16results about How to "Improve the evaluation effect" patented technology

The application discloses a kind of campus safety early warning method based on multi-modal fusion and AI vision, it is related to computer vision and intelligent security technology field, comprising the following steps: S1, access control passage perception dataset is constructed;S2, based on access control passage perception dataset identification passage object entering order and passage behavior characteristics;S3, facing relationship expression, in combination with gate occupancy state and authorized behavior record, matching relationship discrimination is carried out;S4, according to gate occupancy state and accompanying behavior characteristics, risk-driven evaluation is carried out;S5, according to risk response condition, linkage control behavior is executed and passage process record and strategy adjustment are completed.The problem that the same direction accompanying borrow line under the passage peak is not associated with the opening and closing rhythm of access control, leading to the problem that the trailing mixed in is difficult to identify in advance is solved.

The invention discloses an AI-based spoken language dialogue quality evaluation method, and relates to the technical field of artificial intelligence and natural language processing, and the method comprises the steps: receiving a spoken language dialogue audio stream, extracting text content information and acoustic rhythm information, carrying out the correlation fusion according to a timestamp, and generating a multi-mode dialogue data sequence; carrying out conjoint analysis on the dialogue logic relation graph and the dynamic memory bank, and calculating a dialogue structure consistency index to obtain a comprehensive quality evaluation score; and based on the comprehensive quality evaluation score and the dynamic memory library, positioning contradictory nodes and contexts in the dialogue logic relation graph, constructing a local consistency reconstruction task, and updating the memory enhancement neural network by using the local consistency reconstruction task. According to the method, the memory enhancement neural network is updated by using the local consistency reconstruction task, adaptive learning and sustainable evolution of the memory enhancement neural network based on actual dialogue contradictions are realized, and the consistency detection accuracy and the dynamic ability of adaptability are improved.

The application relates to the technical field of chemical exploration, in particular to a gradient detection method and system based on geochemical exploration technology. The method comprises the following steps: sampling a to-be-explored area to obtain sampling point information and a number; setting an abnormal flag bit through element content after screening the abnormal sampling points, and obtaining abnormal sampling points; presetting local sampling points for the abnormal sampling points, determining local correlation based on the similarity of element content and the stability of local abnormal flag bits; normalizing associated elements, forming a gradient of the abnormal sampling points and the local sampling points, and determining gradient correlation in combination with the spatial distance of the two; calculating a target area probability value through the local correlation and the gradient correlation, and obtaining a gold mine target area thermal map to provide data support for subsequent detection. The application improves the positioning accuracy of the gold mine target area.

The application discloses a kind of based on multi-task depth knowledge tracking method and device, wherein the method comprises: obtaining the question representation, the knowledge point representation and the answer correctness result of all time before current time;Input the question representation, the knowledge point representation and the answer correctness result to the knowledge tracking model after training for processing;And, in response to the processing of the knowledge tracking model, obtain the knowledge point answer correctness result of next time;Wherein, the knowledge tracking model is based on the specified knowledge tracking network, including the first auxiliary task component for predicting the knowledge point corresponding to the question of current time t and the basic knowledge tracking task component for predicting the knowledge point answer correctness result of next time t+1. The present application scheme simultaneously predicts the knowledge point input by student at current time and predicts the historical overall answer accuracy of student at each time step on the basis of the basic task of predicting the correctness of question to be answered by student, and improves the evaluation effect of doing question.

This invention relates to the fields of data processing and remote sensing information processing technology, specifically to a remote sensing carbon source and sink monitoring and assessment method based on a multimodal hybrid expert model. The method includes: feature extraction from multi-source remote sensing data (hyperspectral, visible, and infrared); multimodal fusion of the extracted features; construction of a large-scale multimodal hybrid expert carbon source and sink model using a hybrid expert partitioning sparsity processing mechanism to detect and segment carbon source and sink changes, obtaining a carbon source and sink distribution map; assessment of annual carbon emissions / absorption rates for different types of land cover using a combination of source and sink distribution and ground measurement data; and calculation of regional carbon emissions and absorption based on land cover type area and annual carbon emissions / absorption rates to obtain carbon flux assessment results. This invention can be applied to carbon metering, regional carbon emission inventory compilation, and carbon neutrality effectiveness assessment, providing technical support for achieving the "dual carbon" goal and improving the effectiveness of remote sensing carbon source and sink monitoring and assessment.

