33results about How to "Add valid information" patented technology

The invention discloses a fast non-destructive detection system and method for matsutake based on convolutional neural network, including a deep learning convolutional neural network model, a control terminal and a consumer terminal; the deep learning convolutional neural network model includes sample collection, data collection, Deep learning convolutional neural network modeling and optimization; sample collection completes sample screening of detection objects to establish a sample set, and divides the sample set into training set, verification set and test set; data collection includes sample chemical content measurement and spectral data collection ; Deep learning convolutional neural network modeling and optimization Utilize deep learning convolutional neural network model and pooling processing to model preprocessed spectral data and corresponding chemical content; deep learning convolutional neural network model for matsutake The detection results of the matsutake are stored in the control terminal; the consumer terminal can obtain the detection data of the matsutake by accessing the control terminal. The invention can effectively reduce the detection cost, and is beneficial to the supervision department to supervise the market.

The invention provides a geographical location information-based method and a geographical location information-based system for outputting an advertisement reminding message, belonging to the technical field of communication. The geographical location information-based method comprises the following steps of: obtaining current geographical location information of a terminal in which a first user and / or a second user are or is when the condition an instant messaging message is sent to the second user by the first user is collected; obtaining advertisement data matched with the geographical location information according to the current geographical location information of the terminal in which the first user and / or second user are or is, and generating an advertisement reminding message according to the matched advertisement data; and pushing the matched advertisement reminding message of the terminal in which the second user is to a client end of the first user for outputting; and / or pushing the matched advertisement reminding message of the terminal in which the first user is to a client end of the second user, and outputting reminding information as the instant messaging message sent to the second user by the first user. By using the invention, the advertisement information and the geographical location of the users are combined, and thus the attraction of the advertisement is enhanced to ensure that better advertisement effect is achieved.

A normalized radar screen monitoring method includes the steps that S1, the model of a to-be-monitored radar and icon information corresponding to a PPI of the model are determined; S2, according to the determination condition, corresponding radar model information, the icon information and radar image and character data information are extracted from a radar data storage unit; S3, by means of anerected external video data acquisition unit, PPI image videos and PPI display data of the to-be-monitored radar are collected in real time; S4, the collected PPI image videos are subjected to noise removal and de-jittering, image information of a non-PPI part is deleted, target monitoring and target tracking are carried out, and to-be-monitored target data is output; OCR is carried out on the collected PPI display data, and recognition data is output; S5, data fusion is carried out on the target data and the recognition data output in S4 and the information extracted in S2, and a virtual radar image and character report is output and displayed. The technical problem about unified platform display of various kinds of radar system information data is solved, and more visual radar information data is provided for a command and control center.

The invention discloses a scanning picture matching method and a scanning picture matching device, which belongs to the field of image processing and are used for solving the problems of poor accuracy and low efficiency of scanning picture matching in the prior art. The scanning picture matching method comprises the following steps of: performing binarization processing on a scanning picture according to color information of the scanning picture; dividing the scanning picture subjected to binarization processing into a plurality of pixel blocks, and performing OR operation on pixel values corresponding to all pixels in each pixel block to obtain a lower sampling picture of the scanning picture, wherein the sampling picture is composed of OR operation results of each pixel block; dividing the obtained lower sampling picture into a plurality of pixel regions, and summing the OR operation results of all pixel points in each pixel region to obtain characteristic information of each pixel region forming the scanning picture; and further calculating similarity of the scanning picture and a picture to be matched, and judging whether the scanning picture is matched with the picture to be matched or not.

The invention discloses a FGR-AM method for remote sensing scene recognition, comprising: performing effective information enhancement processing and invalid information suppression processing on the image features extracted by the third and fifth bottleneck convolution modules; From the image features extracted by the first bottleneck convolution module, the contour information contained in the remote sensing image and the more interesting features in vision are extracted at the same time, and the detailed features contained in the remote sensing image are extracted from the image features extracted by the fifth bottleneck convolution module; Integrate channel attention and spatial attention enhancement features; map multi-dimensional features to orthogonal k-dimensional features to identify and classify remote sensing images. The present invention takes into account the main features and detailed features of the image, extracts and fuses the interested information and detailed information, so that the recognition accuracy of the network is improved, and the network can accurately identify the scene in complex scenes and similar scenes. identification.