97 results about "Weather parameter" patented technology

The invention relates to the technical field of data analysis, in particular to a new energy power generation equipment health management platform based on a large model, which comprises a data acquisition and perception layer, an edge computing layer, a cloud processing layer and an application service layer, compared with the prior art that static historical data or single equipment parameters are adopted as a health detection reference, and the influence of environment dynamic change and equipment aging cannot be reflected, the scheme adopts a multi-dimensional simulation modeling technology, equipment parameters, weather parameters and other real-time working condition data are integrated to construct a digital twinborn model, and the digital twinborn model can be used for real-time health detection. Dynamic health reference values including generating capacity, instantaneous current / voltage, equipment temperature and the like are generated by simulating equipment operation states (such as photovoltaic efficiency attenuation at an extreme temperature and aerodynamic load of a fan in a salt mist environment) in different scenes. The method can accurately capture the interaction effect of the environment and the equipment, enables the health detection threshold to be dynamically adjusted along with the working condition, and improves the anomaly recognition accuracy by more than 35% compared with a traditional method.

The invention discloses a short-term power load prediction method in combination with weather parameters. The method comprises the following steps: acquiring power load data and weather parameters of corresponding time; preprocessing the data; using an EEMD method to decompose the power load data into a high-frequency nonlinear component and a low-frequency linear component; in combination with weather parameters, training a CNN-LSTM model which introduces an attention mechanism and is based on multi-scale feature extraction, and outputting a high-frequency nonlinear component predicted value; for the low-frequency linear component, a time sequence model SARIMA is adopted, and a low-frequency linear component predicted value is output; and adding the high-frequency nonlinear component predicted value and the low-frequency linear component predicted value to obtain a complete load prediction result, carrying out error analysis, and carrying out iterative optimization on the model. According to the method, weather data are introduced, high-frequency nonlinear components are extracted through sequence decomposition, weather parameters are used as input to construct a deep learning prediction model, and the prediction capability of complex load fluctuation and the precision and reliability of short-term load prediction are improved.

A method for determining improved flight trajectory. The method includes receiving one or more weather parameters to determine contrail forecast data; receiving one or more flight parameters associated with at least one aircraft 204 to determine flight data thereof; receiving flight schedule including at least one flight plan of at least one aircraft; analyzing at least one flight plan to determine at least one navigational avoidance between at least two aircraft; determining contrail likelihood associated with at least one aircraft; altering one or more flight parameters to determine improved flight trajectory for at least one flight plan; sending at least one flight plan including improved flight trajectory to at least one aircraft; and validating improved flight trajectory using imagery data, when at least one aircraft flies according to at least one flight plan including improved flight trajectory. A system for determining improved flight trajectory.

The invention relates to the technical field of power transmission lines, in particular to a power transmission line toughness evaluation method and system considering important loads under an ice disaster. The method comprises the following steps: firstly, constructing an icing thickness real-time growth model considering multiple parameters such as water-ice density, an ice precipitation rate, a wind speed and air water content, carrying out regionalization analysis on a power transmission line according to weather parameters such as the wind speed and the precipitation rate, respectively calculating a unit length fault rate of each section, and comprehensively obtaining an overall fault probability of the whole line; then establishing a toughness evaluation system comprising a disturbance performance index, a recovery speed index and an important load maximum loss index, and dividing the affected process into five stages of system normal operation, performance reduction, performance maintenance, preliminary recovery and complete recovery; and finally, carrying out normalization processing on the evaluation indexes, determining the weight by adopting an optimization algorithm, and calculating the toughness value of the system. The fault prediction accuracy and the system recovery efficiency are remarkably improved through regional analysis and multi-dimensional evaluation, and the power supply reliability of important loads is effectively guaranteed.

The invention discloses a rural historical building protection evaluation system based on big data, and relates to the technical field of building protection. Comprising the following steps: a data processing module normalizes environmental data to generate an environmental degradation factor, extracts a crack depth parameter and a facade weathering parameter from structure detection data through a feature extraction algorithm, and generates a building life attenuation curve through correlation analysis; and the decision module establishes a maintenance optimization model according to the building life attenuation curve and historical maintenance data, generates a risk level through threshold comparison, evaluates expected effects of different maintenance modes on degradation delay, and outputs an optimal maintenance suggestion. According to the method, accurate prediction of the rural historical building degradation process and optimal recommendation of the maintenance strategy are realized, and the scientificity and efficiency of protection work are improved.

