30 results about "Overcast" patented technology

The invention discloses an environment monitoring big data integration system based on computer services, comprising a temperature information collection module, a weather information collection module, an illuminance collection module, a wind information collection module, an air information collection module, a data receiving module, a data processing module, a data integration module, a master control module and an information sending module; the temperature information collection module is used to collect temperature information, the temperature information includes indoor temperature information and outdoor temperature information, and the weather information collection module is connected to the Internet to obtain weather information from the network , the weather information includes: precipitation, sunny days and cloudy days; the illuminance collection module is used to collect outdoor illuminance, and the wind information collection module is used to collect wind information. The beneficial effects of the present invention are: data can be more comprehensive and faster At the same time, prompt information can be integrated to meet the different needs of users.

The invention discloses a weather phenomenon recognition system for assisting automatic driving of vehicles, which includes an ARM processor, and a vehicle-mounted camera, a remote server, a vehicle-mounted LCD display screen and a communication module respectively connected to the ARM processor; the vehicle-mounted camera shoots the vehicle in real time The weather image of the scene; the ARM processor divides the weather image into two parts: the sky image containing the weather information of the sky area and the ground image including the weather information of the ground area; the remote server will receive the weather image, the sky image and the ground image Import the weather phenomenon recognition model to identify the weather phenomenon type corresponding to the current weather image. The invention extracts the local features of the sky, the local features of the ground and the global features respectively, and imports the classifier after fusion to identify six weather phenomena of sunny, cloudy, snowy, rainy, foggy and sandy days, reducing the dependence on the number of samples , high-accuracy weather phenomenon recognition results can be achieved based on medium-sized weather image datasets.

The invention relates to a household ventilating and rain-shielding device, and belongs to the technical field of intelligent household products. The household ventilating and rain-shielding device comprises a wall fixing bottom plate, a ball screw mechanism, a linear guide rail mechanism, a nut sliding plate connecting seat and a controller, wherein the ball screw mechanism consists of a lead screw, a nut, bearings, bearing seats, a motor and a shaft coupling; the linear guide rail mechanism consists of a guide rail, slide blocks and a sliding plate; one end of the nut sliding plate connecting seat is connected with the nut, and the other end of the nut sliding plate connecting seat is connected with the sliding plate; the controller is electrically connected with the motor. The household ventilating and rain-shielding device is characterized by further comprising a ventilating and rain-shielding plate which is fixed on the sliding plate through a bolt. The household ventilating and rain-shielding device has the advantages that (1) the device is in an open state in the daytime, so that air in southern and northern bedrooms forms convection current, and the aim of natural ventilation is fulfilled; on a cloudy and rainy day, the ventilating and rain-shielding device is closed automatically, so that rain water is prevented from flowing into rooms; (2) bedroom lighting is not influenced; (3) the actions of the device are controlled automatically through the controller.

The invention discloses a method for estimating the proportion of direct radiation, which comprises the following steps: S1: using more than two radiometers to separately collect horizontal total radiation and slope total radiation; S2: preset sunny days according to the horizontal total radiation Index, get the horizontal direct radiation and horizontal diffuse radiation, calculate the theoretical slope radiation according to the slope radiation model; S3: deduce the sunny index, compare the theoretical slope radiation with the measured slope radiation, determine the sunny index range, and finally can Infer the current sunny index by interpolation. The present invention can provide necessary basic radiation information for subsequent data analysis, and can also provide necessary input conditions for calculation of optimal angles of tracking brackets under different weather conditions, such as excluding "pseudo-cloudy" states, and judging "cloudy days are flat", The cost of adding equipment is not high, and it is simple and easy to implement.

An intelligent identification and early warning method for abnormal events in the open scene of the electric power field based on edge computing. This invention compresses the improved SSD target detection model and transplants it to the mobile terminal to give full play to the advantages of edge computing. Through experiments, this invention converts the Android end as an optimal solution; the present invention fuses the Conv4_x feature layer and the Conv5_x feature layer in the VGG16 network, and then directly acts on the feature layer after fusion to the final prediction layer, so as to improve the accuracy of small target detection; at the same time , the present invention summarizes a variety of basic weather conditions: sunny, cloudy, rainy, foggy, etc., and uses image enhancement technology to increase training data in different scenarios, thereby improving the generalization ability of the model.

The invention provides a drone that can be judged remotely based on the two-way influence of wind force and flash light, including a drone body, a support arm is fixedly connected to the surface of the drone body, and the end of the support arm away from the drone body is fixedly installed with or without The human-machine driving motor and the UAV driving motor are fixedly connected with a supporting protective cover on the outer surface, and the surface of the supporting protective cover is fixedly connected with the surface of the supporting arm, which relates to the field of unmanned aerial vehicles. The UAV that can be judged remotely based on the two-way influence of wind and flash lights allows people to remotely and intuitively understand the flight status of the UAV through the re-creation of the outer ring of the UAV's wing, and people can better cope with various weather conditions. At the same time, it enhances the fun of UAV entertainment, thus effectively solving the problem that the remote control of UAVs for general entertainment is easily disturbed by wind direction, wind force and cloudy sky. Misjudgment may cause the crash of the drone.

The invention provides a ship target detection method for joint training of deep learning features and visual features, comprising the following steps: sample data collection, CNN feature extraction, traditional invariant moment feature and LOMO feature extraction, feature dimensionality reduction, and feature fusion network FCNN construction , and finally use the sample data to train the network, and use the test data to test the model. Compared with the prior art, the visual feature extraction process of the present invention comprehensively considers the characteristics of ship shape, color and texture, making the detection process interpretable, and can standardize the CNN back propagation process to learn other features than traditional features. This method is fast, efficient, and highly accurate. It still has good detection results for complex scenes such as clouds, cloudy days, and rain, and has high robustness. Features that are complementary to traditional features can be extracted, and the speed is extremely fast, which can achieve the effect of real-time monitoring.