the threshold, outputting the final snow depth. Using this invention, along-track snow depth inversion can be achieved directly without relying on any other data.

The invention provides a volume cloud visualization method, device and equipment based on meteorological data and a medium, cloud amount in the meteorological data is converted into a texture picture, and anisotropic three-dimensional noise textures are constructed by combining wind meteorological data with cloud types, so that different noise parameters are set for different cloud types. The method comprises the following specific steps: firstly, acquiring meteorological data and performing data preprocessing, mainly selecting meteorological factors including low, medium and high cloud coverage rates and horizontal wind, and performing data interpolation on the low, medium and high cloud coverage rates to improve spatial resolution; a density change height correlation function is designed according to the cloud type, and a basic three-dimensional contour of the volume cloud is constructed in combination with the cloud coverage rate; using differentiated noise parameters to construct volume textures for different cloud types to perform detail enhancement on the volume cloud; and finally, performing volume cloud rendering. According to the method, the rendering authenticity and efficiency of different types of volume clouds in a large-scale scene are effectively improved.

Disclosed in the present invention is a method for studying the interannual variability of marine heatwaves in the Southern Indian Ocean. The method includes: on the basis of acquired data, exploring the spatial distribution characteristics and linear variation trend characteristics of marine heatwaves in the Southern Indian Ocean; analyzing the relationship between the intensity of marine heatwaves in the Southern Indian Ocean and an El Niño-Southern Oscillation (ENSO) process; analyzing the spatial evolution of marine heatwaves and sea surface temperature anomalies in the Southern Indian Ocean during El Niño events; analyzing the energy contributions of the ocean and atmosphere to marine heatwave events during El Niño events, and exploring the physical mechanism of marine heatwaves in the Southern Indian Ocean; and determining whether marine heatwaves in the Southern Indian Ocean are affected by shortwave radiation and latent heat modulated by the ENSO process. The present invention discovers that during El Niño events, within a study area, low cloud cover decreases while high cloud cover increases, the mixed-layer depth reduces, and shortwave radiation penetrates the sea surface; moreover, northwesterly winds anomalously intensify while climatological southeasterly winds are suppressed, thereby reducing evaporation, decreasing latent heat energy loss, and thus promoting warming.

The application discloses a kind of based on attention mechanism and reinforcement learning's ultra-short-term DNI prediction method, comprising: collecting solar thermal power station historical measured direct normal irradiance data and meteorological characteristic data;Periodic time characteristics and historical lag term are introduced to enhance time series dependence;Utilize bidirectional long short-term memory network to extract DNI time series features, construct attention mechanism based on dynamic change of cloud amount, strengthen the response capability of model to weather mutation;Adopt KAN to replace traditional full connection to high-dimensional nonlinear characteristics Adaptive function approximation is carried out;Introduce PPO reinforcement learning algorithm, dynamically optimize model feature representation and parameter, realize error self-feedback correction;Output DNI prediction result, for the dispatching and operation control of concentrating solar thermal power generation system.The method can effectively improve the DNI prediction accuracy and robustness under complex weather conditions, provide high-precision prediction support for photo-thermal power generation and renewable energy grid connection.

The invention provides a moving ship self-adaptive tracking method based on space-time semantic association, which comprises the following steps of: firstly, adaptively determining an area range in which a target possibly appears in a current frame by combining collaborative prediction of ship movement track coherence and appearance feature consistency, greatly reducing a matching search space and reducing calculation overhead, and improving the tracking accuracy of the target in the current frame; then, the detection targets meeting the physical motion law and the target size change range are screened in the associated area, so that the matching accuracy is remarkably improved, and for missed detection targets caused by cloud layer shielding or signal weakening, the prediction positions of the missed detection targets are directly adopted for position covering to fill detection blind areas and avoid transient loss, so that the detection accuracy is improved. And finally, by utilizing the characteristics of irregularity, short life cycle and the like of the broken cloud motion, setting motion variable thresholds of speed, acceleration, course and the like and a continuous leak detection frame number threshold, and automatically filtering transient pseudo tracks which do not meet stable motion characteristics, thereby greatly reducing the false alarm rate and improving the accuracy of the false alarm. Therefore, accurate tracking of a real ship target is still kept in a multi-broken-cloud scene.