The invention discloses a troposphere delay model construction method based on a numerical meteorological model and an unmanned aerial vehicle, and the method comprises the steps: obtaining meteorological parameter values in the numerical meteorological model for a target region; a troposphere observation value obtained by the target unmanned aerial vehicle through the airborne GNSS receiver and the airborne multi-source meteorological equipment in the process of moving along the preset path in the target area is obtained; determining first troposphere delay data according to the meteorological parameter values and a corresponding preset oblique troposphere delay model; determining second troposphere delay data by adopting a preset ambiguity fixing algorithm based on the satellite observation value; and according to the first troposphere delay data and the second troposphere delay data, determining troposphere delay data of the target area. According to the method, accurate estimation of troposphere delay in a complex region and a meteorological condition rapid change region is realized, and a reliable troposphere delay model is provided for realizing high-precision monitoring of InSAR (Interferometric Synthetic Aperture Radar) deformation monitoring.

Disclosed in the present invention are an optimization-based regulation and control method and apparatus for distributed energy storage, a device, and a storage medium. The method comprises: on the basis of current weather parameters, constructing an extreme weather model; determining a plurality of faulted and out-of-service elements in which faults will occur in a power distribution network; then on the basis of the faulted and out-of-service elements, system parameters of a distributed energy storage system, and an operation parameter of the power distribution network, constructing a dual-objective optimization model taking load loss minimization and power failure time minimization as objectives; and finally, solving for the dual-objective optimization model to generate an optimal regulation and control scheme applied to the distributed energy storage system. Therefore, when a fault has occurred in the power distribution network, the distributed energy storage system is regulated and controlled by means of the optimal regulation and control scheme, such that power supply of the power distribution network is restored as soon as possible in extreme typhoon weather, and the transient power deficit of the power distribution network is made up promptly. Therefore, the present invention can effectively guarantee the safe operation of power distribution networks under the condition of extreme typhoon weather.

The invention relates to a self-adaptive garden landscape irrigation method and system based on artificial intelligence. The method comprises the following steps: acquiring a current garden plant monitoring live image and historical weather parameters of a position where a garden is located; the historical weather parameters comprise a soil humidity value per hour in a preset period and a rainfall condition in a future preset time period; matching types and growth stages of the garden plants according to the monitored actual images of the garden plants to obtain regular water absorption characteristic data of the garden plants; according to the historical weather parameters, obtaining predicted soil humidity data in a future preset time period; and generating a corresponding irrigation scheme based on the regular water absorption characteristic data of the garden plants and the predicted soil humidity data. By adopting the method, the accuracy of irrigation water volume prediction can be improved by combining the water absorption characteristics of plants and weather rainfall conditions.

The invention provides an atmospheric waveguide interference prediction method, electronic equipment, a storage medium and a program product, relates to the technical field of communication, and is used for improving the accuracy of an atmospheric waveguide interference prediction result. The method comprises the steps of obtaining weather parameters of a to-be-predicted target set area in a preset time period; based on the weather parameters and an atmospheric waveguide prediction model, determining a prediction result of an atmospheric waveguide event of the target set area in a preset time period; and under the condition that the prediction result of the atmospheric waveguide event indicates that the atmospheric waveguide event will occur in the target set area within the preset time period, determining a cell in which the atmospheric waveguide event will occur in the target set area within the preset time period based on the cell in which the atmospheric waveguide event occurs in the target set area within the historical time period.

The invention discloses an intelligent targeted dredging method for an expressway confluence area, which belongs to the technical field of intelligent traffic and comprises the following steps: S1, acquiring traffic flow, vehicle speed, lane occupancy and weather parameters of a main road / ramp through a vehicle-mounted communication unit; s2, constructing a traffic capacity baseline value based on a random forest model; s3, when the traffic capacity is lower than a dynamic threshold value, calling a deep reinforcement learning model through an edge computing node; s4, a hierarchical pushing mechanism is adopted to distribute a control instruction, and variable information board speed limit values are issued to non-network-connected vehicles; and S5, when a residual error between a predicted value and an actual value of the sensor data confidence coefficient continuous time window exceeds a historical mean value standard deviation, switching to a time sequence prediction model to generate a conservative control strategy. According to the method, a multi-index feedback mechanism based on the carbon emission, the average delay time and the number of traffic conflict points is constructed, the weight is dynamically distributed through the entropy weight method, and quantitative evaluation of the dredging effect is achieved.

