18results about How to "Reduce subjective error" patented technology

This invention discloses a real-time evaluation method for photovoltaic panel defects and performance based on multispectral imaging, belonging to the field of computer processing technology. The method includes the following steps: S01, emitting multispectral light including visible light, near-infrared, and thermal infrared bands to the photovoltaic panel, and simultaneously receiving reflection and radiation signals based on the multispectral light to obtain a multispectral image dataset; wherein, the dataset includes visible light images, photoluminescence images in the near-infrared band, and thermal infrared images; S02, performing registration and fusion processing on the multispectral image dataset to generate a registered multispectral fused image. This invention, through multispectral information fusion and intelligent analysis, achieves simultaneous diagnosis of internal and external defects of photovoltaic panels and quantitative evaluation of power generation performance, significantly improving detection efficiency and maintenance accuracy.

The application belongs to the technical field of computer hardware testing, and discloses a multi-sensor integrated detection dock system for notebook computer performance testing, which comprises a multi-sensor module, a data acquisition and interface module, a main control module and a hardware component. The application has the following remarkable advantages: high integration and improved efficiency; data synchronization precision and improved analysis value; high automation degree and reduction of human error; consistent test conditions and strong result comparability.

This invention discloses an AI-based physical education exam scoring method, belonging to the field of exam scoring methods, including the following steps: (1) activating the cameras deployed in the exam room to collect motion images and facial expression images of the examinee during pull-ups; (2) analyzing the collected facial expression images using AI image recognition technology to divide the exam process into three stages; (3) for each stage, analyzing the examinee's motion images using AI technology to determine whether each pull-up motion is a qualified motion; (4) counting the number of qualified motions in each stage and scoring them separately, adding the scores of the three stages to obtain the examinee's final exam score. This invention can divide the exam into three stages based on facial expression recognition, perform differentiated analysis on the motion characteristics of each stage, better fit the examinee's physical fitness change patterns, and make the evaluation results more targeted.

This application discloses a method, apparatus, device, storage medium, and program product for classifying complaint work orders, aiming to solve the problems of low processing efficiency, insufficient classification accuracy, and high labor costs caused by the reliance on manual classification of complaint work orders. The method includes: acquiring user complaint text data; inputting the complaint text data into a trained work order classification model, and outputting the classification result of the complaint text data; wherein the work order classification model includes at least a feature extraction layer, an attention layer, and a classification layer; the feature extraction layer is used to extract text features at least two scales from the complaint text data, and fuse the text features at least two scales to obtain fused features; the attention layer is used to assign weights to features corresponding to different words in the fused features to obtain weighted features that reflect the differences in word importance in the complaint text data; the classification layer is used to output the classification result based on the weighted features.

The application discloses an artificial intelligence-based food colony detection analysis method and system, which comprises the following steps: performing gradient elution-layered filtration pretreatment on a food sample to be detected to generate a de-impurity colony sample; simultaneously performing colony microscopic image acquisition and near-infrared spectrum data acquisition on the de-impurity colony sample to generate initial multi-modal data; performing space-time calibration processing on the initial multi-modal data to generate space-time calibrated multi-modal data; performing multi-dimensional colony feature extraction and screening processing on the space-time calibrated multi-modal data to generate a strong correlation feature subset; performing AI double-branch attention fusion analysis processing on the strong correlation feature subset to generate a colony classification result and a colony concentration estimated value; and performing multi-reference system correction processing on the colony classification result and the colony concentration estimated value to generate a corrected detection result. The timeliness and reliability of the detection result are improved.

The application provides a tunnel lining concrete evaluation method and system, and belongs to the technical field of tunnel lining quality detection. The method comprises the following steps: a data acquisition and matrix construction step: obtaining full polarization data and establishing a complex domain scattering matrix of each sampling point; a spatial phase locking step: constructing a spatial phase locking loop, tracking the spatial phase of the steel bar in real time, determining the locking state according to the phase difference and determining the steel bar high-weight prediction area; a polarization feature calculation step: in the steel bar high-weight prediction area, performing eigenvalue decomposition on the local covariance matrix, calculating the polarization entropy and the average scattering angle; an adaptive projection stripping step: generating an adaptive gain factor and adjusting the projection strength of the projection matrix, performing projection transformation on the original polarization vector to obtain a residual signal; and an evaluation step: reconstructing a defect signal based on the residual signal to evaluate the quality of the tunnel concrete lining. The scheme can improve the accuracy of tunnel concrete lining quality evaluation.

