11results about How to "Address subjectivity" patented technology

The application provides an animal immune evaluation method based on a Lutzomyia longipalpis infection model, and comprises the following steps: S1, experimental animal preparation and screening; S2, Lutzomyia longipalpis preparation and quality verification; S3, Lutzomyia longipalpis infection of experimental animals; S4, immune related index detection; S5, animal immune function evaluation; and S6, repeatability verification and data summarization. The application realizes systematic and objective evaluation of the effect of immune candidate drugs.

The application discloses a self-adaptive multi-modal data fusion bridge health monitoring method and system, generates a response sample database through establishment of a bridge nonlinear dynamics equation, pre-processes bridge sensor data and trains a deep learning model to extract damage features, and self-adaptively optimizes model parameters; based on model output, real-time and historical data are combined to perform bridge health evaluation, and a monitoring strategy is dynamically adjusted or early warning is triggered; an edge computing device is deployed to perform pre-processing and damage identification, and long-term trend analysis is realized in cooperation with a cloud platform; macro-structure damage and micro-crack data are fused through cross-scale feature extraction; an artificial intelligence algorithm is used to generate a bridge health evaluation result, a bridge maintenance and repair scheme and a decision-level early warning signal. The application combines nonlinear dynamics modeling, self-adaptive deep learning, edge cloud collaborative computing and cross-scale analysis, improves complex damage identification precision and monitoring intelligent level, and is suitable for large-scale traffic network bridge monitoring management.

The application discloses a coating defect diagnosis method and device based on physical mechanism constraint, and relates to the technical field of coating detection. The method comprises the following steps: synchronously acquiring multi-modal detection data in the same detection area of a to-be-detected coating, wherein the multi-modal detection data at least comprises hyperspectral data, Fourier transform infrared spectrum data and electrochemical impedance spectrum data; extracting optical characteristics representing optical response of the coating, chemical characteristics representing chemical composition of the coating and electrochemical characteristics representing electrochemical behavior of the coating interface from the multi-modal detection data; inputting the optical characteristics, the chemical characteristics and the electrochemical characteristics into a pre-trained feature coupling model based on physical mechanism constraint; wherein the coating degradation physical mechanism is embedded into the training process of the feature coupling model as a constraint condition, and the multi-modal features are coupled and modeled; and outputting quantitative diagnosis parameters of the coating defect through the feature coupling model. The application realizes accurate and comprehensive coating diagnosis based on the coupling model.

This invention discloses a big data analysis-based method and system for art teaching management, relating to the field of educational information technology. The method includes: acquiring multi-source teaching big data for a target art teaching unit; constructing a pattern graph extraction unit and a teaching pattern graph based on the multi-source teaching big data; collecting stage-specific student assignment data and stage-specific student process data for the target art teaching unit according to preset stage window constraints; extracting pattern graphs from the stage-specific student assignment data through the pattern graph extraction unit to obtain a student pattern graph set; comparing the student pattern graph set with the teaching pattern graph; making evaluation decisions based on the comparison results; and implementing stage-specific management of the target art teaching unit based on the evaluation decision report. This invention solves the problems in existing teaching management where teacher evaluation is subjective and difficult to quantify, teaching evaluation lacks process analysis, and lacks the ability to deeply analyze the content of assignments themselves.

The invention specifically relates to a metal oxide arrester leakage current on-line monitoring system and method, and relates to the technical field of power equipment monitoring, and the system comprises a multi-physical field sensing array module; a synchronization and data aggregation gateway module; the central analysis server is connected with the fusion module; and a multi-dimensional state evaluation and decision module. According to the invention, through the collaborative design of the electrical, acoustic and thermal three-dimensional sensing units, the problems of early defect misjudgment and interference signal misjudgment caused by the fact that traditional MOA monitoring only depends on power frequency parameters are solved; nanosecond-level partial discharge pulses are captured in the electrical dimension; the acoustic dimension realizes three-dimensional positioning of defects; the thermal dimension is combined with an environment compensation algorithm, so that tiny temperature rise can be identified; and in cooperation with triple cross validation of a space-time correlation algorithm, internal defects and external interference are effectively distinguished.