The invention relates to the technical field of agricultural intelligent monitoring, in particular to a wheat scab risk prediction system and method based on deep learning and random forest. Data acquisition: transmitting historical meteorological data of past 30 days through an interface; preprocessing, wherein the system cleans and standardizes the data to ensure the data quality; model prediction and result output: combining a prediction result of the LSTM model with original historical data, and calculating a future risk probability through a random forest model; the invention discloses a multi-modal data fusion prediction system based on an LSTM neural network, an attention mechanism and a random forest algorithm. The multi-modal data fusion prediction system is used for wheat scab risk assessment and weather parameter prediction. And in combination with an LSTM neural network, an attention mechanism and a random forest algorithm, accurate prediction of the risk of the wheat scab is realized, and targeted prevention and control suggestions are provided.

The invention discloses a meteorological prediction method, and the method comprises the steps: obtaining meteorological collection of meteorological elements in a preset region, and obtaining the meteorological parameters of the meteorological elements in the preset region at the current moment; on the basis of the meteorological parameters of the meteorological elements in the preset area at the current moment, meteorological integration features of the meteorological elements in the preset area are obtained; processing the meteorological integration features to obtain meteorological region features, and performing region attention representation on the meteorological region features to obtain meteorological association features of meteorological elements of the preset region; and splicing the meteorological integration features and the meteorological association features, and predicting the meteorological parameters of the preset area at the target moment. The problem that the accuracy of multi-weather prediction is low in the prior art is solved.

The invention relates to a method for determining a grip coefficient Mu of a tyre on a wet road surface in a vehicle use state, comprising the following steps:-determining (S2) a reference longitudinal stiffness KXRef of the tyre on a reference road surface in a wet state; -acquiring (S1) a grip coefficient MuRef of the tyre on a reference road surface in a wet state; -determining a weather parameter (S3) during use of the tyre on the vehicle; determining (S4) the longitudinal stiffness, KXMes, of the tyre on the road surface if the road surface is in a wet state; -evaluating (S5) the grip coefficient Mu of the tyre on the wet road surface using formula (I), where the coefficient n is a real number between 0.2 and 2.0.

Disclosed is a method for determining an atmospheric radiative forcing difference by optimising or preventing contrail formation caused by an aircraft. The method comprises receiving one or more weather parameters to determine contrail forecast data; receiving one or more flight parameters associated with aircraft to determine flight data; determining tentative atmospheric radiative forcing quantity, along tentative flight trajectory, based on contrail forecast data and flight data; altering one or more flight parameters to determine optimised flight trajectory having optimum atmospheric radiative forcing quantity, wherein optimised flight trajectory is validated using imagery data; and determining an atmospheric radiative forcing difference to evaluate offset value for at least one forcing parameter associated with atmospheric radiative forcing difference. Disclosed also is an apparatus for determining atmospheric radiative forcing caused by aircraft by optimising or preventing contrail formation. Further, disclosed is computer program product to carry out aforementioned method.

Examples are disclosed that related to providing flight-delay estimations of airborne flights in a real-time environment. In one example, an aircraft information message for a current aircraft flight is received. The aircraft information message has a designated format consumable by a machine learning model previously trained to assess delay predictions for aircraft flights. The aircraft information message includes one or more aircraft flight-plan parameters, one or more aircraft surveillance parameters, and one or more weather parameters for the current aircraft flight. The aircraft information message is provided as input to the machine learning model to assess a real-time delay prediction for the current aircraft flight based at least on the one or more flight-plan parameters, the one or more aircraft surveillance parameters, and the one or more weather parameters included in the aircraft information message.

