38results about How to "Reduce subjective error" patented technology

The invention discloses an industrial intelligent safety risk early warning and management and control platform, and relates to the technical field of industrial safety monitoring and management and control. The industrial intelligent security risk early warning and management and control platform comprises a full-scene perception layer, a data management layer, an intelligent risk identification layer, a hierarchical management and control layer and a global operation and maintenance layer, and each layer and an edge computing node construct a distributed data interaction network through an industrial Ethernet; the scene sensing layer is used for realizing all-region and all-time security data acquisition of industrial production and comprises a multi-mode sensing terminal array, an equipment protocol adaptation module, a personnel state monitoring unit and an acquisition scheduling module; according to the industrial intelligent safety risk early warning and management and control platform, through a multi-mode terminal array and a dynamic acquisition scheduling mechanism of a full-scene sensing layer, full-area and full-time-period automatic monitoring of industrial production is realized, dependence on manual inspection is thoroughly eliminated, the problems of low manual inspection efficiency and missing inspection of hidden risk points are effectively solved, and the industrial production safety risk early warning and management and control platform is suitable for popularization and application. And the monitoring coverage rate is improved to 100%.

The invention discloses a lung disease diagnosis and grading method based on quantitative CT image omics and deep learning. According to the method, through multi-dimensional information integration and model optimization, two core tasks of disease judgment and disease condition grading can be completed at the same time, and comprehensive support is provided for clinical diagnosis and treatment. The model sets feature priorities by referring to clinical diagnosis logic in the training process, the judgment result and the grading standard are completely matched with clinical general specifications, and the model can be directly applied to diagnosis and treatment decision-making without secondary conversion of doctors. After multi-center clinical verification and iterative optimization, the stability and the accuracy of the model are fully guaranteed, subjective errors caused by manual film reading can be effectively reduced, the diagnosis efficiency can be improved, the chronic obstructive pulmonary disease screening capability of primary medical institutions can be remarkably improved, early diagnosis and early treatment of more patients can be helped, and the clinical application prospect is wide. Therefore, the morbidity and disability rate of diseases are reduced, a clinical management path is optimized, and the overall disease burden is relieved.

The embodiment of the invention provides an image retrieval method, device and equipment for target image information. The method comprises the following steps: acquiring to-be-retrieved target image information; analyzing and extracting the target image information to obtain target image feature information; matching the target image feature information in a preset image data structured index database, and retrieving to obtain a retrieval result containing matched target image data and structured description information associated with the target image data; outputting a retrieval result; wherein the image data structured index database is obtained through the following processes: obtaining multi-modal original data containing images, videos and text descriptions of the same target object; performing content analysis and feature extraction on the multi-modal original data to obtain corresponding structured description information; and storing the structured description information as an index structure supporting feature-based matching retrieval according to a preset storage format. According to the embodiment of the invention, the deep semantic content of the image can be efficiently understood and accurately matched.

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 discloses a metamorphic rock P-T-t trajectory reconstruction method and system based on multi-source data fusion, relates to the field of metamorphic rock P-T-t trajectory reconstruction, and comprises the following steps: acquiring electronic probe micro-area mapping data and internal standard sample data of a metamorphic rock sample, and performing multi-stage data correction; performing mineral phase identification and chemical component quantitative interpretation, generating a mineral classification mapping, an element concentration distribution diagram and a whole rock major component data set; performing thermodynamic phase equilibrium simulation, generating a P-T apparent profile diagram, and extracting a P-T trajectory; performing micro-area in-situ chronology analysis on selected minerals in the mineral classification mapping to obtain chronology data; coupling the P-T trajectory and the chronology data in time and space, constructing a P-T-t three-dimensional trajectory model of the metamorphic rock, and outputting a visualized reconstruction report. The application realizes full-process automation and standardization of the metamorphic rock P-T-t trajectory from data acquisition and processing to model reconstruction, and improves the precision, efficiency and reliability of trajectory analysis.

The application provides a first-aid hemostasis robot and a control method thereof. The first-aid hemostasis robot comprises a robot main body capable of walking on the ground, a mechanical arm with multiple degrees of freedom arranged on the robot main body, a pressing head and a blocking piece arranged on the mechanical arm, the pressing head is used for pressing and hemostasis of a blood vessel of a bleeding port, the blocking piece is carried by a flexible continuum robot, a front end of the continuum robot is provided with a camera used for collecting information of a bleeding site, the blocking piece can be inserted into the inside of the bleeding port through the continuum robot and expanded after blocking the bleeding port to seal a large blood vessel of the bleeding port. The mechanical arm carries the pressing head and the blocking piece to perform hemostasis on different conditions of large bleeding respectively, if the bleeding is of a split type, the pressing head is used for pressing and hemostasis at a proximal end of an artery bleeding or a distal end of a vein bleeding, if the bleeding is of a broken type, the blocking piece is inserted into a wound through the continuum robot to seal a large blood vessel of the bleeding.

