48 results about "Weather parameter" patented technology

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.

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 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 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 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 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 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.

The disclosure relates to a method for separating carbon dioxide from the ambient air. The method comprises determining current and / or future weather parameters at the location of the system, conveying an air flow of the ambient air into a first process chamber, wherein the air flow is dried in the first process chamber, adsorbing carbon dioxide from the dried air flow using a physisorbent in a second process chamber, desorbing the carbon dioxide adsorbed in the physisorbent, and storing the desorbed carbon dioxide in a storage unit. It is provided that the process times of the system, the performance parameters of the system, and / or the air flow through the system are adjusted depending on the current or future weather parameters.

This invention discloses a real-time assessment method for forest ecological carbon sinks based on the fusion of multiple meteorological parameters, relating to the field of forest ecological technology. The specific steps of this method are: data acquisition, data preprocessing, data screening, data fusion, and data accounting. This invention uses a time-series analysis unit combined with the time-series pattern of forest carbon sink accumulation to divide carbon sink sensitive periods and non-sensitive periods. Based on the correlation between meteorological data and carbon sink changes, it completes the screening of key data and eliminates redundant data. This scheme takes the time-series change mechanism of forest carbon sinks itself as the core basis, abandons the logic of indiscriminate data processing, accurately locks meteorological data that are highly correlated with carbon sink changes, and aligns with the inherent characteristics of carbon sink formation and fluctuations from the data screening stage, eliminating the interference of redundant data on the assessment process, and providing data source support that is highly matched with carbon sink changes for carbon sink accounting.

The application relates to the technical field of electric digital data processing, in particular to an information system intensive operation and maintenance management method integrating multi-source data. The method comprises the following steps: determining the vehicle quantity difference between adjacent time points in a road image, determining the main vehicle flow direction of a target traffic node by using the vehicle quantity difference; determining the traffic flow characteristic peak value change similarity between adjacent traffic nodes by using the main vehicle flow direction difference between the adjacent traffic nodes; determining the traffic flow pressure coefficient of the target traffic node by using the vehicle quantity difference and the traffic flow characteristic peak value change similarity; determining the driving influence index of the current weather parameter on the road, combining the traffic flow pressure coefficient to determine the bearing pressure coefficient of the target traffic node, and then realizing the hierarchical operation and maintenance management of the traffic management information system. Through the technical scheme of the application, the accuracy of urban road traffic pressure bearing evaluation is improved, and the operation and maintenance resource allocation efficiency is optimized.

The invention discloses a turning meteorological multi-time-scale multi-dimensional feature analysis method, system and device based on historical data and a medium, and belongs to the technical field of electric power system meteorological data analysis, and the method comprises the steps: obtaining historical meteorological data; setting a flag bit for each meteorological parameter according to a preset judgment condition, and identifying whether a turning weather occurs in a time period; calculating a daily time scale feature, a monthly time scale feature and an annual time scale feature based on the flag bit, wherein the monthly time scale feature comprises a time period statistical feature and a day number statistical feature; and outputting the multi-time scale features. According to the invention, analysis is carried out directly from historical meteorological data, dependence on new energy output data is avoided, and original features of turning weather can be reflected objectively; a day-month-year multi-time scale hierarchical feature analysis system is constructed, and accurate support is provided for resource scheduling decisions of power scheduling personnel on different time scales.

The application discloses a kind of coal mine explosion-proof unmanned transport vehicle intelligent sensing system, comprising: multi-modal mixed sensing module, gas weather self-adaptive monitoring module, explosion-proof edge computing module;Multi-modal mixed sensing module is used to collect the multi-modal environment sensing information of target coal mine by multidimensional environment sensing detection unit in real time;Gas weather self-adaptive monitoring module is used to collect the gas-weather parameter information of the area where explosion-proof unmanned transport vehicle is located in target coal mine in real time, wherein, gas parameter information includes at least one of methane concentration, carbon monoxide concentration, dust concentration, and weather parameter information includes at least one of air temperature, air humidity;Explosion-proof edge computing module is used to determine the fusion weight of multi-modal environment sensing information based on gas-weather parameter information, and fusion processing is carried out on multi-modal environment sensing information based on fusion weight, and based on fusion processing result, explosion-proof unmanned transport vehicle is autonomously navigated.

