7results about How to "Highlight substantive" patented technology

The invention relates to a line patrol unmanned aerial vehicle power line patrol data fusion method based on multi-aerial vehicle cooperative control, provides deep learning, and belongs to the field of image or video recognition or understanding. The method comprises the following steps: a ground cooperative control station synchronously issues an N-view-angle line patrol task of a target line segment of a set power line transmission line to N line patrol unmanned aerial vehicles; and adopting an AI inspection data fusion model of a set power line transmission line to intelligently judge various fault types existing in a target line segment at a preset acquisition moment and positioning information of occurrence positions of the fault types on the basis of multiple analysis data corresponding to the N-view-angle line inspection task. In order to solve the technical problem that the precision, efficiency and comprehensiveness of a power inspection result cannot be improved at the same time in the prior art, a hardware architecture for power line inspection data fusion of the unmanned aerial vehicle for multi-vehicle cooperative control is established, and synchronous intelligent analysis of multiple fault types and occurrence positions thereof is completed by adopting an intelligent mode. Therefore, the technical problem is solved.

This invention relates to a machine vision-based system and method for recognizing and assessing gait abnormalities in children, belonging to the field of action recognition, and more specifically to the field of recognizing human actions or behaviors in image or video data. The system includes: a model building device for building an AI gait abnormality recognition and assessment model corresponding to a side-view imaging device; and an intelligent assessment device for intelligently assessing, based on selectively chosen multi-source basic data, whether the child currently being assessed has gait / crawling abnormalities and the number of months of such abnormalities. This invention addresses the technical problem of simultaneously failing to balance the quality and efficiency of gait / crawling abnormality assessment and the inability to simultaneously provide the number of months of abnormalities. It employs a custom-designed AI gait abnormality recognition and assessment model, based on selectively chosen multi-source basic data, to simultaneously complete both the status assessment and the assessment of the number of months of abnormalities, thereby solving the aforementioned technical problems.

The application discloses a chemical precipitation method using magnesium oxide and a metal hydroxide product, and the chemical precipitation method comprises the following steps: S1, obtaining a pre-reaction solution capable of being used for a precipitation reaction, and obtaining a pulp solution containing ions of a target metal in the pre-reaction solution; S2, mixing magnesium oxide powder and the pulp solution, and obtaining a first slurry through a pulp treatment; and S3, putting the first slurry into a reactor to perform a precipitation reaction, and generating a precipitated slurry containing a metal hydroxide of the target metal. In the magnesium oxide powder, a first part of the magnesium oxide powder has a higher average activity than that of the whole magnesium oxide powder. The method is used to solve the problem of serious activity attenuation of the magnesium oxide slurry, improve the precipitation efficiency of the target metal in the precipitation reaction, increase the content of the target metal in the metal hydroxide product, and reduce the single consumption of the magnesium oxide and the content of magnesium impurities in the product.

The application discloses a PCB defect image detection method based on an improved deep learning algorithm. In view of the deficiencies of the YOLOv3 deep convolutional neural network model, such as imbalance of positive and negative samples, feature expression of small defects of the PCB, and extraction of important feature information of the image, the application proposes an improvement method of replacing the MSELoss function in the original YOLOv3 model with the FocalLoss function, so as to dynamically control the weights of positive and negative samples and difficult and easy classification samples. Meanwhile, the application embeds the SPP module in the branch small target detection head and parallelly integrates the CBAM module into the YOLOv3 model, so that the model can pay more attention to the defect information in the image, thereby improving the overall feature extraction capability of the model. The method can detect different types of PCB defect images, and compared with the original YOLOv3 model, the sensitivity and other indicators are improved, has good performance in the PCB defect detection task, can reduce the missed detection rate and the false detection rate, and better realizes the automatic and intelligent detection of the PCB defects.

The invention provides a low-temperature vacuum extraction system for soil or plant moisture, which is characterized in that a pre-cooling / heating table, a refrigeration table and a plurality of rod core type pipelines are arranged in automatic extraction equipment, placement positions corresponding to sample tubes are arranged in the rod core type pipelines, and collection test tubes are detachably arranged at the lower parts of the rod core type pipelines; a placement through hole is formed in the pre-cooling / heating table, a pre-cooling pipeline and a heating pipe are wound in the pre-cooling / heating table and correspond to the middle of the rod core type pipeline, a groove is formed in the refrigerating table, and a refrigerating pipeline is wound in the refrigerating table and corresponds to the groove; the refrigerant source is communicated with the pre-cooling pipeline and the refrigerating pipeline; a lifting platform is arranged at the upper part of the automatic extraction equipment, a vacuumizing system is arranged in the lifting platform, a floating end cover is arranged at the bottom of the lifting platform corresponding to a top port of the rod core type pipeline, and the floating end cover is communicated with the vacuumizing system; a control unit is arranged on the automatic extraction equipment and is in control connection with the vertical lifting mechanism and the heating pipe. The method has the advantages of high efficiency, low leakage rate, safety and convenience.

This invention provides a controllable image description method, system, device, and medium based on multimodal fusion. The method includes the following steps: based on the original image features X... ν * and object regression box features X ν b The images are fused to generate sample image features X. ν Extract each word from the labeled language text, and label and positionally encode the extracted words to generate sample text description features X. w Generate a trajectory regression box based on the mouse trajectory segment, and generate sample trajectory features X based on the trajectory regression box. r Using a pre-built TO-Transformer feature fusion model, the sample image features X are processed. ν The sample text description feature X w and the sample trajectory feature X r Feature fusion is performed to generate sample fusion features; based on the sample fusion features, a controllable image description model is generated using a pre-built output model, thereby effectively improving the controllability and efficiency of image description.

A high-yield and high-efficiency drip irrigation method for cotton cultivation based on root system configuration time-series optimization is proposed. Target root system configurations are set for three stages: seedling stage, budding to early flowering stage, and boll-forming to boll-opening stage. A coupled model of "drip flow rate - water spatial distribution - root morphology response" is used to inversely design the root zone water field at each stage, thereby dynamically configuring dripper flow rate and irrigation parameters. A dripper flow rate of 0.8–1.1 L / h is used during the seedling stage to induce the formation of slender, spreading roots; a dripper flow rate of 1.5–1.8 L / h is used during the budding to early flowering stage to promote root thickening; and a dripper flow rate of 2.2–2.4 L / h is used during the boll-forming to boll-opening stage to ensure high yield and quality. Field trials show that compared with a fixed low flow rate, yield is increased by more than 120%, and irrigation water productivity is increased by more than 20% compared with a fixed high flow rate, achieving a synergistic effect of high yield, high efficiency, and high quality in cotton cultivation.