8results about How to "Overcome rigidity" patented technology

The embodiment of the invention discloses a camera adjusting method, system and device. A target shooting parameter of a conference room space is acquired; based on the target shooting parameters, determining a target shooting range which needs to be covered by the at least two first cameras in the conference room space; determining a lens adjustment angle corresponding to each first camera according to the target shooting range and the current shooting range of each first camera; and based on the lens adjustment angle, adjusting the at least two first cameras, so that the shooting ranges of the adjusted first cameras jointly cover the target shooting range. Therefore, the target shooting range, which needs to be covered by the first camera in the conference room space, is determined according to the target shooting parameter of the conference room space, so that the angle of the first camera is adjusted according to the target shooting range, and the shooting angle of the camera in the conference room space can be automatically adjusted in accordance with a conference room scene; the definition of picture output is ensured under the condition of covering a conference room scene.

The invention discloses a magnetic-temperature dual-response micro-curl body based on micro-channel shrinkage molding and a preparation method and application thereof, and belongs to the technical field of micro-nano manufacturing and intelligent materials. According to the method, a two-dimensional compression force field is generated by shrinking a micro-channel, a two-dimensional thin film is induced to be curled into a three-dimensional microstructure, and controllable preparation of the micro-curled body is achieved. The obtained micro-curl body integrates magnetic response and temperature-sensitive characteristics, can realize targeted drug delivery through magnetic field navigation and photo-thermal triggering, and has a wide application prospect in the field of biomedicine. The method is simple in process, low in cost and suitable for batch production, and a new thought is provided for intelligent microcarrier development.

This invention discloses an automatic homework grading and feedback method and system based on a multimodal large model, belonging to the fields of artificial intelligence and educational informatization. The method first acquires and corrects handwritten homework images, then performs feature analysis on the image content, and adaptively selects either visual understanding grading or text recognition grading path accordingly. If the output confidence score of the text path is lower than a threshold during processing, the visual path will be automatically triggered to review the same content. After grading, the system uses coordinate mapping and image fusion technology to overlay visual grading marks on the original homework image using the parsed score and location information, generating a feedback image that mimics the teacher's handwriting. This invention overcomes the shortcomings of existing multimodal grading systems, such as modal isolation and rigid collaboration, by introducing intelligent routing decision-making and cross-modal review mechanisms, significantly improving the adaptability, reliability, and user experience of grading.

The invention discloses a collision interference detection method based on a binary tree LOD model, belongs to the field of virtual machine tool processing, and aims to solve the technical problem of insufficient efficiency of a space segmentation model based on an octree in the prior art. The method comprises the following steps: obtaining a binary tree LOD model corresponding to a geometric model participating in collision interference detection, wherein the binary tree LOD model comprises a plurality of levels of bounding boxes; performing intersection detection based on the corresponding binary tree LOD model to obtain bounding box overlapping information of the binary tree LOD model; and according to the bounding box overlapping information, dynamically optimizing the intersection detection range and path of the subsequent hierarchy until an intersection detection completion condition is met, and outputting a collision interference detection result. According to the method, the hierarchical detail model of the binary tree structure is combined with the dynamic optimization strategy in the detection process, so that unnecessary bounding box traversal and calculation can be effectively reduced, and the real-time efficiency of collision interference detection is remarkably improved.

The invention belongs to the technical field of unmanned vehicle environment perception and obstacle avoidance, and discloses an unmanned vehicle transparent obstacle visual touch fusion perception and obstacle avoidance method, and the method comprises the steps: carrying out the transparent object edge detection of an image collected by a vehicle-mounted around-view camera through an improved Transform semantic segmentation network, and outputting an initial position coordinate and a credibility score; a tactile verification and enhancement module is built, flexible multi-mode tactile sensors fusing triboelectric sensing and visual tactile sensing are deployed on the edges of a bumper and a hub of the unmanned vehicle, and close tactile features are generated in the non-contact stage; when the visual credibility is relatively high, the visual data is dominated, the tactile data assists correction, and when the visual credibility is relatively low, a tactile signal dominant mode is triggered; and generating and executing an obstacle avoidance path. According to the invention, the detection precision of the unmanned vehicle on transparent obstacles in extreme scenes such as urban roads, indoor venues, rainy days and nights is obviously improved, the collision risk is reduced, and the unmanned vehicle is adapted to complex moving working condition requirements.

The invention discloses a dust removal system intelligent regulation and control method based on Internet of Things data acquisition. According to the method, coal quality and flue gas conditioning influence factors are calculated by collecting fired coal quality, corrected sulfur trioxide concentration and electric field secondary voltage and current data, a working condition evaluation index is generated through fusion, and then an expected corona current interval is determined. The real-time characteristic current value is extracted, the electric field control margin is calculated, the characteristic current is compared with an expected interval, the voltage adjustment amount is determined under the safety constraint, and the electric field voltage is adjusted and controlled according to the voltage adjustment amount. Finally, the regulation and control effect is verified by re-collecting data, a closed loop is formed, and self-adaptive optimization and safe operation of the dust removal system on the working condition are achieved.

The invention discloses an event relation extraction method and system based on dynamic graph relation reasoning, and belongs to the technical field of natural language processing. The method comprises the following steps: performing event mention recognition and semantic coding on an input text to generate a semantic representation vector; dynamically constructing a multi-relation event graph based on the vector, and learning time sequence, causality and co-reference relation weights among events through a relation perception attention mechanism; carrying out message passing and node updating by adopting a relation perception graph neural network, and explicitly modeling logic constraints among relations through a cross-relation interaction layer; multi-label relation classification is carried out based on enhanced event representation, and the same event pair is supported to have multiple relations; an external knowledge fusion mechanism is introduced to enhance reasoning reasonability, end-to-end optimization is performed by adopting a joint loss function containing a logic constraint item, and rapid migration of a low-resource scene is realized in combination with a meta-learning adaptation mechanism. The problem that a traditional method is difficult to process multi-relation co-occurrence, long-distance dependence and low-resource adaptation is solved, and the accuracy, robustness and generalization ability of event relation extraction are remarkably improved.

This invention relates to the field of industrial equipment safety monitoring technology, and discloses a data-driven intelligent early warning method and system for the degradation risk of organic heat transfer fluids. The method includes: acquiring multimodal monitoring data of the organic heat transfer fluid, performing preprocessing and feature extraction to obtain entropy yield and acoustic emission activity index; inputting the entropy yield, acoustic emission activity index, and medium quality parameters into a mechanism classification neural network model to obtain the determination probability of the degradation mechanism; according to the determination probability, inputting the feature vector into the corresponding target branch neural network to obtain an instantaneous risk index; predicting the time required for the medium quality parameters and entropy yield to reach a safety threshold, taking the minimum value as the predicted failure time, and calculating the degradation resilience value in conjunction with the planned maintenance time; and determining the comprehensive risk level based on the instantaneous risk index and the degradation resilience value. This solution can achieve early detection of degradation trends and distinguish different degradation mechanisms, improving assessment accuracy and making maintenance strategies more targeted.