5results about How to "Meet deployment needs" patented technology

The invention discloses a karst cave stalactite damage degree intelligent assessment method based on image recognition, and relates to the cross technical field of computer vision and geological resource investigation. The method comprises the steps of collecting stalactite images in a karst cave, dividing the images into a labeled data set and a non-labeled data set, preprocessing the images in the labeled data set and the non-labeled data set, constructing a depth separable convolutional neural network model fusing region proposal and alignment technologies, and constructing a deep separable convolutional neural network model; a mixed training strategy combining supervision and semi-supervision is adopted, and training and parameter optimization are carried out on the network model by using the preprocessed image; and inputting a to-be-evaluated stalactite image into the trained network model for feature extraction, candidate region generation and feature alignment, and synchronously outputting a stalactite existence judgment result, a damaged region position and a damage degree classification result through a built-in three-detection-head structure of the model. Therefore, automatic and non-contact detection of stalactite damage assessment is realized.

This invention proposes a target detection method for low-light environments, comprising the following steps: acquiring road traffic image data under nighttime or low-light conditions; annotating the road traffic image data with targets; constructing an enhanced detection network LLE-YOLO based on an improved version of YOLOv11n, where the C3k2 module in the backbone network is replaced with a CSP-PMSFA module, and the Conv module is replaced with a ContextGuidedBlock module; the C3k2 module in the neck network is replaced with a CSP-PMSFA module, and an SPDConv module is introduced; a DetectAux module is introduced in the head network; the low-light traffic perception dataset is divided into a training set, and the enhanced detection network LLE-YOLO is trained; after the enhanced detection network LLE-YOLO is trained, the auxiliary detection head and its related branches are removed, and end-to-end inference is performed on targets in low-light environments, outputting target category and location information. This method achieves lossless downsampling, adaptive enhancement of multi-scale features, effective suppression of background noise, and guidance of hard sample learning, thus solving the problem of target detection in low-light environments.

The embodiment of the invention provides a network planning method, a display method, equipment, a storage medium and a program product, and the method comprises the steps: enabling a network planning server to respond to a network planning request, and determining at least one network type; determining a target network configuration file matched with any network type from the plurality of network configuration files; the target network configuration file comprises network equipment information and a connection rule; generating first network planning information corresponding to the network type according to the network equipment information and the connection rule; under the condition that the at least one network type comprises a plurality of network types, determining a grid-connected part corresponding to the plurality of network types, and determining second network planning information corresponding to the grid-connected part according to a connection rule; and generating target network planning information based on the first network planning information and the second network planning information corresponding to the plurality of network types. According to the technical scheme provided by the embodiment of the invention, the network planning efficiency and accuracy are improved.

The utility model discloses a modularization expandable data collection ware belongs to environmental data collection technical field, solved the traditional data collection ware field maintenance efficiency low, the problem of poor expansion flexibility and poor environmental adaptability, including collection case, pluggable main control board subassembly, pluggable main control board subassembly includes double backboard zoning architecture, electromagnetic shield frame, battery, and main control board and battery are detachably installed in double backboard zoning architecture, and electromagnetic shield frame sets up between double backboard zoning architecture and the shell of collection case, double backboard zoning architecture includes upper backplate and lower backplate, in the utility model, pluggable main control board subassembly includes double backboard zoning architecture, and upper backplate can provide high density's I / O interface, and lower backplate constructs high -speed computing bus, and can easily expand to 128 way and above acquisition channel, and realizes the collaborative work of each component through the standardization bayonet, satisfies the deployment demand of large -scale ocean monitoring array.

The present application belongs to the technical field of quantum computing and artificial intelligence, and particularly relates to a quantum neural network architecture search optimization method and system and application thereof in agricultural image classification. The present application adopts a quantum coarse screening-classical fine tuning method, efficiently excludes architectures with poor performance or not meeting resource constraints in the search space through Grover quantum pre-screening, quickly narrows the search funnel to the potential high performance area, avoids blind search of the classical evolutionary algorithm in a huge space, and greatly shortens the overall search period. The present application can finely optimize multiple targets such as precision, delay and resource consumption, and the obtained model architecture can effectively balance the problem of "global exploration" and "local exploitation" in the search process, so that the model architecture has the characteristics of high precision and low delay; meanwhile, the preset value lightweight constraint ensures the lightweight and high performance of the architecture, has good compatibility, and can completely compatible with the existing agricultural IT infrastructure and edge devices.