This invention discloses a method and system for detecting superficial fungi using microscopic images based on the YOLO model. The method involves collecting and annotating microscopic images of clinical superficial fungal disease samples. The annotated images are then classified and input into deep learning-based target detection models (including SSD-VGG16, Faster R-CNN, YOLOv5, YOLOv8, and YOLOv11 models) for training and testing. The system adjusts the Intersection over Union (IOU) threshold and evaluates the effectiveness of the confidence aggregation strategy. After preliminary performance evaluation, the best-performing model, YOLOv11, is optimized. The IOU threshold is optimized, and the system evaluates thresholds within the range of 0.25-0.75. Image-level confidence analysis is performed to evaluate the aggregation strategy. The YOLOv11 model of this invention performs optimally at 400× magnification, with an F1 score of 86.60% for hyphae detection and 95.90% for spore detection.

The invention belongs to the technical field of intelligent analysis and early warning, and discloses a traffic management public opinion intelligent analysis and early warning system based on VIT multi-mode, which comprises a platform adaptive data acquisition unit, a VIT cross-mode public opinion situation awareness module, a dynamic weight public opinion risk assessment module, a knowledge mapping public opinion guide aid decision module and a grading early warning push unit. According to the platform adaptive data acquisition unit, adaptive strategies are designed for various short video platforms, timed incremental pulling and high-heat real-time triggering acquisition are combined, videos, metadata and text information are covered at the same time, and the problems that existing public opinion monitoring is limited in coverage range and low in efficiency are solved; multi-modal analysis and association fusion are achieved through the VIT cross-modal public opinion situation awareness module, a three-level index system and real-time weight adjustment of the dynamic weight public opinion risk assessment module are matched, artificial experience judgment is replaced with quantitative calculation, and the problems that public opinion risk assessment is high in subjectivity and non-uniform in standard are solved.

The application discloses a kind of mineral resources reserves estimation method using fractal algorithm, step (S1): resource distribution map generation based on machine learning;Step (S2): feature matching mine site identification based on fractal geometry;Step (S3): reserve estimation based on regression analysis.The application is a new mineral resources reserves estimation calculation scheme used in the field of intelligent technology of mineral exploration.The scheme uses the "fractal" method in mathematics, combined with AI technology, to provide a complete solution for resource reserves calculation of target mine, especially weathered bauxite, weathered laterite nickel ore and other types of near-surface mine, to achieve the task goal of calculating the mineral resources reserves of target mining area without field exploration data or only using a small amount of artificial exploration data.

This invention relates to the field of environmental protection and resource recycling technology, and discloses a method for reducing the volume of oily emulsified wastewater. The method includes: real-time acquisition of multimodal sensor data such as turbidity, conductivity, oil-water interface height, pH, and temperature; and fusing and calculating a dynamic characteristic state vector composed of coalescence efficiency, phase separation rate, and interface stability. This invention, through the fusion of multimodal sensor data and deep feature extraction, transforms the monitoring of the demulsification process from macroscopic, qualitative subjective observation to microscopic, quantitative dynamic state characterization, achieving precise, objective, and real-time understanding of the process state. This fundamentally solves the problems of subjectivity and lag in manual judgment; it can predict the evolution trend of the system state over a future period, thereby transforming the control strategy from passive responsive regulation to proactive predictive intervention.

The application provides a fabric pilling automatic grading method and device, and relates to the technical field of textile detection. The method comprises the following steps: collecting a thermal infrared polarization image sequence of a fabric sample by using a thermal infrared polarization camera; calculating a polarization degree and a thermal decay time constant based on the sequence data, and extracting a micro-thermal radiation feature; simultaneously collecting a visible light image by using a visible light camera, and extracting an image feature; fusing the micro-thermal radiation feature and the image feature to generate multi-modal feature data; and finally identifying and determining a pilling grade by using a neural network model. By combining thermal physical characteristics and visual features, the problems of strong subjectivity of artificial grading and large limitation of single optical detection are overcome, and objective, accurate and efficient automatic grading is realized.

The application discloses a tower crane bolt axial force intelligent ultrasonic detection method and system based on multi-modal data fusion. First, a multi-level influence factor system is constructed based on fuzzy hierarchy comprehensive evaluation theory, and a high-risk area identification model is established. Based on the model, the regional risk value is calculated, and the monitoring distribution scheme is determined. In the selected area, a heterogeneous network composed of fiber Bragg grating strain sensors and ultrasonic sensors is arranged, and strain and ultrasonic data are synchronously collected. The multi-modal data are input into a deep belief network for feature fusion, and dynamic evaluation of the health state is realized through a fuzzy recognition model. Based on the dynamic grading evaluation result, an improved rain flow counting method and cumulative damage theory are used for fatigue life prediction. Finally, data fusion display and intelligent early warning are realized through a remote monitoring platform. The application realizes intelligent health management of the whole chain from risk identification, real-time monitoring to life prediction.