The invention relates to the technical field of power prediction of photovoltaic power generation, in particular to a photovoltaic power prediction method based on geographic information and numerical weather forecast, which comprises the following steps of: according to parameters such as known geographic information and orientation of a power station, combining temperature, wind speed and cloud cover data in numerical weather forecast service disclosed on the Internet; and determining the influence of each factor on the generating capacity through a simple trigonometric function and linear calculation. And finally, the prediction data of the generating capacity in the next 24 hours is updated once per hour. The prediction process mainly considers the influence of cloud cover, temperature and wind speed power station characteristics on the photovoltaic power station generation power.

The invention discloses a distributed photovoltaic power station power prediction method and system. The method comprises the following steps: collecting meteorological balloon data by using a reciprocating meteorological balloon; constructing an initial graph structure, and dynamically adjusting the connection weight between the distributed photovoltaic sites according to the real-time cloud cover data to obtain an updated graph structure; performing space-time interpolation on the meteorological balloon data by using a Kalman filtering algorithm, and constructing a four-dimensional data structure based on a space-time interpolation result; using 3D-CNN to extract the four-dimensional data structure to extract vertical meteorological features, using a space-time diagram convolutional network to construct an inter-site power propagation relationship based on the updated graph structure, and using Transform to extract time sequence features of the four-dimensional data structure; and based on the vertical meteorological characteristics, the inter-station power propagation relationship and the time sequence characteristics, dynamic weight fusion is realized by using an attention mechanism, and a 0-24-hour multi-time-scale distributed photovoltaic power prediction result is obtained.

The invention discloses a remote sensing surveying and mapping data processing method and a remote sensing surveying and mapping data processing system, which relate to the technical field of remote sensing surveying and mapping, and realize pixel-level gray scale reconstruction control under the effect of multiple interference superposition by extracting and processing a remote sensing image sequence IMGSEQ and a time sequence TIMESEQ, establishing a time drift feature vector VTD and calculating a combined correction matrix CCM. The method comprises the following steps of: obtaining a correction image sequence IMGCOR, outputting the correction image sequence IMGCOR, and finally evaluating the spectral stability of the correction image sequence IMGCOR and the original remote sensing image sequence IMGSEQ by calculating a consistency index SIM, so that the consistency improvement degree of the image on the time sequence can be quantified, and the accuracy of the image correction is improved. Compared with the prior art in which a fixed value correction or linear regression mode is usually adopted to ignore time drift and observation environment coupling interference, the method can adapt to gray shift caused by cloud layer shielding, illumination angle change or terrain height difference in the remote sensing image, and can accurately identify spectrum abnormity of a local area at a specific moment.

The invention discloses a new energy power prediction optimization method for dynamic evaluation of multi-source heterogeneous meteorological data, and relates to the technical field of meteorological prediction, data access and source fingerprint identification, weather event evidence chain construction and credibility dynamic evaluation, prediction model structure self-adaption and multi-model fusion, online calibration and audit log management, and the like. Unified access and storage of multi-source meteorological data such as wind speed, temperature and humidity, cloud cover and echo intensity are realized through source fingerprint identification, a multi-source standardized data set is formed through space-time alignment and semantic unification, and data quality is dynamically evaluated by combining observation health degree vectors and equipment state signals. According to the method, a weather event evidence chain is constructed, a credibility weight vector and a scene label are output, prediction model adaptive configuration and multi-model fusion are realized, deviation calibration and correction are performed based on actual power receipt data, an audit log and a version snapshot are generated, and the accuracy, stability and traceability of new energy power prediction are integrally improved.

This invention discloses a spatiotemporal reconstruction method and system for coastal vegetation indices based on geographic logic gating and multi-agent systems, belonging to the fields of remote sensing ecological monitoring and deep learning. The method acquires and preprocesses multi-temporal high- and low-resolution remote sensing images to construct NDVI spatiotemporal feature sequences. Spatiotemporal features are obtained through deep encoding, and a multi-agent system incorporating information on vegetation, cloud cover, tides, and topography is established. Coastal geographic logic constraints are introduced to construct multi-scale spatiotemporal differences and generate geographic logic gating, which weights and modulates the spatiotemporal features to enhance reliable information and suppress interference noise. Multi-agent collaborative reconstruction is achieved under multi-objective constraints, outputting high-resolution vegetation indices and supporting edge deployment and online learning. This invention effectively overcomes problems such as cloud and fog obstruction, tidal inundation, and data gaps, significantly improving reconstruction accuracy, geographic rationality, and spatiotemporal continuity. It is more adaptable to the complex ecological environment of coastal zones, providing a stable and reliable technical means for long-term dynamic monitoring of vegetation.