339 results about "Microclimate" patented technology

The invention relates to the technical field of wind power prediction. The invention provides a wind power prediction method and system. The method comprises the following steps: acquiring multi-dimensional meteorological time series data, three-dimensional elevation data and unit operation data of a target wind power plant; constructing a spatial-temporal feature fusion network, extracting time sequence dynamic features, and performing weighted fusion on the spatial correlation features and the time sequence dynamic features to obtain a fusion feature vector; establishing a hybrid prediction model, and taking the fusion feature vector as input to obtain a wind power initial prediction result; introducing a terrain correction factor, constructing a turbulence intensity compensation function, and performing micro-terrain disturbance correction on the wind power initial prediction result; and outputting a final power prediction curve and a confidence interval. The problems that in an existing wind power prediction method, a physical model is insufficient in complex terrain microclimate modeling precision, high in calculation complexity and difficult to meet the real-time requirement, a statistical learning method is limited in high-dimensional nonlinear time sequence feature expression capacity, and prediction errors are remarkably increased under the abnormal working condition are solved.

The invention relates to the technical field of air conditioner cooling load prediction, and discloses an air conditioner cooling load prediction method and system. The method comprises the following steps: acquiring temperature, humidity and CO2 data through an environment sensor network and carrying out standardization processing; establishing a time slice matrix based on the environmental parameters and the personnel density, and obtaining user behavior characteristics; constructing a wall heat storage model by combining the environmental parameters, and calculating instantaneous heat flow; establishing a spatial downscaling model according to the heat flow and meteorological data; time sequence features are extracted based on local weather and heat flow data, and the cooling load is predicted; and a time-phased refrigeration control instruction is generated accordingly. By establishing a data processing and analysis process, factors such as building microclimate characteristics, user behavior modes and wall dynamic thermal characteristics are comprehensively considered, and more accurate prediction of the cooling load of the air conditioner is realized.

The invention discloses a chemical safety risk management and control method and system, and relates to the technical field of risk management and control. Comprising the following steps: acquiring climate data of each region in a chemical plant in real time to obtain microclimate information of different regions; deploying a gas sensor to monitor the gas concentration change and calculate the gas leakage rate; combining the climate data and the gas leakage rate, and adopting a computational fluid mechanics method to establish a gas diffusion prediction model; and in combination with the output of the diffusion prediction model, judging the gas leakage risk level of each region, setting different early warning thresholds according to the risk levels, and automatically triggering an early warning mechanism when the set thresholds are reached. The method not only considers climatic factors, but also combines the change of the factory environment, and can update the prediction result of the diffusion path and the potential dangerous area in real time.

The invention discloses an intelligent garden management method and system based on big data, and belongs to the technical field of garden management. Microclimate region division is carried out on a garden through a clustering algorithm, and the region representative weight of each sensor is calculated in combination with a data heterogeneity response factor and a microtopography interference factor; weighted fusion of sensor data is realized, and a more representative environment estimation value is generated; according to the method, prediction and anomaly detection are carried out on a plant growth environment trend based on time sequence analysis, a garden regulation and control instruction is automatically generated, actual plant state feedback is introduced to realize dynamic optimization and adjustment of a weight, and the method effectively improves the accuracy of environment perception and the intelligent level of decision response, and improves the user experience. And resource misuse and plant health risks caused by information distortion are obviously reduced.

The invention discloses a monitoring system for forest ecological carbon sink calculation, and belongs to the field of ecological environment monitoring and carbon sink evaluation. Comprising a foundation optical remote sensing module, a crown biomass calculation module, a soil carbon component identification module, a vertical carbon flux detection module, a regional microclimate analysis module, a time sequence big data coupling module, a carbon source and carbon sink model fusion module and a result correction feedback module. The method comprises the following steps: collecting vegetation three-dimensional structure data through a multi-frequency laser radar, calculating unit-area canopy carbon storage acceleration in combination with seasonal climate interference parameters, and fusing turbulent carbon exchange and soil carbon change data to construct a regional carbon flux coupling model; the system collects cross-rosa chinensis carbon change data to construct a multi-dimensional feature tensor diagram, dynamically updates a weight and a fitting function, realizes carbon sink dynamic estimation and feedback optimization based on a biology-climate-time three-dimensional cooperation mechanism, and improves forest carbon sink monitoring precision and stability. The beneficial effect is that scientificity and accuracy of ecological environment monitoring and carbon sink evaluation are improved.

