8results about How to "Implement error correction" patented technology

The invention relates to the field of automatic conveying, in particular to C-shaped lining conveying and detecting equipment which comprises a friction type self-adaptive rotating device, a T-shaped screening and conveying device and a first flexible internal expansion material moving mechanical arm. The friction type self-adaptive turning device drives the C-shaped bushing to rotate by utilizing friction force, and uniformly adjusts the opening direction of the C-shaped bushing to a preset angle; the first flexible internal expansion material moving manipulator is used for transferring the C-shaped bushings between the friction type self-adaptive rotating device and the T-shaped screening and conveying device and grabbing the inner walls of the C-shaped bushings in an internal expansion manner; and the T-shaped screening and conveying device is used for receiving the C-shaped bushings and removing the C-shaped bushings with incorrect opening directions by utilizing a profiling structure of the T-shaped screening and conveying device. The friction type self-adaptive turning device is used for replacing expensive machine vision equipment, the cost is low, interference of greasy dirt and light rays is avoided, and the adaptability is high; and meanwhile, by means of the physical profiling design of the T-shaped screening and conveying device, error correction and rejection of wrong opening directions are achieved, and accurate feeding is ensured.

The application relates to the technical field of artificial intelligence, and discloses a text correction method and device, equipment and a medium, wherein the method comprises the following steps: obtaining initial text input by a user based on a preset ASR speech recognition technology or input method technology, performing pinyin conversion on the initial text to obtain an initial pinyin sequence; and performing pinyin conversion and pinyin-based text correction on the initial text according to each pinyin dictionary tree in a preset pinyin dictionary tree configuration to obtain corrected text, wherein each pinyin dictionary tree in the pinyin dictionary tree configuration comprises a wrong word pinyin dictionary tree, a multiple-word pinyin dictionary tree, a few-word pinyin dictionary tree and a disordered pinyin dictionary tree. Thus, the correction of homophonic words, few words, multiple words and disordered text is realized, and the accuracy and comprehensiveness of the correction are improved.

The application discloses a crop disease identification method based on local selection and feature interaction, which adopts a deep learning image recognition technology to identify and classify crop disease images. The application firstly performs data preprocessing on the disease images, and then extracts leaf disease features through an LSFI-ViT model constructed by the application. The LSFI-ViT takes a ViT as a backbone network to extract global features of the whole image, a local selection module LSM and a local-global feature interaction attention encoder LGFIA-Former are connected in series to form an extended branch, the LSM is responsible for selecting a local area with distinguishing features in the image, and the LGFIA-Former simultaneously processes local and global feature sequence inputs through the LGFIA to obtain interactive features of the local-global. Two groups of features are respectively sent to different fully connected layers to obtain two groups of prediction results, and finally, effective identification of different crop diseases and severity is realized in an integrated voting manner. The application effectively solves the problem of unsatisfactory crop disease and severity identification.

This invention discloses a computer program product and its application for data processing devices in the field of bioinformatics for ctDNA variant detection. The technical problem this invention aims to solve is how to detect ctDNA variants in early-stage cancer or postoperative minimal residual disease (MRD) under conditions of no UMI library construction and moderate sequencing depth. This invention constructs a set of supporting sequences for candidate variant sites, generating a sequence feature tensor and a fragment physical feature vector (including normalized fragment length). The former is input into a first neural network branch to extract sequence representation, and the latter into a second neural network branch to extract physical representation. A fusion module combines the sequence representation and physical representation, and a gating unit calculates the gating weight based on the physical representation and dynamically adjusts the contribution of the sequence representation, outputting the probability of the true ctDNA variant. This invention utilizes physical laws to suppress sequencing noise and can be applied to monitor MRD under conditions without molecular barcodes.

The invention discloses a document information automatic input system and method based on a large language model, and belongs to the technical field of knowledge maps. The system comprises an input module, a preprocessing module, a feature extraction module, a key information identification module, an information verification module and an information input module. The method comprises the following steps: accessing multi-source policy data and carrying out format identification and cleaning; using a lightweight fine-tuning large language model to extract semantic features and construct an index; key information is extracted through mixed retrieval and entity recognition; performing multi-stage verification to ensure information accuracy and logic consistency; and finally, classifying, storing and dynamically optimizing the system. According to the method, policy semantics can be automatically understood, multi-modal content can be analyzed, policy changes can be rapidly adapted, all-directional verification is carried out, the problems that in the prior art, semantic understanding is insufficient, multi-modal processing is weak, dynamic updating lags behind, and a verification mechanism is single are solved, and efficient, accurate and intelligent input of policy information is achieved.

The present application relates to the technical field of pedestrian detection, and especially relates to a pedestrian detection and light intelligent control method and system. The present application optimizes the matching cost mechanism of the Hungarian algorithm, fuses the motion cost and deformation compensation cost of the human body point cloud cluster to complete the initial pedestrian matching, and divides to obtain the human body point cloud cluster subsets of matching success and matching failure. The present application configures independent Kalman filters for each human body point cloud cluster, predicts the next frame pedestrian motion state based on the historical motion parameters, combines the pedestrian motion distance, direction similarity and environmental obstacle information to reconstruct the motion cost, and secondarily uses the Hungarian algorithm to check and correct the matching result. Meanwhile, the present application combines the historical trajectory features of pedestrians and the surrounding environment features to adaptively optimize the process noise covariance of Kalman filtering, constructs the pedestrian motion state based on the position, speed and acceleration, effectively improves the pedestrian detection accuracy, and optimizes the office light intelligent linkage control effect.

The invention discloses a multi-source data-oriented intelligent dynamic character event atlas construction method, and aims to solve the core problems of causality deficiency, structure static stiffness and uninterpretable prediction in the traditional technology. The method comprises the following steps: firstly, unifying heterogeneous data into a'character-behavior-time 'three-dimensional event tensor through multi-source analysis; further, semantic similarity and causal strength calculation are creatively fused, and a dynamic map with an explicit causal relationship is constructed; driving adaptive growth and updating of the atlas through a structural uncertainty index; an individual behavior latent variable is learned by adopting a variational auto-encoder, so that personalized behavior prediction is realized; and finally, generating a prediction result with natural language interpretation in combination with causal path consistency constraint and a large language model. According to the method, the crossing from static association to dynamic causal reasoning is realized, and the accuracy, the self-adaptability and the interpretability of character behavior modeling under the scenes of public security criminal investigation, financial risk control and the like are remarkably improved.

The application provides a borehole camera probe error correction method, device and system, and relates to the technical field of geological exploration.The borehole camera probe error correction method comprises the following steps: a borehole camera gray scale feature model is established; a borehole wall development drawing is acquired; three-dimensional coordinates of a borehole camera probe are determined according to the image gray scale features of the borehole wall development drawing and the borehole camera gray scale feature model; eccentric gray scale error and eccentric perspective error are determined according to the three-dimensional coordinates and the borehole camera gray scale feature model; and error correction is performed on the borehole wall development drawing according to the eccentric gray scale error and the eccentric perspective error.The technical scheme can correct the gray scale error and the perspective error caused by probe eccentricity during the operation of the borehole camera system, thereby improving the measurement accuracy of the borehole camera system.