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 invention provides a multi-agent reward function automatic generation and optimization method and system, and relates to the technical field of artificial intelligence and reinforcement learning. According to the method, state description information of an environment is extracted through an environment context construction module, and then a plurality of reward function candidates are evaluated at the same time through a parallelized reward function evaluation module by utilizing a reward function code generated by a large language model; afterwards, statistical information in the training process is converted into structured natural language feedback through a reflection report generation module, so that the large language model can understand the training dynamics and improve the reward function in a targeted manner, and then context-based directional rewriting of the reward function is realized; and finally, through an iterative optimization process, generating a reward function which not only conforms to a task target but also has good training characteristics. According to the method, the code generation capability of the large language model is combined with the reinforcement learning training process, so that automatic generation and iterative optimization of the reward function are realized.

The application relates to a bridge modal parameter automatic identification method and system considering multi-channel information, which comprises the following steps: S1, generating a stability diagram by directly analyzing multi-channel monitoring signals by using COV-SSI, automatically extracting stable axes by combining a DBSCAN clustering algorithm, and realizing automatic identification of modal frequencies; S2, decomposing multi-channel monitoring data by using MvFIF to generate an intrinsic modal function (IMF) group with modal alignment characteristics; S3, calculating the instantaneous frequency and bandwidth of the IMF in step S2 by using HHT, screening out an IMF component containing a target modal frequency, and performing linear reconstruction; and S4, taking the multi-channel monitoring data after the reconstruction in step S3 as system input of the COV-SSI algorithm, calculating a system matrix and an output matrix, and calculating modal damping ratios by using eigenvalue decomposition; the method can consider the space-time correlation between multi-channel data, realize synchronous decomposition of multi-channel monitoring data, and realize automatic and accurate identification of structural modal parameters under different noise levels.

This invention discloses a method for assessing shrimp health status based on multi-source information fusion, which relates to the field of data processing technology. The method includes the following steps: acquiring and preprocessing shrimp images at different growth stages; generating training images using artificial labels; extracting features such as shrimp outline length, area, and shell contrast; establishing a target recognition and feature extraction model based on the training images; identifying shrimp in the monitored aquaculture environment and extracting their behavioral characteristic parameters after training; calculating the behavioral assessment influence coefficient and growth assessment influence coefficient; and obtaining water source characteristic parameters to calculate the disease incidence influence coefficient; finally, generating a comprehensive health status assessment index, comparing it with a health judgment threshold, and outputting the health status judgment result. This achieves effective assessment of shrimp health status, real-time monitoring, and early warning, helping farmers take timely measures to prevent disease occurrence and improve shrimp survival rate and economic benefits.

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 invention provides a voice voiceprint analysis and numeralization method and system for reflecting the yang deficiency state of a human body. The method comprises the steps that a preset number of human body voice data are acquired through voice acquisition equipment, the voice data comprise voice data of people in a yang-deficiency state and voice data of people in a non-yang-deficiency state, and yang-deficiency state labeling information of human body states corresponding to the voice data is acquired; for each piece of voice data, voice global features are extracted from the voice data by using a feature extraction algorithm, and the voice global features comprise a fundamental frequency feature, an energy feature and a Mel-frequency cepstrum coefficient feature; screening the extracted voice global features by using a feature selection algorithm to obtain a feature subset related to the yang deficiency state; constructing a yang deficiency state feature voiceprint group model based on the feature subset; and inputting to-be-analyzed voice data into the yang deficiency state feature voiceprint group model, and calculating and outputting a numerical result reflecting the yang deficiency state of the human body.

The invention discloses a crop phenotype rapid identification system and method based on multi-modal sensor fusion. The crop phenotype rapid identification system comprises a mobile bearing platform, a multi-modal data acquisition module, a master control and data processing module, a wireless communication module, a data analysis module and a result output module. According to the invention, by integrating visible light imaging, multispectral imaging, three-dimensional sensing and other multi-mode sensors and constructing a matched data fusion and intelligent analysis system, rapid, accurate and multi-parameter synchronous identification of crop phenotype information is realized, the fusion of multi-source data effectively makes up for the deficiency of a single sensing mode, and the accuracy of crop phenotype identification is improved. According to the method, the comprehensiveness and accuracy of phenotype parameter extraction are remarkably improved, the adaptability of the system in a variable environment is enhanced, the operation efficiency is greatly improved through automatic and non-contact acquisition and processing procedures, the labor cost and subjective errors are reduced, and the method is suitable for large-scale field high-throughput phenotype screening.