The invention relates to an irrigation amount control method and system for prune cultivation. The method comprises the steps of performing timestamp calibration and dynamic weight distribution processing according to soil data, meteorological data and transpiration data to obtain a multi-modal data set; based on the multi-modal data set, microclimate abnormal event detection and soil humidity correction processing are carried out, and anti-interference soil water content data are obtained; according to the anti-interference soil moisture content data and preset prune growth stage parameters, carrying out root system layered water absorption dynamic modeling treatment to obtain a deep root system moisture demand prediction value; according to the deep root system moisture demand prediction value and the transpiration data, irrigation amount dynamic planning processing is carried out, and the target irrigation amount is obtained. According to the method, through multi-source data fusion, root system water absorption modeling processing and dynamic irrigation planning, the target irrigation amount of prune cultivation can be accurately determined, the accuracy and scientificity of irrigation management are remarkably improved, and powerful guarantee is provided for high-quality growth of prunes and efficient utilization of water resources.

The invention discloses a green building microclimate regulation emission reduction and convergence increase method based on park biodiversity, and the method comprises the steps: obtaining biodiversity data and building environment parameters, constructing a three-dimensional ecological space digital twinborn model, and carrying out the multi-physics coupling analysis; loading boundary conditions to generate a comprehensive model, then carrying out microclimate dynamic simulation, training an ecological regulation model to generate initial regulation parameters, constructing a multi-objective optimization model in combination with dynamic regulation simulation information and building design parameters to obtain optimal parameters, and finally adjusting a regulation strategy based on real-time prediction. According to the method, by integrating biodiversity and building environment data and utilizing digital twinning, multi-field coupling simulation and intelligent optimization algorithms, collaborative optimization of microclimate adjustment, carbon emission reduction and carbon sink capacity improvement is achieved, a technical scheme is provided for deep fusion of green buildings and ecological systems, and the method is suitable for popularization and application. The method is suitable for the fields of urban building energy conservation and ecological environment protection.

The invention discloses an urban blue-green space microclimate prediction system and method based on AI fusion. The method comprises the following steps of: 1, acquiring field microclimate measured data through linkage of a portable weather station and a GPS (Global Positioning System); 2, utilizing a machine learning downscaling algorithm and a deep learning semantic segmentation technology to extract high-resolution land surface temperature and urban land surface coverage classification data from the remote sensing image; 3, constructing a time-space aligned multi-modal GIS data set, and constructing a high-precision microclimate prediction model by using an ensemble learning algorithm; and 4, introducing an SHAP interpretability framework, carrying out deep analysis on the model, and quantifying the contribution degree of various environmental elements to a microclimate prediction result and the complex nonlinear influence of the contribution degree. According to the method, unprecedented high-precision and explainable scientific decision support can be provided for formulating urban planning, landscape design and thermal environment mitigation strategies, and development of healthy, sustainable and climate-flexible cities is powerfully promoted.