The invention discloses a vehicle window control method and device, a storage medium and a vehicle, and belongs to the technical field of vehicle control. The method comprises the steps of at least obtaining a vehicle window opening and closing degree parameter, an air conditioner switching parameter, a vehicle speed parameter, a vehicle inside and outside temperature difference parameter, a vehicle inside and outside air quality difference parameter and a current weather parameter in real time; calculating a car window score according to each parameter and the weight corresponding to each parameter; and executing a car window control action according to a comparison result of the car window score and a set score threshold value. The problems that in the prior art, in the daily car using process of people, especially in summer, a driver neglects the importance of window opening ventilation to achieve the purpose of rapidly relieving summer heat, and then health risks are caused, and experience is poor can be at least solved; on the basis that the hardware cost of the vehicle is not increased, on one hand, the safety of the user and the health of passengers in the daily vehicle using process can be guaranteed, on the other hand, automatic control over the vehicle window can be achieved, user operation is simplified, and the user experience is improved.

The invention relates to the technical field of weather forecast, and discloses an AI-based deep learning intelligent grid temperature forecast platform, which comprises a data acquisition module used for acquiring numerical weather forecast data of a to-be-predicted area; the data preprocessing module is used for preprocessing the numerical weather forecast data; the deep learning model prediction module is used for inputting the preprocessed numerical weather forecast data into a deep learning model to obtain a temperature prediction map, and the temperature prediction map comprises a temperature prediction value of each position in the whole area; wherein the deep learning model comprises a corresponding relation between the numerical weather forecast data and the temperature prediction value; the numerical weather forecast data form a grid according to positions, each grid point represents a longitude and latitude, and each grid point in the grid comprises a plurality of weather parameters; and the correction module is used for correcting the temperature prediction value based on the correction coefficient and outputting intelligent grid temperature data. The temperature prediction accuracy of the specific position is improved.

The invention relates to a communication method and system of an expressway warning terminal, and belongs to the technical field of emergency dispatch, and the communication method of the expressway warning terminal comprises the steps: obtaining the real-time weather parameters and the real-time communication event type of the expressway warning terminal; the real-time weather parameters serve as input of a network parameter prediction model, real-time network parameters output by the network parameter prediction model are obtained, and the network parameter prediction model is obtained by training an LSTM network based on historical weather parameters and historical network parameters; and determining a communication mode of the highway warning terminal based on the real-time network parameters, the real-time communication event types and a network parameter-energy consumption ratio table, wherein the network parameter-energy consumption ratio table is constructed based on energy consumption ratios corresponding to different types of communication events under different network parameters. According to the invention, the dynamic balance between the energy efficiency and the network stability of the highway warning terminal can be realized.

A heat exchange station heat load correction method considering building heat characteristics mainly comprises the following steps that S1, operation parameters and weather parameters of a heating station and a user position are obtained, and the operation parameters and the weather parameters mainly comprise the heat supply amount of the heat exchange station, the secondary network water supply temperature, the secondary network water return temperature, the heat supply area of the user and the outdoor dry-bulb temperature; s2, the indoor temperature Tin, 0 of a user at a certain moment or in a certain period of time is collected, and a building heat exchange characteristic parameter BC0 is calculated according to the outdoor dry-bulb temperature and the two-network supply and return water temperature at the current moment or in the current period of time; and S3, complementing the indoor temperature of the user at other moments according to the building heat exchange characteristic parameter BC0. And S4, according to the two-network supply and return water temperature and the outdoor dry-bulb temperature at each moment and the building heat exchange characteristic parameter BC0 obtained through calculation in the step S2, actual indoor temperatures at all moments are obtained through calculation. And S5, according to the outdoor dry-bulb temperature, the indoor temperature of the user and the heat supply area of the user, a relation model of the indoor temperature of the user and the heat load of the heat exchange station is constructed. And according to the existing operation parameters, identifying the heat capacity C'of the unit heat supply area in the model and the indoor heat loss mu under the unit temperature difference of the unit heat supply area by adopting a particle swarm algorithm. And S6, the heat load of the heat exchange station is corrected by using the relation model of the indoor temperature of the user and the heat load of the heating station, and the corrected heat load can be used as input data of load prediction.