This invention relates to a method for assessing brain gray matter microstructure based on multi-shell diffusion MRI and NODDI-GBSS, comprising: acquiring T1-weighted structural magnetic resonance images and multi-shell diffusion-weighted magnetic resonance images, and preprocessing the multi-shell diffusion-weighted magnetic resonance images; fitting the preprocessed multi-shell diffusion-weighted magnetic resonance images based on the NODDI model to obtain a parameter map including at least the neurite density index (NDI); constructing a gray matter fraction map based on tissue segmentation results, and constructing a study-specific template and a standard spatial registration framework accordingly; using a spatial statistical method based on the gray matter skeleton to skeletonize the neurite density index (NDI) parameter map to obtain a skeletonized NDI map; extracting feature values ​​of occipital lobe-related brain regions; and outputting the current subject's brain gray matter microstructure assessment results and abnormality indications based on the feature values. This technical solution can reflect changes in dendritic / axon-related neurite density and has stronger biological interpretability for the pathological basis of gray matter microstructure.

The application discloses a three-dimensional isosurface extraction and quantification method and system for aviation emissions, comprising obtaining flight trajectory data, constructing a three-dimensional voxel grid, mapping emission data to corresponding spatial voxels, and then constructing a three-dimensional emission field; the three-dimensional emission field is divided into a plurality of voxel blocks, the local state code of the voxel block is obtained, the edge set in the voxel block crossed by the isosurface is determined according to the local state code; the intersection position of the edge and the isosurface is calculated, and a three-dimensional isosurface grid is obtained; the minimum value and the maximum value of the emission field are obtained according to the three-dimensional emission field, and the emission threshold is determined, each emission threshold corresponds to an isosurface of the emission; the geometric parameter characteristics of the isosurface are calculated. The application aims to solve the problems of insufficient geometric accuracy, single structure recognition and lack of depth quantification in the prior art.

The present application relates to the field of psychological psychiatry, and discloses a psychological stress evaluation device for psychological psychiatry, which comprises a seat plate, supporting legs, a transverse handrail, a supporting rod, a back pad, a notch, a connecting block, a linking block, a supporting plate I, a supporting plate II, a hinge, a movable opening, a movable plate, a window, an evaluation plate, a storage battery I, a charging port I, a groove, a test screen, a camera hole, a gap, a rotary motor III, a rotating rod II, a support column, a placing groove II, a heartbeat test ring, a storage battery II, a charging port II, a control panel and a stress prompt lamp. The present application can reduce the vigilance of patients, improve patient comfort and improve evaluation effect. Meanwhile, the device has multiple functions, and is suitable for patients to answer questions and evaluate while sitting or to have a conversation with doctors while sitting, and is also suitable for patients to have hypnosis evaluation while lying down. The present application is suitable for popularization and use, and is convenient for doctors to observe the psychological state of patients and quickly adjust the chatting mode and evaluation mode.

This invention discloses a data compliance dynamic detection method and system based on deep learning, belonging to the field of data security processing technology. It addresses the problem that existing methods, which focus primarily on text data processing when performing compliance detection based on extracted comprehensive semantic words, lack the ability to comprehensively process multimodal data and comprehensively evaluate complex compliance issues, leading to inaccurate compliance assessments. The method includes preprocessing the original data stream, extracting features from the preprocessed data stream based on data stream type, and using a compliance analysis model to identify and analyze multimodal feature sets based on a standard rule base. In this invention, preprocessing the original data stream filters and reduces noise in the multimodal data, ensuring the authenticity and validity of the data. Furthermore, the compliance analysis model's identification and analysis of multimodal feature sets ensures comprehensive processing capabilities for multimodal data and comprehensive evaluation capabilities for complex compliance issues.

This invention discloses a method, device, and medium for predicting the tail risk of static stability margin in power systems, belonging to the field of risk prediction technology. The method includes: using a quantile regression model to perform multi-quantile prediction of the output of new energy sources such as wind power and photovoltaics, obtaining the cumulative distribution function of the output of each new energy node; further constructing a discrete probability density function of the new energy output through discretization and differencing to avoid modeling errors caused by the assumption of continuous distribution; based on this, combining the thermal power output configuration and the static stability margin based on converter dynamic parameters, establishing a mapping relationship between the random injection of new energy and the static stability margin of the system, realizing the quantitative prediction of the stability margin probability distribution and its tail risk. This invention can accurately reflect the distribution characteristics of the static stability margin of the receiving-end power system in a probabilistic sense, especially the stability margin variation law under low-probability, high-risk operating conditions.

The invention provides a model training method, an image quality evaluation method and related equipment, which are used in the technical field of image quality evaluation, and the method comprises the following steps: obtaining a training data set; inputting the training data set into a first pseudo-tag generation system for tag generation to obtain a first pseudo-quality tag set; inputting the training data set into a first quality regression network for quality evaluation to obtain a first quality score set; based on the first quality score set and the first pseudo-quality label set, performing iterative optimization on the first pseudo-label generation system to obtain a second pseudo-label generation system, and generating a second pseudo-quality label set corresponding to the training data set; and performing iterative optimization on the first quality regression network based on the second pseudo quality label set and the first quality score set to obtain a second quality regression network. Through the method, the assessment capability of the second quality regression network for different scene image data can be improved, and the accuracy of quality assessment results for different scene image data can be improved.