The invention discloses an AIoT-based farm weather and soil environment monitoring automatic inspection unmanned vehicle, which integrates an all-terrain adaptive mobile platform, a multi-mode mechanical arm and an intelligent environment sensing module to construct a whole-process unmanned operation system: autonomous obstacle avoidance and path planning of a complex farmland are realized through laser navigation and a three-dimensional environment sensing technology; a switchable execution mechanism is integrated at the tail end of the mechanical arm, accurate loading, unloading and transferring of a fruit basket are completed by utilizing a self-adaptive clamping module, and soil in-situ sampling is realized by combining a soil probe; a multispectral visual system is carried, crop maturity and pest and disease distribution are synchronously analyzed based on a deep learning algorithm, and canopy microclimate data are dynamically acquired in cooperation with a liftable environment monitoring device. The technical bottleneck that traditional agricultural equipment is single in function and poor in collaboration is broken through, intelligent collaboration of efficient turnover of fruit baskets, lossless sampling of soil and multi-dimensional monitoring of the growth environment is achieved, and an all-weather closed-loop solution is provided for precision agriculture.

The invention discloses a photovoltaic power generation power prediction method and system based on multi-source data fusion. The method comprises the steps of obtaining historical operation data of a target photovoltaic power station and historical meteorological data corresponding to the historical operation data; based on the historical operation data and the historical meteorological data, constructing and training a historical deviation mode correction downscaling model, and correcting future coarse resolution meteorological forecast data provided by a numerical weather forecast model to obtain refined meteorological forecast data of a target photovoltaic power station site scale; on the basis of the refined meteorological prediction data, utilizing a physical-statistical coupling prediction model to predict the future generation power of the target photovoltaic power station; according to the historical deviation mode correction downscaling model, a targeted correction function is established by analyzing a systematic deviation mode between a theoretical prediction value and an actual observation value in historical data, and conversion from coarse resolution prediction to site scale microscopic meteorology is realized, so that the photovoltaic power generation power prediction precision is improved.

The invention discloses a high-precision distributed photovoltaic output prediction method and system based on micrometeorology and feature hierarchical clustering, and relates to the technical field of distributed photovoltaic output prediction.The method comprises the steps that numerical weather forecast data are collected, an initial micrometeorological field is generated through space-time alignment and self-adaptive KNN interpolation, and the initial micrometeorological field is subjected to feature clustering; a WRF-LES system and a bidirectional LSTM are combined to establish cross-scale mapping, a dynamic residual correction field is fused to generate hectometer-level high-resolution micrometeorological data, and the problem of insufficient resolution of traditional numerical forecasting is solved. MIC and PA-DTW are used for jointly analyzing the characteristics of the power station, and dynamic clustering is achieved through a sliding time window and incremental spectral clustering. According to the method, a physical information graph network and causal expansion convolution are coupled to extract features, federal learning cross-power-station cooperative training is combined, the distributed photovoltaic output prediction precision and robustness are improved, and privacy security is considered.

The invention discloses an indoor environment control method based on a two-stage function, and belongs to the field of indoor environment control, and the method comprises the steps: obtaining the division information and function demand data of a multifunctional region, including the temperature, humidity preference and personnel activity density of each region, carrying out the clustering processing of the demand data through a region function analysis module, and obtaining a region function analysis module; obtaining a microclimate regulation target parameter set of the multifunctional region; according to a parameter transition function set according to a boundary condition and an equipment regulation and control signal, a cooperative control strategy is adopted to optimize multi-region microclimate regulation, and a distributed optimization algorithm is adopted to perform iterative coordination on the regulation and control signal of each region to obtain a globally optimized environment control execution scheme; and the optimized control execution scheme is issued to execution equipment, including an air conditioner, a humidifier and a fan, of each area through an environment control execution module, a control signal is transmitted by adopting a real-time communication protocol, and a dynamic adjustment result of indoor environment parameters is obtained.

The invention discloses a travel itinerary navigation method, device and equipment and a storage medium, and the method comprises the steps: collecting user biological rhythm parameters, cultural heritage activation indexes and multi-dimensional data of microclimate evolution prediction in real time, carrying out the feature weight dynamic distribution of the fusion of the multi-dimensional data through a quantum annealing algorithm, and constructing a four-dimensional decision space; based on a four-dimensional decision space, the tourist density, the cultural relic protection critical value and the commercial facility bearing capacity are mapped to an Isin spin system, a spin state evolution path is optimized in combination with a quantum walking algorithm, and an optimal solution set containing multiple candidate paths is generated; bio-rhythm parameters, emotional changes and scenic spot environment data of tourists are collected in real time, a quantum annealing algorithm and a quantum walking algorithm are adopted to perform deep fusion and optimization on multi-dimensional data, and tourism recommendation routes, information pushing frequency and feedback intensity are dynamically adjusted according to instant demands of the tourists. And the tourism experience of tourists is optimized.