The present application belongs to the technical field of energy-saving control of refrigeration system, and particularly relates to a refrigeration system optimization control method based on virtual cold energy. The method comprises: collecting field historical data of the refrigeration system and preprocessing; establishing component mechanism model based on the composition of the refrigeration system, wherein the component mechanism model comprises evaporator mechanism model, compressor mechanism model, condenser mechanism model and throttling valve model, all component mechanism models are connected in series based on the law of conservation of mass, and the to-be-determined coefficients are calculated through genetic algorithm; combining the refrigeration system model and particle swarm optimization algorithm, calculating the optimal operation control parameters under the given virtual cold energy and weather parameters, so as to minimize the energy consumption of the refrigeration system; and issuing the optimal operation control parameters to the equipment controller; re-collecting data to calculate the system model error, and repeating the above steps when the error is too large. The present application solves the problem that the refrigeration system optimization control method cannot be implemented under the scenario of missing cold energy data.

The invention relates to a geological disaster life cycle tracking and dynamic prevention and control method and system, and relates to the technical field of geological disaster prevention and control. Determining an adaptive marker and an adaptive position point according to the prevention and control demand area; the adaptive markers are arranged based on the adaptive position points, and prevention and control detection parameters and regional weather parameters corresponding to the prevention and control demand region are collected based on the adaptive markers; determining a prevention and control monitoring result in combination with the prevention and control detection parameters and regional weather parameters; determining a prevention and treatment scheme and evacuation notification information in combination with the prevention and treatment monitoring result and the prevention and treatment demand area; determining evacuation notification personnel and prevention and control workers according to the prevention and control demand area; and outputting the prevention and treatment scheme to the prevention and treatment staff for prevention and treatment, and outputting evacuation notification information to the evacuation notification staff for evacuation. The device has the effect of conveniently and timely preventing and treating geological disasters.

The present invention provides a solar power generation prediction device and the like that can accurately predict the amount of electricity generated by solar power generation facilities. [Solution] The solar power generation forecasting device 10 includes a weather parameter acquisition unit 121 that acquires predicted values ​​of weather parameters, a weather parameter variation calculation unit 122 that calculates the variation of predicted values ​​of weather parameters in numerical weather forecasting, a forecast mode selection unit 123 that selects one of a plurality of forecast modes based on the variation of predicted values ​​of weather parameters, a snow depth forecasting unit 124 that predicts the amount of snow in the area where the solar power generation equipment 40 is located based on a predetermined forecast mode, a power generation reduction rate calculation unit 125 that calculates the power generation reduction rate of the solar power generation equipment 40 based on the predicted value of snow depth, and a power generation forecasting unit 126 that predicts the amount of power generated by the solar power generation equipment 40 based on the power generation reduction rate.

The invention relates to a network storage collaborative planning method for a high-proportion new energy power distribution network under a meteorological composite event, and the method comprises the following steps: S1, recognizing the type of the meteorological composite event and a corresponding meteorological parameter based on a meteorological data set, S2, analyzing a coupling relation between the meteorological parameter and an electric quantity parameter through a Spearman correlation coefficient, and obtaining a power distribution network storage collaborative planning result. The coupling relation serves as a training set to train a random forest regression prediction model based on the sparrow algorithm, a meteorological-output prediction model is obtained, and a meteorological data set is input into the meteorological-electric power prediction model to obtain an electric power prediction result; and S3, constructing a network storage collaborative planning model based on a power prediction result, and solving the model to obtain the output in the worst scene of the meteorological composite event and the output in normal operation. Compared with the prior art, the method has the advantages of improving the toughness of the power distribution network in the weather composite event scene and the like.

The invention relates to the technical field of electric digital data processing, in particular to an information system intensive operation and maintenance management method integrating multi-source data, and the method comprises the steps: determining the vehicle number difference between adjacent moments in a road image, and determining the vehicle mainstream driving direction of a target traffic node through the vehicle number difference; determining the traffic flow characteristic peak value change similarity between adjacent traffic nodes by using the vehicle mainstream driving direction difference between the adjacent traffic nodes; determining the traffic flow pressure coefficient of the target traffic node by using the vehicle number difference and the traffic flow characteristic peak value change similarity; and determining the driving influence index of the current weather parameter on the road, and determining the bearing pressure coefficient of the target traffic node in combination with the traffic flow pressure coefficient, thereby realizing hierarchical operation and maintenance management of the traffic management information system. According to the technical scheme, the accuracy of urban road traffic pressure bearing evaluation is improved, and the operation and maintenance resource allocation efficiency is optimized.