This invention discloses a method for quantitatively assessing the severity of dark circles under the eyes, belonging to the field of objective evaluation technology in cosmetic dermatology. The method divides the eye into the upper and lower eyelids, each assigned a 50% weight. Each area is scored in three dimensions: area (A), color (D), and structure (S). A weighted total score for each eye and a combined total score for both eyes are calculated. Based on the total score, the severity is classified as mild, moderate, severe, and extremely severe. Furthermore, the percentage of each indicator is used to classify the condition as pigmentary, structural, and mixed types. This invention provides an objective, standardized, and repeatable assessment. The combination of multi-dimensional indicators improves the accuracy of the classification. It can be used for preoperative diagnosis and postoperative efficacy comparison, and is compatible with automated assessment in medical aesthetic systems, solving the technical problem of the lack of unified standards in traditional subjective assessments.

The application provides a seed germination detection method and device, electronic equipment and storage medium, and belongs to the technical field of seed germination detection. The method comprises the following steps: performing seed individual image segmentation according to a plurality of global image frame data of a plurality of target seeds to obtain local sampling area data of each target seed; according to the local sampling area data, a plurality of local image frame data corresponding to each target seed is segmented from the plurality of global image frame data; for each target seed, pixel difference germination detection is performed according to the plurality of local image frame data to obtain pixel germination frame state data; for each target seed, semantic germination detection is performed according to the plurality of local image frame data and germination detection prompt text to obtain semantic germination frame state data; and the target germination detection result of the target seed is determined according to the pixel germination frame state data and the semantic germination frame state data. The application can improve the accuracy and efficiency of seed germination detection.

This invention provides a system and method for equipment fault diagnosis based on collaborative reasoning using a large model and knowledge graph. It relates to the field of equipment fault diagnosis technology and includes: a data layer for acquiring real-time operating data of the equipment under test and preprocessing the data; a knowledge layer connected to the data layer for analyzing the preprocessed real-time operating data to determine the presence of fault data; and a model layer connected to the knowledge layer for analyzing the fault data when the analysis results output by the knowledge layer determine the presence of fault data, thereby obtaining the cause of the fault and the corresponding maintenance plan. This invention deeply integrates data processing, fault identification, intelligent analysis, predictive warning, and lifespan assessment, achieving an upgrade in equipment fault diagnosis from passive response to proactive prevention, and from single-data judgment to multi-dimensional collaborative reasoning.

The embodiment of the application provides a kind of testing device of water-using equipment.The testing device of water-using equipment includes: controller, actuator, temperature measuring component and support, actuator and temperature measuring component are all arranged on support;Controller and actuator, temperature measuring component are electrically connected respectively, actuator is used to operate the water outlet button of water-using equipment;The end of temperature measuring component is arranged on the path of water outlet of water-using equipment;Therefore, the detection of water temperature and water stop control are realized automatically, the subjective error caused by human operation is reduced, and the accuracy of test result is improved.

This invention provides a method and system for reconstructing the outer contour function of a projectile's head shape based on machine vision, belonging to the field of machine vision image processing and automatic parameter calculation technology. First, the invention performs semantic segmentation on the frontal view image of the projectile's head shape to obtain a binary mask of the projectile target, and extracts and crops the outer contour of the head shape segment. Second, through coordinate axis self-calibration and attitude alignment, the standard attitude coordinates of the projectile's head shape outer contour are obtained. Then, in the local coordinate system, geometric constraint feature points are extracted to obtain the endpoints, pixel areas, and single-point coordinates of the projectile's head shape outer contour. Third, a two-parameter head shape outer contour shape function satisfying endpoint constraints is constructed, and the shape parameters are solved using area constraints and single-point constraints to obtain the analytical expression of the head shape outer contour. Finally, the outer contour function and its derivative are substituted into the head shape factor integral formula to achieve automatic calculation of the head shape factor.

This invention proposes a method and system for predicting the drag contribution of automotive rear wing designs, belonging to the fields of automotive aerodynamics and artificial intelligence. The method includes: acquiring and preprocessing raw CFD flow field images to generate standardized binary images; extracting flow field depth and tilt angle features from the binary images, using them together as multi-source features characterizing the wake field structure; constructing a deep learning model, using the multi-source features as input, training the deep learning model, and outputting scalar parameters; inputting the flow field image of the automotive rear wing design to be predicted into the trained deep learning model, extracting multi-source features, and combining them with changes in target design parameters to quickly predict the drag contribution using a quadratic function. This invention significantly improves prediction efficiency and has strong generalization ability, providing accurate and efficient decision-making basis for automotive rear wing drag optimization design, and is suitable for rapid iteration and parameter scanning in automotive rear wing design.