The invention discloses an urban ventilation corridor construction method based on local climate region classification and remote sensing inversion, and belongs to the technical field of urban climate regulation and space optimization. According to the method, a multi-scale urban thermal environment analysis framework is constructed, a coupling mechanism of an urban form and a thermal effect is systematically identified from macroscopic, mesoscopic and microscopic levels, and fine modeling and regulation and control path identification of the thermal environment are realized. Meanwhile, in combination with machine learning and interpretability analysis methods, scientificity and transparency of thermal environment driving factor identification are enhanced. Through combination of remote sensing inversion, morphological factor construction and three-dimensional analogue simulation, a complete technical path of cold source identification, resistance modeling, path optimization and microclimate response evaluation is provided, and the aeration-cooling potential identification capability of blue-green infrastructures is effectively improved. The method is suitable for wind gallery planning and climate adaptation type update design of various urban spaces, and has wide popularization and application prospects.

The invention discloses a poplar degenerated forest stand accurate restoration method, and relates to the technical field of poplar degenerated forest stand restoration, and the method comprises the steps: obtaining a multi-temporal satellite remote sensing image and an unmanned plane multi-spectral image of a target area; in combination with soil moisture content, rhizosphere respiration rate and micrometeorological data collected by ground Internet of Things sensing nodes arranged in a forest stand, geographic coordinate registration and time sequence alignment are performed on the data to form a multi-source observation data set; the method comprises the following steps: extracting vegetation indexes, short-wave infrared reflectivity, soil moisture and rhizosphere physiological parameters based on a multi-source observation data set, constructing a degradation diagnosis index fusing spectral change, moisture stress and root activity, calculating the degradation degree of each pixel, and generating a degradation level spatial distribution diagram; the area with the degradation degree reaching a set threshold value is determined as a to-be-repaired area; and selecting representative poplar individuals in the to-be-repaired area, measuring the photosynthetic ability of the individuals, and calculating the individual competition intensity in combination with the three-dimensional point cloud data.

The invention discloses an interactive garden design method and system based on landscape garden simulation, particularly relates to the technical field of garden landscape design and computer graphic simulation, and relates to a plurality of modules of environmental data acquisition, three-dimensional modeling, plant growth simulation, water simulation, interactive design, microclimate prediction and the like. According to the method, terrain, climate and soil data are collected through a laser radar and multiple sensors, and a standardized environmental map is constructed; generating a plant topological structure based on a context sensitive type L system, and simulating a growth process by adopting a Logistic function in combination with an environmental factor; the method comprises the following steps: realizing dynamic simulation of a water body by utilizing an SPH particle method and a Navier-Stokes equation, and establishing a plant-water body feedback model; and real-time operation and design optimization are supported through a three-dimensional interaction interface. The system has the advantages of being high in ecological adaptability, high in visualization degree, perfect in feedback mechanism and the like, the intelligence and scientificity of garden design are remarkably improved, and the system is suitable for various scenes such as urban green lands and ecological restoration.

The invention discloses a desert region water resource recycling method based on ecological restoration coupling, and the method comprises the steps: building a moisture-vegetation coupled spatial dynamic model through obtaining soil humidity distribution, vegetation coverage and regional microclimate data, and recognizing a suspected water resource retention region; the water resource circulation potential is evaluated in combination with microclimate fluctuation characteristics and earth surface characteristics, and an actual water resource optimal utilization area is screened; according to the water resource circulation potential, the vegetation planting density, variety and irrigation mode are dynamically adjusted, the ecological restoration gain coefficient is calculated, and collaborative optimization of the water resource utilization efficiency and the ecological restoration effect is achieved. Water resource management and ecological restoration are organically coupled, accurate utilization and efficient circulation of water resources are achieved, and the restoration capacity and sustainability of an ecological system in the desert area are remarkably improved.