The invention relates to the technical field of agricultural intelligent monitoring, and discloses a method for detecting a multi-layer composite high-strength anti-aging agricultural mulching film, and the method comprises the steps: carrying out the high-frequency noise suppression and edge preservation smoothing of a mulching film image, and obtaining a smooth image; generating a degraded area mask based on the color distribution characteristics and morphological processing; calculating a roughness degree score in combination with the standard texture value; fusing the roughness and color data to perform clustering mapping to obtain a comprehensive aging map; calculating an intensity reduction proportion according to the aging map, and calculating a recovery risk and a pollution level in combination with edge fragmentation characteristics to obtain a final aging quantification index; and inputting the index into a neural network model for deviation calibration to obtain a target aging detection result. According to the invention, accurate quantification of the aging degree of the mulching film and scientific evaluation of the pollution risk in a complex environment are realized.

This utility model discloses a wastewater collection monitoring device, including a wastewater tank. An inlet pipe is located on the upper side surface of the wastewater tank, and a drain pipe is located on the lower side surface. A vortex-shaped groove is formed on the upper surface of the wastewater tank, connecting the inside and outside of the tank. An I-shaped slider is slidably mounted within the groove. A geared motor is installed at the center of the lower surface of the wastewater tank. The output shaft of the geared motor extends through the upper surface of the wastewater tank and connects to a rotating column. The side surface of the rotating column is connected to the slider via multiple telescopic rods. At least one ultrasonic depth gauge is mounted on the lower surface of the slider. The geared motor drives the rotating column, which in turn drives the slider equipped with the ultrasonic depth gauge via the multiple telescopic rods, moving it along a pre-designed vortex-shaped groove trajectory. This trajectory covers most of the area from the edge to the center of the tank, allowing a single sensor to complete a large-scale scanning measurement of the wastewater tank's cross-section.

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.

The invention relates to the technical field of vehicles, and discloses a skylight sunshade curtain configuration method and device, computer equipment and a storage medium, the skylight sunshade curtain configuration method comprises the following steps: when a sunshade curtain configuration signal is received, pre-implanting a plurality of rear sunshade curtain parameter groups matched with front sunshade curtain configuration parameters into a front sunshade curtain controller; the rear sunshade curtain is controlled by a front sunshade curtain controller to do reciprocating motion in the running direction, so that running parameters of the rear sunshade curtain are obtained; and based on the target sunshade curtain parameters matched with the operation parameters in the multiple rear sunshade curtain parameter sets, parameter configuration is conducted on the rear sunshade curtain, and the overall assembling efficiency and reliability of the skylight sunshade curtain can be improved.

The present application relates to the technical field of sediment patch identification, and particularly discloses a mountainous riverbed sand characteristic patch automatic identification method, device, equipment and computer storage medium. The mountainous riverbed sand characteristic patch automatic identification method is characterized in that, through unmanned aerial vehicle image acquisition and advanced image processing technology, efficient and accurate identification of riverbed sediment patches is realized. Compared with the traditional manual identification method, the present application greatly improves the identification efficiency, reduces the subjective error, can comprehensively cover a large area of riverbed area, and dynamically reflects the patch change. The results can provide an important basis for river water and sediment dynamic characteristic analysis, benthic habitat assessment and flood risk prediction, and have significant scientific and application value.

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 belongs to the field of metallurgical analysis technology, specifically relating to a method for determining the silicon content in manganese-silicon alloys using spectrophotometry. The method involves decomposing the manganese-silicon alloy sample with acid to convert the silicon into soluble silicic acid or fluorosilicic acid. A masking agent is added to eliminate the influence of interfering ions, resulting in a clear test solution. This solution is then reacted with a colorimetric reagent to generate a colored complex with characteristic absorption in the visible light region, facilitating quantitative detection by a spectrophotometer. The absorbance of the colorimetric solution is measured at a characteristic wavelength to obtain an optical signal positively correlated with the silicon content. Based on a pre-established linear regression equation, the measured absorbance value is converted into the mass fraction of silicon, thus achieving quantitative analysis of the silicon content. The method provided in this application offers significantly shorter processing time for a single sample compared to traditional titration methods, is simpler to operate, and reduces human error.