The application relates to a visual sensor influence analysis method, in particular to a visual sensor influence analysis method for intelligent automobile visual sensors in rain and fog environments, steps of which comprise the following: a test scene is constructed based on a rain and fog weather environment simulation platform of a closed site; [rain and fog weather parameters-target object distance-image information affected by the weather] data is collected; the influence of the rain and fog environment on the intelligent automobile visual sensors is analyzed through three levels of original data, image features and identification functions; the application realizes full-dimension quantitative analysis of the influence of the rain and fog environment on the intelligent automobile visual sensors, so that the performance evaluation of the visual sensors in the rain and fog environment is more comprehensive and accurate; the distance parameter is introduced into the influence evaluation range, so as to better guide the defect analysis and optimization design of the visual sensor algorithm. The application discloses the attenuation effect of the rain and fog environment on the performance of the visual sensor, and provides technical support for the robustness optimization and test verification of the visual sensing system.

The invention discloses a power transmission line fault prediction and strategy generation method and system in severe convective weather, and the method comprises the steps: obtaining the grid weather forecast data of a target region, extracting severe convective weather parameters, calculating the meteorological threat index of a power transmission line based on the data, dividing the power transmission line into a plurality of line units, and carrying out the fault prediction of the power transmission line. Extracting anti-disaster toughness attributes of the line units, fusing meteorological threat indexes of meteorological grid points of the positions where the line units are located, predicting failure rates of the line units through a pre-trained machine learning model, determining risk levels of the line units according to the failure rates, and combining a preset risk level-disposal rule mapping library to determine the risk levels of the line units. And generating and pushing a corresponding coping strategy for each line unit. According to the method, the fault rate of the individual line unit is accurately and quantitatively predicted by using the pre-trained machine learning model, the early warning accuracy, the strategy performability and the operation and maintenance resource use efficiency are greatly improved, and an effective technical support is provided for ensuring safe and stable operation of a power grid in extreme weather.

This application discloses a device operation control method and related apparatus applied to a globe, the globe including a light source component. The method includes: selecting a target area of ​​the globe, the target area indicating a target geographical location; obtaining a specified time point; determining a local time point corresponding to the target geographical location based on the specified time point; obtaining target weather parameters for the target area; determining target illumination parameters corresponding to the target weather parameters and the local time point; determining target operating parameters for the light source component corresponding to the target illumination parameters; and controlling the light source component to illuminate the target area using the target operating parameters. This application embodiment enhances the intelligence of the globe display.

The invention relates to a photovoltaic prediction method and system fusing historical and real-time photovoltaic curves, and the method comprises the steps: collecting related photovoltaic data, including historical photovoltaic data, historical weather data and real-time photovoltaic data; fitting the historical photovoltaic data and the real-time photovoltaic data into a corresponding curve; taking the related photovoltaic data as a training set to train a weather encoder to obtain an optimal weather parameter matrix; extracting a plurality of characteristic curves from the historical photovoltaic curve obtained by fitting; performing weighted calculation on the plurality of characteristic curves based on the trust weight to obtain an initial curve; correcting the initial curve by taking the optimal weather parameter matrix as a scaling factor; and splicing the corrected initial curve and the real-time photovoltaic curve obtained by fitting to obtain a final photovoltaic prediction curve.

The invention discloses an air conditioner datum line selection method, system and device and a medium. The method comprises the steps that weather parameters of the day and historical air conditioner load data are obtained; on the basis of the weather parameters of the day and the historical air conditioner load data, a pre-constructed optimal datum line selection model is combined for calculation, and the optimal datum line of the air conditioner of the day is obtained; wherein the optimal datum line selection model is obtained by correcting and constructing a candidate optimal datum line obtained by performing temperature difference weighting and Frechet distance calculation on historical air conditioner load data by using a dynamic correction function; according to the invention, the temperature difference weighted calculation can quantify the influence of temperature deviation on the load; the Frechet distance calculation can more accurately capture the load curve form similarity; the dynamic correction function is combined with the reference line adjusted by the real-time temperature, so that the accuracy is

1. The name of the product of this design: Four-rotor weather detection drone. 2. Purpose of the product of this design: used to detect meteorological parameters such as temperature, humidity, air pressure, wind speed, wind direction, etc. in the atmosphere. 3. The design key point of this appearance design product lies in the shape. 4. The picture or photo that best illustrates the design points: three-dimensional picture.