This application discloses a device and method for detecting defects on the inner surface of high-strength steel, relating to the field of automatic detection. The device includes: a main control system, a composite electromagnetic detection module, and a lift-off adjustment mechanism; the main control system and the lift-off adjustment mechanism are electrically connected; the lift-off adjustment mechanism is mechanically connected to the composite electromagnetic detection module; in the composite electromagnetic detection module, both the magnetic flux leakage channel structure and the permeability channel structure are electrically connected to the mode switching and control unit, and the magnetic flux leakage channel structure and the permeability channel structure share a magnetizer; the lift-off adjustment mechanism adjusts the lift-off height of the magnetizer; the main control system parses the detection command and determines the detection mode based on the lift-off height; the mode switching and control unit activates the magnetic flux leakage channel structure or the permeability channel structure for detection based on the detection mode to obtain a detection signal. This application can maintain high sensitivity and high reliability in detecting defects on the inner surface of high-strength steel containers over a wider lift-off range, significantly improving the adaptability and comprehensive evaluation capability of coating-free grinding detection.

The invention relates to a semantic segmentation model evaluation method and device, electronic equipment and a medium, and belongs to the technical field of computer vision, the method comprises the steps that a binarized model prediction map and a manual annotation map are acquired, and the model prediction map is obtained through prediction of a semantic segmentation model to be evaluated; extracting a defect area in the manual annotation graph, and performing morphological expansion processing on the edge of the defect area to generate an edge tolerance area; and only indicating the model prediction map as a defect, indicating the manual annotation map as no defect, and taking pixels outside the edge tolerance area as false positive examples to calculate evaluation indexes so as to evaluate the semantic segmentation model. According to the method, a tolerance mechanism is introduced to remove traditional false positive examples falling into an edge tolerance region, and only false positive examples falling out of the edge tolerance region are reserved to calculate evaluation indexes, so that excessive sensitivity of traditional indexes to edge errors is avoided, and real invalid false alarms can be effectively filtered.

This invention discloses a method and system for photovoltaic power generation inversion based on plant growth status. The method includes acquiring remote sensing image data and hyperspectral image data of a target area at a reference time point; obtaining enhanced plant state characteristics based on the hyperspectral image data; using the enhanced plant state characteristics and auxiliary environmental data, acquiring the plant's utilization of light energy through photosynthesis via a plant-photosynthetic response sub-model; mapping the light energy utilization to a total surface irradiance time series using a light inversion sub-model; inputting this time series into a photovoltaic power output model to obtain a photovoltaic system power reconstruction sequence; and obtaining a photovoltaic power reconstruction curve, error confidence interval, and missing data completion report based on the photovoltaic system power reconstruction sequence, thus completing the photovoltaic power generation inversion. This invention enables the reconstruction of historical photovoltaic power, completion of missing data, and prediction of power generation trends, improving the monitoring and evaluation capabilities of photovoltaic systems.

The invention discloses an AI fish tank ecological management method and system, and the method comprises the steps: collecting multi-source ecological data, carrying out the multi-modal feature extraction, and generating an ecological fingerprint vector; constructing a space-time dynamic causal map based on the ecological fingerprint vector in combination with day and night and operation states; a control action generation space is constructed, deformation constraint evolution is combined, and an anti-fact future path is generated; constructing a strategy form space based on a future path, executing expansion, contraction, splitting and merging, and updating the form; generating a control strategy template based on the updated morphological space, and issuing the control strategy template to equipment for linkage execution; and collecting ecological feedback data after execution, and updating a fingerprint spectrum path form to form a self-adaptive cycle. According to the method, ecological fingerprints are generated according to multi-source ecological data of the fish tank, and a space-time dynamic causal map, an anti-factual future path and a strategy form space are constructed, so that predictable generation and self-adaptive evolution management of a fish tank ecological control strategy are realized.

The application relates to the technical field of novel medical instruments crossing digital medicine and embodied robots, in particular to an intelligent body driven Alzheimer robot evaluation system and equipment, which comprises a data set construction and feature extraction module, which is used for generating a standardized first multi-modal fusion feature vector based on voice data, eye movement data and electronic medical record text data of historical subjects; an intelligent body training and verification module, which is used for training an AD evaluation intelligent body based on the first multi-modal fusion feature vector and a clinical cognitive domain atlas, combining a retrieval enhancement generation mechanism, and obtaining a verified AD evaluation intelligent body after verification; and an automatic evaluation output module, which is used for generating an Alzheimer disease prediction evaluation report through the verified AD evaluation intelligent body. Through multi-modal behavior data collected in a noninvasive manner, the application realizes Alzheimer disease automatic evaluation close to a biological evaluation level, and improves the efficiency and accuracy of early evaluation.