Techniques for forecasting solar power generation include determining, by a computing device, respective proximity scores for a plurality of measurement devices, wherein the respective proximity scores are based on proximity of the measurement devices to a photovoltaic installation; determining, by the computing device, respective bearing scores for the plurality of measurement devices, wherein the respective bearing scores are based on respective angular offsets between the plurality of measurement devices and an azimuth of the photovoltaic installation; selecting, by the computing device, a first measurement device in the plurality of measurement devices using the respective proximity scores and the respective bearing scores; and predicting, by the computing device, a solar power generation level for the photovoltaic installation based on data obtained from the first measurement device.

A deodorization treatment apparatus according to an embodiment of the invention includes a deodorant tank housing microorganism-retaining carriers, supplied with odorous gas, and discharging an exhaust gas in which an odor has been removed by the microorganisms, a water sprinkler device, a water tank, a water sprinkler pipe, an exhaust pipe, a first blower, an aeration device, and a control device controlling at least one of blowing amount of the first blower, watering amount or watering frequency of the water sprinkler device, amount of air bubbles generated in liquid of the aeration device, water supply to the water tank, drainage from the water tank, or temperature of the deodorant tank according to measurement results.

The present application provides a method and system for dynamically predicting a deoxynivalenol content of wheat at harvest, including: on the basis of historical data, screening out by particle swarm optimization algorithm combined factors suitable for establishing a prediction model, and establishing the prediction model by using the combined factors; on the basis of data of a current year, predicting a second flowering date and a second harvest date of wheat in the current year by an agricultural model; then obtaining a weather forecast on the basis of the second flowering date and the second harvest date, and combining the weather forecast and geographic data into correlated factors; and finally predicting the deoxynivalenol content of wheat at harvest by means of the prediction model and the correlated factors. Compared with the prior art, statistical items in the prediction model are more comprehensive, and growth period data of the current year can be dynamically predicted on the basis of growth period indexes model, thus continuously adjusting and establishing the prediction model. In addition, an overhead time for screening multi-dimensional large-batch data by the particle swarm optimization algorithm has more advantages, and the prediction model established by a multiple linear regression algorithm has higher precision.

The present application provides a method and system for dynamically predicting a deoxynivalenol content of wheat at harvest, including: on the basis of historical data, screening out by particle swarm optimization algorithm combined factors suitable for establishing a prediction model, and establishing the prediction model by using the combined factors; on the basis of data of a current year, predicting a second flowering date and a second harvest date of wheat in the current year by an agricultural model; then obtaining a weather forecast on the basis of the second flowering date and the second harvest date, and combining the weather forecast and geographic data into correlated factors; and finally predicting the deoxynivalenol content of wheat at harvest by means of the prediction model and the correlated factors. Compared with the prior art, statistical items in the prediction model are more comprehensive, and growth period data of the current year can be dynamically predicted on the basis of growth period indexes model, thus continuously adjusting and establishing the prediction model. In addition, an overhead time for screening multi-dimensional large-batch data by the particle swarm optimization algorithm has more advantages, and the prediction model established by a multiple linear regression algorithm has higher precision.

A road surface state determination device includes a tire-side device and a vehicle-body-side system. The tire-side device is attached to a back surface of a tread of each of a plurality of tires included in a vehicle. The vehicle-body-side system is included in a body of the vehicle. The tire-side device outputs a detection signal corresponding to a magnitude of vibration applied to the tire. The tire-side device generates road surface data indicative of a road surface state appearing in a waveform of the detection signal. The tire-side device transmits the road surface data. The vehicle-body-side system performs bidirectional communication with the tire-side device and receives the road surface data. The vehicle-body-side system determines the road surface state of a road surface on which the vehicle is traveling based on the road surface data.

An adaptive method and device for predicting a weather-related characteristic of a surface of a segment of a road network. The method includes obtaining a location and of measuring a weather-related characteristic of the surface of the roadway of a road segment on which a measuring vehicle is traveling; predicting a weather-related surface characteristic of the road segment using a weather-observation history and a first prediction model associated with the road segment; associating a second prediction model with the road segment when a difference between the measured characteristic and the predicted characteristic is greater than a threshold; and transmitting to a vehicle a prediction made by applying the associated model to the weather-observation history.

Improved mechanisms for collecting information from a diverse suite of sensors and systems, calculating the current precipitation, atmospheric water vapor, atmospheric liquid water content, or precipitable water and other atmospheric-based phenomena, for example presence and intensity of fog, based upon these sensor readings, predicting future precipitation and atmospheric-based phenomena, and estimating effects of the atmospheric-based phenomena on visibility, for example by calculating runway visible range (RVR) estimates and forecasts based on the atmospheric-based phenomena.

The present disclosure generally relates to managing weather information. In some embodiments, methods and user interfaces for managing weather information are described. In some embodiments, methods and user interfaces for displaying daily weather information are described.

The present disclosure generally relates to managing weather information. In some embodiments, methods and user interfaces for managing weather information are described. In some embodiments, methods and user interfaces for displaying daily weather information are described.

In a road surface condition determining device, when determining a road surface condition, a vibration detection unit, a waveform processing unit and a data transmission unit for implementing a sensing function and a data transmission function are not set continuously to an active state for all tire side device, but at least only one tire side device is set to an active state. Remaining one or more is set to a sleep state. A reduction in power consumption of the tire side devices set to the sleep state can thus be achieved. Further, with regard to the at least one tire side device, since the sensing function and the data transmission function remain in the active state, the road surface condition can be reliably determined based on the road surface data of the tire side device.