307 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 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 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 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 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 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.

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.

The invention discloses a landscaping landscape design method based on big data, and belongs to the technical field of landscape simulation design, and the method specifically comprises the steps: obtaining microclimate data of a target area and surrounding building environment data, and inputting the data into a dynamic microclimate simulation model to obtain a microclimate dynamic distribution diagram; identifying the position and the type of a microclimate discomfort area based on the distribution map, and generating a landscape design element set; carrying out overlay analysis on the element set and the distribution map, and adjusting the attribute and position of each element to form a preliminary design scheme; the preliminary scheme is input into a model to simulate microclimate distribution after implementation, and the difference degree of new and old distribution diagrams is calculated; and when the difference degree exceeds a preset range, iteratively adjusting the design elements and repeating the optimization process, and outputting a final scheme until the difference degree reaches the standard. According to the invention, dynamic coordination of landscape design and microclimate environment is realized.

The invention discloses a photovoltaic power generation prediction method and system based on micrometeorological information, and belongs to the technical field of power system energy storage equipment, and the method comprises the steps: arranging a sensor in a photovoltaic power generation region, and collecting the meteorological data of the photovoltaic power generation region; extracting micro-meteorological characteristics from the meteorological data, wherein the micro-meteorological characteristics comprise air temperature profile characteristics and wind field vector characteristics; a convolutional neural network and a gating cycle unit are used as basic frameworks, a multi-head attention mechanism is introduced, a hybrid neural network model is established, micrometeorological features are trained, and photovoltaic power generation power is predicted; and adjusting the operation state of the photovoltaic power generation equipment according to the prediction result of the photovoltaic power generation power, and optimizing the power generation efficiency. According to the invention, through the hybrid neural network model, the space-time dependence of meteorological parameters is accurately captured, and the accuracy of photovoltaic power generation power prediction is effectively improved. According to the predicted photovoltaic power generation power curve, the maximum power point tracking parameter is dynamically adjusted to optimize power output, and the influence of power fluctuation on the power grid is reduced.

The invention provides a Beidou geological disaster displacement and image video monitoring fusion method and system based on a high-reliability communication link, and the method comprises the steps: collecting displacement, inclination angle and five-way high-definition video in real time, accessing microclimate and wire clamp temperature data, and carrying out the unified packaging through a data gateway; aPN special line transmission is adopted, and centimeter-level RTK differential service is provided for the unmanned aerial vehicle; based on a Beidou VRS virtual reference station and a long baseline monitoring resolving technology, a Beidou ground-based enhanced station network is multiplexed, and a virtual observation value is generated for real-time dynamic RTK resolving; inverting a three-dimensional water vapor field based on oblique path troposphere delay PWV, realizing tower-level rainfall prediction, and generating a risk assessment report by fusing an A I algorithm; superposing a displacement thermodynamic diagram, a real-time video and an early warning label in a BIM + GI digital twinning scene, and supporting 360-degree panoramic stitching and alarm trigger snapshot; and carrying out intelligent early warning decision. According to the invention, through high-precision Beidou monitoring, multi-modal data fusion and intelligent early warning, the real-time performance, precision and automation level of geological disaster monitoring are significantly improved.

The invention discloses a forest carbon sink dynamic closed-loop regulation and control method and system based on multi-source monitoring data, and relates to the technical field of forest ecological monitoring and carbon sink management, and the system comprises a data collection layer which is used for obtaining multi-source monitoring data including carbon flux data, plant physiological data, microclimate data, soil flux data and sample plot survey data; the intelligent fusion layer is used for processing and feature extraction of multi-source monitoring data and outputting a standardized carbon sink driving factor; the dynamic prediction layer predicts the carbon sink trend through an integrated model by using the standardized carbon sink driving factor; and the regulation and control decision-making layer is respectively connected with the dynamic prediction layer and the forest ecosystem, comprises a regulation and control rule base and a feedback optimization unit, and is used for generating a quantitative regulation and control strategy and optimizing a model and a rule. Therefore, by adopting the forest carbon sink dynamic closed-loop regulation and control method and system based on the multi-source monitoring data, the precision and efficiency of forest carbon sink management can be improved, and real-time and accurate closed-loop regulation and control are realized.