The invention discloses a carbon footprint prediction method for preparing electrolytic copper from waste copper based on artificial intelligence. The method comprises the steps that S1, parameters and weather parameters of all stages of preparing the electrolytic copper from the waste copper are obtained; obtaining the carbon footprint of each stage of preparing electrolytic copper from the waste copper; the data acquisition module is used for acquiring data and pre-processing the acquired data; s2, constructing a TCN-Transformer model, and inputting the data obtained after preprocessing in the S1 into the model for training, thereby training to obtain a neural network model containing a corresponding relationship between parameters and carbon footprints; s3, taking partial parameters of each stage in the process of preparing electrolytic copper from waste copper planned in the future as input characteristics, and inputting the input characteristics into the model trained in the step S2 for prediction so as to obtain carbon footprint data of each stage in the future. And finally, performing addition calculation on the carbon footprint data of each stage to obtain the total carbon footprint in the whole life cycle of preparing the electrolytic copper from the waste copper. According to the method, the future short-term carbon emission level is accurately predicted, and the effectiveness of an emission reduction strategy is improved.

The invention relates to the technical field of ship intelligent navigation and energy management, in particular to a two-stage meteorological route optimization method based on a ship seakeeping model. The method comprises the following steps: constructing a three-degree-of-freedom ship seakeeping model, comprehensively considering the motion characteristics of surging, swaying and yawing, carrying out discretization processing on meteorological parameters and ship motion states, generating a corresponding state node set and a meteorological state set, calculating motion primitive sets under different meteorological conditions, and constructing a state grid space, so as to obtain a seakeeping state of the ship. Optimal path planning is carried out through a heuristic search algorithm, and a navigation path with the lowest energy consumption is obtained. Further, segmentation and speed optimization are performed on the path segment, and the optimal propulsive force and speed profile are output in combination with acceleration constraint. According to the method, collaborative optimization of route planning and dynamic performance is realized, and route optimization calculation efficiency and intelligence and practicability of route planning are remarkably improved.

The invention belongs to the technical field of power system automation, and particularly relates to a power system short-term load prediction method and device, equipment and a medium. The method comprises the following steps: acquiring time sequence data for load prediction; wherein the time series data comprises historical loads, weather parameters associated with the historical loads and electricity prices in the demand response use time period; inputting the time sequence data into a pre-trained Transform short-term load prediction model, and outputting a short-term load prediction result by the Transform short-term load prediction model; wherein the Transform short-term load prediction model is obtained by training on the basis of power demand response fluctuation results corresponding to all electricity price differences under different probability conditions. The method is realized on the basis of the Transform model considering the demand response potential, and the accuracy of short-term load prediction of the power system under the demand response scene is improved.

The invention discloses a random near-extreme day weather data simulation method, equipment and a medium, which are used for screening near-extreme day weather data from historical weather data of previous years by taking daily highest dry-bulb temperature exceeding threshold temperature as a standard, and extracting corresponding hour dry-bulb temperature, wet-bulb temperature and solar radiation data; according to the method, four groups of characteristic weather parameters are identified by quantifying probability characteristics of the characteristic weather parameters, and a typical variation coefficient representing a daily variation mode of each weather element is obtained by performing normalization processing and clustering analysis on the daily variation mode of historical near-extreme weather data; and finally, in combination with characteristic meteorological parameters and typical variable coefficients obtained by random sampling, constructing random near-extreme day weather data, and meanwhile, considering uncertainty and a coupling relationship of near-extreme weather parameters, obtaining probability characteristics of a near-extreme load in daily load random simulation, so that the simulation efficiency is improved, and the accuracy of a simulation result is ensured.

The invention provides an in-game real-time weather system physical interaction verification method and terminal, and the method comprises the steps: dynamically generating weather parameters through a meteorological parameter generator, and the weather parameters comprise wind speed, humidity and temperature; the weather parameters are mapped into a calculation rule of a physical engine through a physical engine coupler, and a weather-physical coupling model is constructed to simulate the change of the weather parameters along with time; a linkage event trigger is adopted to trigger a corresponding linkage event according to the meteorological-physical coupling model, and a game engine is called to carry out physical interaction parameter measurement on the linkage event to obtain an actual value; and comparing the actual value with the theoretical value of the theoretical model through a synchronism detector, and verifying the interaction consistency of the weather event and the physical engine. According to the method, automatic verification of interaction between a real-time weather system and a physical engine in a game is realized by dynamically generating weather parameters, constructing a meteorological-physical coupling model, triggering a chain event, measuring an actual value and performing consistency verification on the actual value and a theoretical value.