14 results about "Wildlife" patented technology

This invention relates to the field of image processing and data transmission technology, specifically disclosing a method and system for adaptive compression and breakpoint resume scheduling of wildlife images. This invention acquires multi-dimensional data of wildlife images in real time, constructs a quantitative evaluation system, and dynamically optimizes data transmission and processing. First, raw data is collected from the wildlife image acquisition terminal, covering core information such as terminal identification, network status, and image content. Then, value assessment coefficients and breakpoint resume scheduling coefficients are calculated sequentially, and an image compression strategy is obtained accordingly. Adaptive compression and breakpoint resume transmission are then executed. Finally, verification, retransmission, and image enhancement optimization are performed on the server side, achieving closed-loop optimization across the entire link from terminal to server. This ensures high-definition transmission and reliable delivery of rare species images even in weak network environments, improving the efficiency and reliability of the monitoring system.

This disclosure relates to the fields of computer vision and wildlife monitoring technology, and specifically to a method and system for identifying Tibetan macaques in the wild based on deep learning. First, a face detection model is used to detect and crop faces from raw wild images. The detection model is initialized with pre-trained weights and fine-tuned by freezing the backbone network. Then, an individual identification model is constructed to output classification results. During training, a joint loss function is used, combining Focal Loss to suppress easily classified samples and guide attention to difficult-to-classify samples. Supervised contrastive loss is used to enhance the discriminative power of the feature space. Simultaneously, an adaptive hybrid enhancement strategy and a hierarchical Dropout regularization mechanism are introduced to effectively address the problems of small sample sizes, class imbalance, and overfitting in complex wild environments. This disclosure enables high-precision identification of Tibetan macaques, providing an efficient technical means for long-term ecological monitoring of wild primates.

This invention provides a wildlife target detection method based on edge computing deployment, belonging to the field of wildlife target detection technology. This invention achieves edge-end model compression by performing hybrid precision quantization and knowledge distillation on wildlife images. The compressed model is deployed to a multimodal temporal attention fusion detection model to achieve visible light and thermal infrared dual-stream fusion detection. Adaptive inference is achieved by dynamically adjusting inference precision and memory allocation based on resource adaptation evaluation values. Early termination mechanisms and sparse convolutions reduce computational power consumption. A hierarchical wake-up architecture compresses cold start latency. Furthermore, a cross-domain feature alignment algorithm based on Riemannian manifold geodesic interpolation, combined with elastic weight solidification and a test-time adaptive mechanism, continuously suppresses cross-seasonal visual domain drift at the edge. This solves the technical problem of continuous performance degradation of artificial intelligence detection models caused by cross-seasonal visual domain drift in wildlife monitoring scenarios using edge computing devices.

A method, apparatus, program product, and electronic device for wildlife monitoring and processing are disclosed, relating to the field of image processing technology. The method includes: acquiring monitoring image frames; determining thermal change intensity based on the monitoring image frames; obtaining motion intensity based on pixel differences between the monitoring image frames; obtaining a fused motion intensity based on the thermal change intensity and the motion intensity; determining the target continuity of the target wildlife in the monitoring image frames based on the target wildlife detection results; triggering image acquisition based on the target continuity and the fused motion intensity to obtain a captured image of the target wildlife; and filtering the captured images of the target wildlife to obtain a target image. This method reduces false triggering caused by factors such as wind blowing vegetation, changes in light, or rain and snow, reduces the generation of redundant images at the source, and improves the efficiency of manual screening.

A transport sled that is configured to receive and retain items such as but not limited to hunting gear, camping equipment and wild game within an interior cavity thereof. The transport sled includes a body having a bottom portion and a top portion wherein the top portion is hingedly secured to the bottom portion. The bottom portion is concave in form having an outer surface configured to slidably traverse across a snow covered surface. The top portion is hexagonal dome shaped forming an interior cavity. The top portion includes storage compartments operable to receive and retain items proximate thereto. The body includes stationary members movably coupled to the rear end of the body. The stationary members move between a first position and a second position wherein in the second position the teeth members of the stationary members engage the snow covered surface to inhibit movement of the transport sled.

The calculation accuracy is improved by considering the risk of damage caused by wild animals and birds from multiple factors. [Solution] The wildlife risk management system 1000 comprises a server unit which includes a server-side calculation unit 10 for calculating the encounter risk for each specific area based on sighting information or capture information acquired by the server-side communication unit 20, and a server-side display unit 30 which can overlay the encounter risk for each area calculated by the server-side calculation unit 10 onto a map including each area, for each corresponding area. Sighting information includes at least one of the sighting location information of the place where the wildlife was sighted, the date of the sighting, and the type of wildlife sighted. Capture information includes at least one of the capture location information of the place where the wildlife was captured, the date of capture, and the type of wildlife captured. The server-side calculation unit 10 is configured to calculate the encounter risk for each type of wildlife for each specific area based on the sighting location information of the sighting information and the capture location information of the capture information.