The invention discloses an urban block green land planning method and device based on thermal environment big data, and the method comprises the steps: obtaining the thermal environment data, vegetation coverage data and a vegetation database of a target block; constructing a thermal environment map of the target block based on the thermal environment data, and obtaining a thermal environment feature vector of each sub-block according to the thermal environment map; acquiring a microclimate influence value of each vegetation type; performing correlation analysis on the thermal environment feature vector and the microclimate influence value of each sub-block to obtain the microclimate adjustment contribution degree of each vegetation type to each sub-block; and according to the microclimate adjustment contribution degree, determining a target vegetation type to be planted in each sub-block and a planting proportion of each target vegetation type, and obtaining a vegetation configuration scheme of the target block. The microclimate adjusting capability of the urban green land can be improved.

The invention discloses a method for constructing an evaluation model for the influence of climate change on biodiversity, and particularly relates to the technical field of ecological modeling and climate response analysis. The method comprises the following steps: generating a high-resolution characteristic grid based on microclimate disturbance data and a remote sensing image, extracting ecological plaques and establishing a heterogeneity spatial index model, constructing a connectivity map and a coupling response path library, extracting a typical species response mode by using nonlinear dimension reduction and spectral clustering, and performing dynamic fitting in combination with biological survey data. And finally, generating a diversity attenuation trend prediction curve and a risk thermodynamic diagram, identifying a biodiversity collapse critical point and outputting an intervention priority, and the method can be widely applied to regional ecological early warning and protection planning.

The invention relates to the crossing field of biothermodynamics and dynamic environment control, and discloses a space thermal microclimate multi-target regulation and control method, device and system, a medium and a product. The method comprises the following steps: acquiring physiological parameters (including effective dressing thermal resistance and local sweat gland density of posture correction) of a passenger, environment space-time parameters and light environment parameters through a biological characteristic sensor, an environment sensor and an optical sensor; constructing a dynamic human body thermal comfort model library, and adopting a feature coding-parameter decoding mechanism to match a target thermal comfort model; generating a hierarchical regulation strategy based on the difference between the local comfortable temperature and humidity interval and the real-time thermal environment sensing parameter; and the refrigerant circulation system, the fluid circulation system and the optical radiation system are cooperatively controlled to realize local microclimate regulation and control. According to the method, the problems of inaccurate thermal comfort modeling and low multi-system cooperation efficiency in a transient environment are solved, the energy efficiency ratio is improved, and the comfort of passengers in a cabin is remarkably improved.

The invention discloses an urban environment monitoring regulation and control system and method based on global greening data. The system comprises an image acquisition module, a monitoring module, an image analysis module, a sampling screening module, a global speculation module and a strategy generation module. The image acquisition module periodically acquires greening image data; the monitoring module collects vegetation monitoring data through a sensor group; the image analysis module extracts vegetation coverage areas and classifies the areas according to texture feature parameters (leaf area index, canopy height and texture uniformity); the sampling and screening module calculates a standard value of each unit area and screens and optimizes sampling areas; the global speculation module is combined with the sampling data to calculate monitoring data of an uncovered area; and the strategy generation module generates a targeted microclimate regulation and control strategy according to the monitoring data and the classification result. According to the invention, image analysis and sensor data are combined, precise monitoring and intelligent regulation and control of vegetation distribution are realized, and the ecological function and microclimate regulation and control effect of an urban greening area are improved.