The invention relates to the field of agricultural yield prediction, and particularly discloses a crop yield prediction method and system based on artificial intelligence, and the method comprises the steps: S1, recognizing crop types through the spectral features of a remote sensing image and field sampling data, and dividing corresponding key growth stages; s2, collecting multi-source feature data for each key growth stage, wherein the multi-source feature data comprises remote sensing image data, an extracted normalized difference vegetation index NDVI, an enhanced vegetation index EVI and a topographic factor; acquiring hourly temperature, humidity, precipitation and illumination intensity of each sub-region through a distributed sensor according to micro-meteorological data; obtaining plant height, leaf area index and fruit development parameters through unmanned aerial vehicle three-dimensional imaging according to crop phenotype data; by adopting the technical scheme of the invention, the terrain, microclimate and crop dynamic growth characteristics can be accurately fused, and the problem of non-uniform sample distribution is solved, so that the accuracy and regional adaptability of yield prediction are improved.

The invention discloses a high-salinity desert region pioneer vegetation rapid planting method based on salinity gradient regulation and control, and relates to the technical field of vegetation planting. The method can effectively regulate and control gradient distribution of salinity in the high-salinity desert region, reduce salt stress of a root zone, improve the seed germination rate and the seedling survival rate of pioneer vegetation, effectively inhibit surface salinity accumulation and optimize moisture distribution, thereby promoting rapid field planting and healthy growth of vegetation; by constructing a stable salinity gradient and moisture regulation and control system, through a micro-irrigation system and a mixed matrix layer, the moisture utilization efficiency is optimized, the fresh water consumption is reduced, the vegetation coverage is improved, the soil erosion risk can be reduced, the stress resistance of an ecological system is enhanced, and the vegetation coverage is rapidly improved in a short time; finally, the desert region is promoted to succession towards a multi-layer and stable arbor-shrub-grass community structure, the soil fertility and the microclimate environment are improved, the stability and the wind erosion resistance of an ecological system are enhanced, and reliable technical support is provided for sustainable ecological restoration.

The invention relates to the technical field of indoor environment control, in particular to an energy efficiency optimization and intelligent energy-saving control method and device for an AI five-constant system, and provides the following scheme that indoor and outdoor environment parameters and personnel detection information obtained by a sensing module are combined with an indoor space three-dimensional geometric model, and an energy efficiency optimization and intelligent energy-saving control method and device for the AI five-constant system are obtained. An indoor space is dynamically divided into a target microclimate area and a non-target microclimate area which are formed around people. By respectively determining a five-constant control target parameter set and a differential allowable fluctuation range of the two types of regions, the regulation and control precision is concentrated in the region where the human body is located, and an energy consumption regulation space is reserved in a non-key region. And an energy consumption model is further utilized to construct an operation energy consumption target function, the five-constant-error constraint and the equipment operation constraint are jointly introduced into a multi-target optimization framework, target air supply parameters of the partitioned air supply structure and indoor unit operation parameters are solved, and coordinated regulation and control of the air temperature, humidity, cleanliness, oxygen content and noise are achieved.

The invention discloses a power transmission line icing analysis method and system influenced by microtopography and micrometeorology, and relates to the technical field of power transmission line risk prediction.The method comprises the steps that original meteorological data of an area where a power transmission line is located is collected and fused, and micrometeorological field variables are generated based on topographic parameters extracted by a DEM; on the basis of the fusion data and the micrometeorological field variables, three-dimensional micrometeorological field data are obtained through a fluid mechanics model, the three-dimensional micrometeorological field data are corrected through terrain parameters obtained through a DEM model, an exponential weighted moving average method is used for adjustment, and accurate three-dimensional micrometeorological field data are obtained; based on the accurate micrometeorological field data, the icing thickness is obtained by adopting a physical-empirical hybrid icing growth model, the output icing thickness is adjusted in real time through a dynamic correction algorithm, and a final icing simulation result is obtained. The accuracy of power transmission line icing disaster prediction and the flexibility of emergency response are improved.

According to the fine correction method for the icing microtopography and microclimate of the long-span power transmission line provided by the invention, the icing mass growth model is established, and the liquid water content and the water drop median diameter in meteorological data are determined according to the actually measured icing mass; and establishing a corresponding computational domain and boundary conditions on the basis of the strain tower of the power transmission line so as to train the physical information neural network, then predicting the meteorological distribution state of the power transmission lines on the two sides of the canyon only by using meteorological parameters near the strain tower, and then calculating the icing state according to the distribution state. Therefore, accurate correction of icing prediction is completed, the hypothesis of a uniform meteorological field in the traditional technology is effectively avoided, the accuracy of an icing prediction result is effectively ensured, and the actual working condition is better met.