This invention discloses a new generation of artificial intelligence bioinformatics method for individual geographic tracing, belonging to the field of wildlife DNA geographic tracing technology, including the following steps: S1, obtaining DNA from biological samples; S2, constructing a novel artificial intelligence DNA individual tracing model based on a convolutional neural network module, combined with a CBAM spatial attention module and a multilayer perceptron; S3, training the artificial intelligence DNA individual tracing model using the sample's genotype data and background sampling geographic data as inputs, and optimizing the model parameters using cross-validation; S4, using the artificial intelligence DNA individual tracing model to predict the geographic coordinates of individual DNA from unknown samples. This invention has the ability to integrate data from different species and batches, greatly simplifying the dataset integration process, significantly reducing complexity, and improving repeatability, enabling the prediction of accurate individual geographic locations.

This invention provides a method for wildlife behavior recognition based on multi-task learning, relating to the fields of video intelligent analysis and animal behavior recognition technology. The method includes: acquiring continuous frame videos containing target wildlife individuals, and segmenting the continuous frame videos according to a preset target window to obtain multiple target video segments; preprocessing each target video segment to obtain an input tensor for each target video segment; inputting the input tensor of each target video segment into a target model to identify and obtain the behavior recognition result of the target wildlife individual in each target video segment; and merging the behavior recognition results of the target wildlife individuals in multiple target video segments to obtain the target behavior result of the target wildlife individual; wherein the target model is a multi-task spatiotemporal action recognition model obtained through progressive training.

This utility model discloses a wildlife burrowing structure for landscape gardens, relating to the field of landscape design technology. It includes a biomimetic tree trunk with first and second biomimetic branches. A first tree hole is formed at the bottom of the trunk, and a second tree hole is formed in the middle. A third and fourth tree hole are formed on the first and second biomimetic branches, respectively. Hanging holes are also formed on both branches. This utility model, by creating tree holes and bird nests of different heights on the biomimetic trunk and branches, simulates traditional tree holes and bird burrows. It can serve as nests for both reptiles and wild birds, facilitating better coexistence of different species of wildlife in the landscape garden. Compared to existing common animal burrowing structures, this utility model has a wider range of applications and, due to its three-dimensional design, can improve the space utilization rate of the landscape garden.

This invention relates to the field of ecological risk assessment technology, and in particular to a method and system for assessing human-wildlife conflict risk in nature reserves. It constructs a list of conflicting species and a standardized distribution dataset by integrating multi-source data. Based on a maximum entropy model, a stacked species distribution model is built to calculate species hazard levels. For three types of conflict—crop damage, livestock predation, and personal injury—arable land, grassland, and population density are matched as exposure indicators, respectively. Based on a "hazard level × exposure level" framework, categorized risk sub-models are constructed, and a comprehensive risk raster map is generated by overlaying these sub-models. Finally, the overall risk assessment value is calculated using nature reserves as units. This invention addresses the problems of existing human-wildlife conflict risk assessment technologies, which fail to effectively integrate multi-species contributions, differentiate conflict types, and adapt to large-scale protected area systems.

This utility model relates to the field of lighting equipment technology, and in particular to an explosion-proof lamp for preventing interference with marine and wildlife habitats. It includes a combined power supply assembly and a light source assembly. The power supply assembly includes a connected power cavity, a power cavity cover, and an LED driver power module disposed within the power cavity. The light source assembly includes a light source cavity and a light source module, an optical panel, and a panel frame disposed within the light source cavity. The light source module includes a printed circuit board (PCB) and low-blue-light LEDs disposed on the PCB. The low-blue-light LEDs are arranged in an array of single LEDs on the PCB. The LED driver power module is connected to the PCB. This utility model can reduce the impact on marine and wildlife habitats while preventing light pollution and interference with astronomical observations.

This invention discloses a method for designing natural habitats based on satellite imagery driven by wild bird GPS trajectories. The method includes: acquiring and preprocessing satellite imagery and wild bird GPS trajectory data; using a deep learning model, inputting each trajectory point from the wild bird GPS trajectory data, and labeling each trajectory point with habitat functions to form wild bird GPS behavior data; extracting image features from the satellite imagery and wild bird GPS behavior features from the wild bird GPS behavior data and satellite imagery to obtain multimodal feature fusion features; constructing a conditional generation model, and introducing ecological guidance condition vectors based on the multimodal feature fusion features to generate design images that conform to bird habitat preferences; and generating habitat design maps according to preset design objectives. This invention deeply integrates bird behavioral ecology with satellite remote sensing and deep learning models, enabling better mimicking of the environmental characteristics of wild animal habitats and eliminating traces of human design.

This invention relates to a method, system, device, and medium for predicting the trajectory of forestry wildlife in sparse scenes. The method includes: dividing the study area into hexagonal grids and extracting multi-dimensional environmental features to construct an environmental state space; pairing start and end points of historical occurrence point data and mapping them to hexagonal nodes to generate a sparse movement task set; calling a random forest model to learn the relationship between environmental features and occurrence points to generate an environmental resource suitability distribution map; learning individual path preferences based on the sparse movement task set and resource suitability distribution map and iteratively training through reinforcement learning to generate an individual experience memory bank; obtaining the current start and end point positions of the target animal and mapping them to the environmental state space, and performing weighted path search; using attention weight allocation, selecting the node sequence with the highest score after weighted integration as the main prediction path, and using the individual paths generated by virtual individuals with high weight rankings as alternative prediction paths.

A Wildlife Monitoring and Tracking Device