73 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.

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 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 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.

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.

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

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 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.

The present invention provides a data-driven, block-scale, building-scale anomaly high energy consumption detection method and system. The method comprises the following steps: obtaining historical energy consumption data for each building in the tested block, determining whether the energy consumption is abnormal and labeling it, thereby obtaining labeled data on the energy consumption of each building; obtaining a dataset of architectural design parameters for each building in the tested block; obtaining a dataset of weather parameters for the city in which the building is located; constructing a detection model and training it based on the datasets of architectural design parameters and weather parameters, and evaluating the training of the detection model based on the labeled data to obtain a trained detection model; obtaining the architectural design parameters of the building to be tested and the weather parameters of the city in which it is located, quantifying them, and then using the detection model to obtain a predicted energy consumption value, and determining whether the energy consumption is abnormal. The present invention can accurately predict whether a building's energy consumption is abnormal, providing a reference for users.

The invention relates to the technical field of power electronics, in particular to a network construction type energy storage converter power oscillation suppression method, which comprises the following steps: acquiring output current, voltage and active / reactive power of each network construction type energy storage converter, acquiring power grid frequency change and line impedance, and acquiring weather parameters of a region where the network construction type energy storage converters are located; determining a disturbance type according to the output current, voltage, active / reactive power, power grid frequency change and line impedance of the grid-forming type energy storage converter and weather parameters of a region where the grid-forming type energy storage converter is located, and suppressing power oscillation according to the disturbance type; according to the method, the multi-type disturbance is accurately identified, the control parameters are dynamically adjusted, and the equipment operation is optimized by fusing environmental factors, so that the operation stability of the network construction type energy storage converter in a complex power grid environment and atrocious weather is effectively improved.

The invention relates to the technical field of automatically querying sample information and weather parameters, in particular to a method for automatically querying sample information and weather parameters in three steps, which is implemented based on an ENA access module, a weather API interface and a data cleaning engine, and comprises the following steps: accessing an ENA database by a system to obtain metadata, generating an intermediate file, and storing the intermediate file into a database; detailed sampling information is further obtained through the intermediate file, a structured table is generated after arrangement, key fields are extracted from the structured table, weather parameters are obtained through an API, and a final integrated file is generated after errors are processed. According to the method, manual item-by-item query is replaced by a three-step process, the time and labor cost is remarkably reduced, incomplete data is automatically removed, the accuracy of an output result is ensured, and finally, sample metadata, sampling information and weather parameters are integrated into a uniform format by a file, so that subsequent analysis is facilitated.

The embodiment of the invention provides a clothes airing machine adjusting method and device, electronic equipment and a storage medium, and relates to the field of Internet of Things. The method comprises the following steps: acquiring a sun position angle and weather parameters of a place where the clothes airing machine is located within a preset time period, and clothes information and current posture parameters of clothes aired by the clothes airing machine; inputting the weather parameter, the sun position angle, the clothes information and the current posture parameter into a pre-trained first model, and obtaining a sunlight radiation area, output by the first model, of the aired clothes when the clothes airing machine is switched to each reference posture parameter; obtaining airing information input by a user in advance, and determining a target posture parameter from the reference posture parameters based on the airing information and the sunlight radiation area corresponding to each reference posture parameter; the airing information is information related to the airing demand of the user; and adjusting the posture of the clothes airing machine based on the target posture parameter. According to the invention, under the condition that the airing efficiency is ensured, the airing requirements of users are met at the same time.

The present disclosure provides a path planning method, which relates to the field of electric power aerial work. The method can generate a first path planning result and a second path planning result based on the environmental parameters of the aerial work, so that workers can perform aerial work more efficiently and safely. The method includes: obtaining the environmental parameters of the aerial work, the environmental parameters including at least three-dimensional terrain parameters, obstacle parameters, and weather parameters; constructing a target environment model corresponding to the aerial work based on the environmental parameters; analyzing the target environment model to obtain a safety risk level corresponding to the target environment model; and determining a first path planning result and a second path planning result corresponding to the target environment model based on the target environment model and the safety risk level, wherein the first path planning result has a higher priority than the second path planning result.

The application provides a kind of intelligent icing weather cloud and mist parameter sensor, belongs to intelligent sensor technical field, the sensor includes fixed seat, the top of the fixed seat is equipped with conductive slip ring, the bolt connection between the conductive slip ring and fixed seat, the top of the conductive slip ring is equipped with pipeline structure, the inside of the pipeline structure is equipped with heating unit and sensing unit, the heating unit and sensing unit are all arranged on the inner wall surface of the pipeline structure.The application adopts the above-mentioned intelligent icing weather cloud and mist parameter sensor, solves the technical problems of insufficient measurement accuracy, reliability and data processing capability of existing sensors under complex weather conditions, can accurately measure weather cloud and mist parameters, and improves the accuracy and reliability of measurement by solving data through a neural network model, meets the demand of weather parameter measurement in the field of aerospace, etc.

A modular weather sensing apparatus is disclosed that includes a cap module, at least one middle module, and a base module, each stacked adjacent to one another to form a sealed and integrated weather sensor assembly. The modules may be configured for detachable interconnection via one or more ring mechanisms that provide both mechanical coupling and electrical connectivity, enabling the assembly to provide power and facilitate data transfer among the modules. This architecture may allow independent sensor modules to be removed, swapped, reordered, or added according to desired configurations, enhancing versatility and ease of maintenance in weather parameter detection.

The application relates to the technical field of automatic driving, in particular to an automatic driving interactive test scene generation device, method, equipment and medium, wherein the device comprises: an extender used for decomposing and supplementing a target natural language description to generate a plurality of scene descriptions; a weather simulator used for simulating a scene according to weather information to generate weather parameters; a self-vehicle locator used for searching a plurality of self-vehicle generated positions on a preset map according to a self-vehicle position; an adversarial vehicle locator used for searching a plurality of adversarial vehicle static positions on the preset map according to an adversarial vehicle relative position and the self-vehicle position; a behavior generator used for constructing a behavior topology network according to an interactive behavior library to generate a plurality of interactive dynamic behaviors; and a scene generation module used for constructing a plurality of comprehensive test scenes according to the related data obtained above. Thus, the problem that the matching degree of the interactivity of scene generation and user demand in the prior art is obviously defective is solved.