The present application relates to a kind of based on wisdom agricultural holographic system of the ecological of Hong Meng Internet of Things, comprising;Based on the parameter of soil acquisition of electrochemical sensor;Based on unmanned aerial vehicle carries multispectral camera and thermal imager and collects the growth state of crop;Weather parameter acquisition module is used to obtain the forecast meteorological parameter information of meteorological bureau and real-time meteorological parameter information collected by sensor;Hong Meng edge computing node is used to the data of the data acquisition unit of multi-source data collection is extracted with the feature and abnormality detection, the feature data and abnormality detection result extracted are sent to Hong Meng Internet of Things platform;Hong Meng Internet of Things platform converts the data sent by each Hong Meng edge computing node into holographic display instruction and then carries out holographic display.Data acquisition is directly called nearby edge node and carries out AI inference, and then analysis result is sent to cloud end and does statistics, cloud end is only responsible for global data summary and strategy update, and real-time performance achieves tens of milliseconds level, and bandwidth occupation directly reduces an order of magnitude.

A system and method for using weather applied metrics for determining the impact of weather conditions on the flight of a football at an outside football venue. Historical and current data for weather parameters, including wind, air pressure, humidity, temperature, and rain, are obtained to calculate the influence of each parameter on the flight of a football. Weather instruments, such as weather sensors, anemometers, LiDAR and SODAR devices, weather consoles, data routing devices, and processors can be included in a system for using weather applied metrics to predict the flight of a football based on current weather conditions.

The invention provides a non-uniform evaporation atmospheric waveguide channel modeling method, and relates to the technical field of atmospheric evaporation waveguide channel modeling, and the method comprises the steps: obtaining spatial refractive index distribution through calculation based on meteorological parameters and related weather parameter continuous distribution before and after generation; calculating by using an equation according to the spatial refractive index distribution to obtain a relation between a ray trajectory and a ray angle; constructing an evaporation waveguide prediction model by using a sequence feature coding module and a constructed channel model decoding module; and training the evaporation waveguide prediction model by using the relation between the ray trajectory and the ray angle to obtain a trained evaporation waveguide prediction model, and completing modeling of the non-uniform evaporation atmospheric waveguide channel. According to the method, the problem that high-precision and high-efficiency channel modeling is difficult to carry out in a horizontal non-uniform evaporation waveguide environment is solved.

The invention relates to the technical field of automatic driving, in particular to an automatic driving interactive test scene generation device, method and equipment and a medium, and the device comprises an expanding and writing device which is used for decomposing and supplementing a target natural language description to generate a plurality of scene descriptions; the weather simulator is used for performing scene simulation according to the weather information to generate weather parameters; the self-vehicle positioner is used for searching a plurality of self-vehicle generation positions on a preset map according to the self-vehicle position; the countermeasure vehicle positioner is used for searching a plurality of countermeasure vehicle static positions on a preset map according to the countermeasure vehicle relative position and the own vehicle position; the behavior generator is used for constructing a behavior topology network according to the interactive behavior library so as to generate a plurality of interactive dynamic behaviors; and the scene generation module is used for constructing a plurality of comprehensive test scenes according to the obtained related data. Therefore, the problem that in the prior art, the interactivity of scene generation and the matching degree with user requirements have obvious defects is solved.

This invention relates to the field of wind power equipment life prediction technology, and discloses a method, device, equipment, and medium for predicting the life of key components of offshore wind power equipment. The invention determines the degree of environmental impact based on weather parameter data and divides the expected lifespan impact scenarios into scenarios with and without expected lifespan impact scenarios. It also expands the data for the expected lifespan impact scenarios, effectively solving the problems of difficult data acquisition and scarce fault samples in offshore wind power equipment monitoring. By constructing an adaptive combined prediction model composed of a Long Short-Term Memory (LSTM) network and a Prophet prediction model, and introducing an adaptive weighting mechanism to dynamically adjust the contributions of the two models, the invention fully leverages the advantages of LSTM in nonlinear time series modeling and Prophet in trend capture, achieving complementary model advantages and significantly improving the prediction accuracy of the remaining lifespan of key components.

The present application relates to the technical field of data analysis, and more particularly to a new energy power generation equipment health management platform based on a large model, comprising a data acquisition and perception layer, an edge computing layer, a cloud processing layer and an application service layer; the present application uses static historical data or a single equipment parameter as a health detection benchmark, which has the shortcoming of being unable to reflect the dynamic changes of the environment and the influence of equipment aging, compared with the prior art; the present application uses multi-dimensional simulation modeling technology, collects equipment parameters, weather parameters and other real-time working condition data to construct a digital twin model, simulates the equipment operating state under different scenes (such as photovoltaic efficiency decay under extreme temperature, fan aerodynamic load under salt spray environment), and generates dynamic health benchmark values including power generation, instantaneous current / voltage, equipment temperature, etc. This method can accurately capture the interaction effect of the environment and the equipment, dynamically adjust the health detection threshold with the working condition, and the abnormal identification accuracy is improved by more than 35% compared with the traditional method.