The invention discloses a farmland environment intelligent monitoring system and method based on multi-modal sensor fusion, and relates to the technical field of farmland environment monitoring, and the method comprises the steps: obtaining first integrated data in a farmland, and carrying out the preprocessing of the first integrated data; constructing a sound wave attenuation model based on the preprocessed first comprehensive data, and inverting the air humidity and the vegetation density through the sound wave attenuation model; conflict detection and resolution processing are carried out based on an inversion result; and constructing a three-dimensional microclimate field based on a conflict resolution result, and carrying out verification and iterative optimization by fusing a crop physiological model. According to the method, farmland space-time heterogeneity is adapted by means of a dynamic space-time weight coefficient, so that a three-dimensional microclimate field covering horizontal and vertical dimensions is constructed, and a'monitoring-verification-optimization 'closed loop is formed through crop physiological model association and iterative optimization; the problems of limitation of a single sensor, low inversion precision, poor data continuity, extensive monitoring and lack of decision association in the prior art are effectively broken through.

The invention discloses a microclimate regulation control method and system for cellaring of salty wine in a salt cavern, and relates to the technical field of environmental control, and the technical scheme is characterized in that non-intrusive accurate inversion of the concentration in a tank is realized by constructing a mass transfer kinetic model and combining real-time environmental parameters collected by a sensor and the real-time concentration outside the tank of flavor substances; based on historical cellaring data, a concentration change reference curve is obtained through data fitting, a scientific quantitative basis is provided for microenvironment regulation and control, a cellaring period is divided according to the change trend of environmental parameters in the concentration change reference curve, and the cellaring time is shortened by accurately quantifying the material change rule of different cellaring periods; meanwhile, a feed-forward-feedback composite regulation and control mechanism is introduced, a feed-forward compensation regulation and control quantity and a PID regulation and control quantity are fused, the strong coupling relation between the temperature and humidity in the salt cavern and the gas concentration is separated, multivariable coupling interference in the salt cavern environment is effectively eliminated, and precise closed-loop control over the salt cavern microclimate is achieved.

The invention discloses a load management operation method for comprehensive energy optimal configuration, and relates to the technical field of energy management, and the method comprises the following specific steps: microclimate partition division: carrying out the comprehensive field exploration of a target region, collecting the landform, building layout and vegetation coverage data, analyzing the influence of each factor on the microclimate formation, and carrying out the calculation of the microclimate; according to the method, a partition division basis is determined according to the difference between airflow and heat caused by topographic relief, the influence of a building on sunlight and ventilation and local microclimate characteristics of vegetation construction, and a linkage model comprehensively considering the relationship among a microclimate partition state, a building adjustment behavior and an energy load is established; the complex relation among microclimate parameter changes, building adjustment action changes and energy load changes is accurately reflected, meanwhile, according to state changes after building adjustment, the energy management system can dynamically optimize an energy configuration scheme, unnecessary energy transmission and conversion links are reduced, and the energy utilization rate is improved. And deep optimization configuration of comprehensive energy of the whole area from generation, transportation to use is realized.

The invention relates to the technical field of power transmission line operation and maintenance, in particular to a power transmission conductor galloping prediction method based on an ice wind load coupling effect, and the method comprises the steps: collecting micrometeorological data, icing parameters, conductor tension, span and other multi-source monitoring data in real time; constructing a three-node cable unit model containing a torsional degree of freedom based on the icing parameters, and simulating an asymmetric icing form; constructing a spatial large rotating beam unit model of the transmission tower based on Beidou positioning and point cloud scanning data; coupling the models, inputting wind speed, wind direction and temperature parameters, and calculating a galloping amplitude, a galloping frequency and an alternating stress amplitude through a self-adaptive step size algorithm; dynamically generating an early warning threshold according to the voltage grade and the span; when the galloping amplitude exceeds a threshold value, triggering graded early warning and outputting an affected tower disposal suggestion; through precise modeling and multi-source data fusion, the galloping prediction precision and response speed are improved, dynamic and precise early warning is realized, and safe and stable operation of the power transmission line can be effectively guaranteed.