An automated scoring system for medial temporal lobe atrophy (MTA) based on YOLO deep learning includes: a coronal slice selection module, an improved YOLOv8 model construction module, an optimal coronal slice automatic selection module, and a medial temporal lobe atrophy detection and automatic scoring module. This invention provides an automated MTA scoring technique that balances accuracy, versatility, and ease of use. By using a lightweight deep learning YOLO object detection framework and combining it with an appropriate loss function, computational overhead is reduced while maintaining algorithm performance. Furthermore, a user-friendly front-end interface improves the efficiency and accuracy of clinical MTA scoring. This invention provides an efficient, accurate, and consistent automated MTA scoring system with significant clinical application value and promising prospects, and is expected to play an important role in the early diagnosis and monitoring of neurodegenerative diseases such as Alzheimer's disease.

The present application belongs to the technical field of transformer cable head construction quality, and particularly relates to a transformer cable head quality detection method based on target detection and a multi-modal large model. A transformer cable head quality detection component is set; a statement related to the installation quality of the transformer cable terminal is parsed and converted into a rule card executable by the multi-modal large model; the collected electric room construction site image is input into the multi-modal large model, the image is preliminarily globally semantically analyzed according to the predefined rule card, a multi-modal structured preliminary report is generated, if the overall confidence of the result of the target detection model is lower than the preset multi-modal semantic threshold, the image area is marked as a semantic uncertain area; based on the semantic uncertain area, the target detection model is used for directional visual detection on the semantic uncertain area, the result of the target detection model is obtained, and the result of the target detection model is fed back to the multi-modal large model for confidence reasoning review to obtain the final comprehensive judgment.

This invention provides a parametric design method and system for jewelry structures that integrates computer vision, relating to the field of jewelry design technology. The method includes acquiring jewelry images and performing semantic segmentation to obtain component contour and texture data; extracting contact points and normal vectors based on boundary positions to determine topological connection data; converting contours and textures into size and style parameters and establishing associated constraints; using size parameters to drive geometric generation; controlling spatial alignment through assembly angles; and iteratively adjusting associated constraints to obtain a three-dimensional digital jewelry. This achieves parametric design of jewelry.

The application relates to the field of performance detection, and discloses a composite intervention guide wire performance detection system and method based on multiple sensors. The method comprises the following steps: performing an axial force test on a first sample to obtain an interlayer axial maximum static friction force; performing a rotary torque test on a second sample to obtain an interlayer torsional static friction force; respectively calculating a first interlayer bonding strength coefficient and a first torsional interlayer bonding strength coefficient, and comparing the two coefficients with preset standard values; if both the coefficients are qualified, outputting a comprehensive value; if any one is unqualified, performing a cyclic fatigue test on a third sample to calculate a second interlayer bonding strength coefficient; if the second interlayer bonding strength coefficient is still unqualified, performing a composite load test on a fourth sample and a fifth sample to respectively obtain calibration interlayer bonding strength coefficients in two sequences, and taking the average value of the two coefficients as a final result. Through a multi-stage evaluation strategy, the application realizes accurate detection of the bonding state of the inner and outer layers of a double-layer guide wire.

The application discloses a kind of multi-dimension benchmark test data analysis method and device based on modular framework, it is related to test data analysis technical field, including: establishing unified connection obtains original data set and standardizes conversion formation structured data;Recognition tool type calls analysis plug-in interface conversion into format uniform comparable data;Construct data matrix, select benchmark system using adaptive algorithm and execute statistical fluctuation detection based on statistical rules, generate analysis result data;According to semantic analysis, optimal chart is matched and visual rendering is generated to generate report.It realizes benchmark test whole process automation closed loop, solves the tool island problem caused by tool data isomerism;Through plug-in architecture, the compatibility and efficiency of heterogeneous data processing are improved;Adaptive calibration and statistical detection are introduced, subjective error of manual selection benchmark is eliminated, the objectivity and scientificity of performance analysis are significantly enhanced, and intelligent generation of performance evaluation report is realized.