5results about How to "Resolve blur" patented technology

The application relates to the technical field of intelligent transportation and carbon governance, in particular to a passenger car carbon emission monitoring and regulation system and method based on multi-source spatio-temporal data. The application solves the problem of scattered and inconsistent data by preprocessing and uniformly mapping multi-source spatio-temporal data. A three-dimensional carbon emission calculation model of vehicle-space unit-time period is constructed to realize accurate calculation of single-trip carbon emission of a single vehicle and positioning of high-carbon emission hotspots. A clustering algorithm is used to complete differentiated division of user groups. A dynamic early warning threshold is generated based on a warning threshold prediction model, regional carbon emission targets and historical data to reduce false positives and false negatives. A warning type-policy tool-adaptation object mapping rule library is established, and a regulation strategy is generated by combining early warning signals and real-time data to realize automatic and accurate generation of regulation strategies from problem identification. The model parameter, early warning threshold and mapping rule are optimized by using policy implementation effect data to form a monitoring, early warning, regulation, optimization closed-loop management and realize accurate regulation.

This invention provides an intelligent inspection method, system, equipment, and medium for railway freight inspection, belonging to the field of railway freight inspection technology. The method includes: acquiring images of the freight cars using a line-scan camera array on both sides and the top of the track; performing spatiotemporal synchronization and adaptive parameter adjustment based on an encoder and a precise clock protocol; performing geometric correction, flat-field correction, motion compensation, and stitching on the images to generate a two-dimensional unfolded image of the freight car; using a deep learning-based freight car segmentation model to segment and number freight car instances, and extracting images of individual freight cars; finally, using a damage detection model integrating severity assessment to identify damage, outputting a structured result including damage category, location, confidence level, and severity score. This invention achieves non-contact, full-coverage, and high-precision intelligent inspection of freight cars, effectively improving inspection efficiency and automation levels.

This invention belongs to the field of laser micromachining and relates to a coaxial monitoring system for ultrafast laser micromachining. The system includes an ultrafast laser, a beam-splitting plate, a laser focusing lens, an intermediate lens group, a filter, a fixed-magnification telecentric lens, and a high dynamic range camera. The ultrafast laser emits ultrafast laser light. The beam-splitting plate and the laser focusing lens are arranged sequentially from front to back along the optical path of the ultrafast laser. The laser focusing lens focuses the ultrafast laser light onto the workpiece, generating visible monitoring light on the workpiece. The visible monitoring light passes through the laser focusing lens and is reflected by the beam-splitting plate to form reflected visible monitoring light. The intermediate lens group, filter, fixed-magnification telecentric lens, and high dynamic range camera are arranged sequentially from front to back along the optical path of the reflected visible monitoring light. This invention provides a coaxial monitoring system for ultrafast laser micromachining that improves imaging clarity and resolution.

The application discloses a sonar image super-resolution method and system based on diffusion generative model, and belongs to the technical field of underwater image processing. The method comprises the following steps: generating an initial diffusion state based on an input image and Gaussian noise conforming to a standard normal distribution; inputting a current diffusion state and a current iteration time step into a diffusion generative model to obtain total noise contained in an image under the current diffusion state; combining the current diffusion state, separating the total noise into structural noise and random noise, and then performing fusion to obtain optimized noise; calculating a preliminary estimated image corresponding to the current iteration time step based on the optimized noise and the current diffusion state; generating sampling noise conforming to the standard normal distribution, and generating a diffusion state of a next iteration time step based on the preliminary estimated image, the optimized noise and the sampling noise; and obtaining a super-resolution image until the inverse diffusion iteration process ends. The application can obtain a high-resolution image with high fidelity, a high signal-to-noise ratio, global consistency and adaptive deployment.

This utility model discloses a plant disease and pest detection device, including a self-propelled vehicle, a rotating seat, an L-shaped support column, a rotating cylinder, a visual inspection instrument, and a leaf-picking mechanism. The self-propelled vehicle has a rotating seat mounted on its top, connected to the L-shaped support column. A rotatable rotating cylinder is located at the end of the support column, and a turntable is located at the bottom of the rotating cylinder. A visual inspection instrument and supplementary lighting are installed below the turntable. The turntable has a leaf-picking mechanism that drives a two-way toothed plate to mesh with an incomplete gear via a threaded rod, thereby actuating two leaf-picking plates to separate branches and leaves, improving image clarity. This device has autonomous mobility and can perform multi-angle, high-efficiency detection in complex planting environments, making it suitable for automated monitoring of agricultural diseases and pests.