The invention discloses a thermal power plant intelligent circulating water energy-saving optimization system control method, relates to the technical field of thermal power plant automatic control, and aims to solve the technical problems of low control precision and large energy consumption of a traditional thermal power plant cold end system and realize optimization control by constructing an intelligent control system based on a Transform deep learning architecture. An intelligent weather data acquisition network is established, meteorological data, satellite meteorological data and a local microclimate sensor network are integrated, and high-precision space-time meteorological information is obtained; according to the method, a Transform encoder-decoder architecture is adopted, multi-source data is converted into a standardized token for representation, and complex association analysis is realized through a multi-head self-attention mechanism; a multi-objective collaborative optimization framework with an objective function as a core is constructed, a steam turbine-condenser-cooling tower-circulating water pump control closed loop is established, and seasonal adaptive control and robust optimization are achieved; providing decision transparent analysis, and explaining a control decision process through attention weight distribution.

This document describes systems, methods, devices, and other techniques used for determining humidity properties of a microclimate of a bed system. One aspect is A bed system comprising a mattress, at least one temperature sensor, and a controller configured to receiving signals from the at least one temperature sensor indicating a detected temperature of a microclimate of the bed system and determine humidity properties of the microclimate of the bed system based at least in part on the detected temperature.

The invention discloses a line icing early warning analysis method and system combined with microtopography and microclimate, and relates to the technical field of power transmission channel risk early warning, and the method comprises the steps: obtaining the geographic information of a to-be-monitored line, carrying out the region division, and obtaining a plurality of fragmented monitoring regions; judging the icing degree of each fragmented monitoring area and calculating an icing initial coefficient; obtaining an effective microtopography factor group and an effective micrometeorological factor group based on the icing initial coefficient; calculating a first icing influence coefficient based on the effective microtopography factor group, calculating a second icing influence coefficient based on the effective micrometeorological factor group, calculating an icing development coefficient according to the first icing influence coefficient and the second icing influence coefficient, and predicting the icing trend based on the icing development coefficient. According to the analysis method and system, the microtopography influence and the micrometeorological influence are analyzed, and the influence of the microtopography factors on the micrometeorological factors is also considered, so that the icing development degree is analyzed, and higher prediction precision can be ensured.

The invention discloses a wind power plant wind speed multi-strategy integrated prediction method and system and related equipment, and the method comprises the steps: obtaining the operation data and multi-source meteorological observation data of a wind power plant monitoring and data collection system, and carrying out the time alignment and data cleaning; constructing multi-dimensional meteorological data features based on the processed operation data and the multi-source meteorological observation data; the multi-dimensional meteorological data features are comprehensive feature spaces including time sequence features, physical features and microclimate features; carrying out clustering analysis on the multi-dimensional meteorological data features by adopting a K-means clustering algorithm, and automatically identifying a microclimate mode type; a Light GBM prediction model is constructed for each microclimate type; and fusing prediction results of the data driving model, the microclimate adaptive model and the physical constraint model, and generating a final wind speed prediction value through dynamic weight distribution. According to the method, the influence of microclimate condition space-time variability on the prediction performance is solved, and technical support is provided for wind power plant operation decision making, power generation capacity evaluation and power grid dispatching.

The invention discloses a method for observing local heavy rainfall in an alpine region, and particularly relates to the technical field of rainfall observation. In order to solve the problem of systematic prediction deviation caused by the fact that a traditional model mistakenly takes the altitude as a positive correlation covariable in a mountain leeside slope or a sinking airflow control area, a local slope direction difference index and a water vapor potential disturbance index are innovatively introduced as alternative covariables, and the actual relation between the altitude and rainfall is dynamically judged through multivariate correlation analysis. Therefore, the covariable setting of the common Kriging model is flexibly adjusted; through residual analysis and systematic deviation value calculation, the method further achieves the recognition and spatial correction of rainfall hot spots and microclimate characteristics, and remarkably improves the accuracy of interpolation prediction and the reliability of regional climate risk recognition.