87results about How to "Reduce subjective error" patented technology

The invention belongs to the field of hollowing detection, and provides an outer wall hollowing detection method and system, and the method comprises the steps: carrying out the multi-mode detection of an outer wall, and obtaining an infrared thermal imaging image and an ultrasonic detection signal; performing image enhancement processing on the infrared thermal imaging image; respectively carrying out feature extraction on the infrared thermal imaging image and the ultrasonic echo image by using a convolutional neural network; extracting and fusing high-dimensional feature vectors, and aligning an infrared thermal imaging image with an ultrasonic echo image through an image registration algorithm and a spatial transformation model; and classifying the fused feature vectors through a multi-layer sensor according to the data aligned in the step, and outputting the specific position, size and severity of the hollowing area. According to the scheme, the outer wall hollowing detection can be efficiently carried out.

The invention relates to an image segmentation method for a burn wound area, and the method comprises the steps: collecting burn wound images under different spectrums through a multispectral imaging device, and enabling the burn wound images to comprise a plurality of wavebands, namely visible light, infrared light and ultraviolet light; for each spectral image, analyzing reflection and absorption characteristics of a burn area under different wavebands by using optical characteristics of biological tissues, and marking a potential burn area; designing a self-adaptive algorithm, and dynamically adjusting parameters, including exposure and light intensity, of imaging equipment based on feedback of different spectral characteristics; the feature contrast of the burn area is enhanced; preliminarily delimiting the boundary of the burn region according to the enhanced spectral image through a threshold segmentation or region growing algorithm; inputting the multispectral image into a hierarchical processing model, and decomposing different tissue levels of the burn area including epidermis and dermis based on different wave band or color information; on each layering result, a feature extraction algorithm including texture analysis and edge detection is used for extracting detail features of each layer.

The invention relates to a tree painting projection data collection and analysis system and method, and the system comprises a data collection module, a data preprocessing module, a feature extraction module, a quantitative analysis module, a classification and prediction module, and a result evaluation module. The modules cooperate with each other to realize standardized processing, feature extraction, quantitative analysis, intelligent classification and prediction and result evaluation of data. According to the method, the holographic theory and the Ama pothole theory are combined, the drawing features are finely analyzed through the cross-modal comparative learning and graph neural architecture search combined algorithm, a scientific and objective psychological data acquisition and analysis method is provided, the diagnosis sensitivity and specificity are improved, a powerful tool is provided for clinical psychological assessment, and the method is suitable for clinical psychological assessment. The method has important clinical and research values, and has wide application prospects in the fields of psychological problem screening, social psychological services, judicial, public security, marriage matching, talent selection and the like.

According to the single-cell transcriptome cell annotation method and system fused with the large language model, cell type annotation is carried out by constructing the proprietary cue word and utilizing the large language model, and the accuracy and universality of cell annotation are improved. The method has remarkable advantages on cell annotation of non-mode species, and an automatic and intelligent cell annotation process is realized.

The invention discloses a PCB (Printed Circuit Board) back drilling defect detection process, which relates to the technical field of defect detection, and comprises the following steps: placing a PCB to be detected on a detection table; wherein the PCB to be detected is subjected to back drilling processing; obtaining distribution of through holes of the PCB to be detected and positions and directions of back drilling holes; according to the scanning direction, the scanning visual angle and the scanning base point of the laser scanner, point cloud data of the back drilling face of the PCB to be detected and point cloud data of the through hole face of the PCB to be detected are collected; performing depth map conversion on the point cloud data of the back drilling surface of the PCB to be detected and the point cloud data of the through hole surface of the PCB to be detected to obtain a back drilling depth image of the back drilling surface and a through hole depth image of the through hole surface; and according to the direction of the back drilling hole, the back drilling depth image of the back drilling surface and the through hole depth image of the through hole surface, identifying the defect of the back drilling hole of the PCB to be detected. According to the method and the device, the defect detection efficiency of the back drill of the PCB is improved.

The invention provides a rapid three-dimensional modeling method based on seabed point cloud data, a medium and a system, and belongs to the technical field of seabed depth measurement, and the method comprises the steps: firstly, obtaining low-resolution point cloud data through spatial downsampling, and carrying out the multivariate mixed decomposition of the low-resolution point cloud data, and obtaining a stable component and a variable component; constructing an initial three-dimensional grid model based on the stable component, and then dividing the initial three-dimensional grid model into a plurality of sub-regions by adopting a self-adaptive partitioning strategy; and performing partition processing on the original point cloud data according to sub-regions to obtain a plurality of high-resolution point cloud data blocks. And next, performing anomaly identification and noise reduction processing on each high-resolution point cloud data block, reconstructing a corrected high-resolution point cloud data block, performing grid reconstruction and boundary registration, and constructing a complete high-resolution seabed scene three-dimensional model. And the final high-quality seabed scene three-dimensional model is obtained through processing, so that the technical problem that the existing three-dimensional seabed model cannot fully reflect the detail characteristics of the submarine topography is solved.

The present invention provides a method for determining the residual acrylonitrile content in a polyacrylonitrile polymerization liquid, and the residual acrylonitrile content in the polyacrylonitrile polymerization liquid is determined by using a fully automatic potentiometric titrator; the method mainly comprises the steps of preparing a solution to be tested, a blank titration step, and a titration step of the solution to be tested. The present invention is used to provide a method for determining the residual acrylonitrile content in a polyacrylonitrile polymerization liquid, which can quickly and accurately determine the titration end point, thereby improving accuracy and timeliness, so as to better guide actual production. Compared with the traditional manual titration method, the present invention is simple to operate, has accurate liquid addition volume, uniform stirring speed, and sensitive endpoint mutation. The instrument can automatically titrate and stop the reaction, so that the test error is small, and the detection efficiency is greatly improved.

The invention provides a PD-L1 immunohistochemical section tumor identification detection method and system, and the method comprises the steps: collecting various organ PD-L1 sections of a clinical pathological experiment, carrying out high-resolution scanning, pre-identification screening and cloud data storage, and obtaining a PD-L1 section data set of various organs; automatically labeling each tumor cell labeling pixel-level mask, automatically generating a labeling bounding box, and obtaining a tumor cell region labeling data set; according to the tumor cell region labeling data set, preprocessing all positions labeled as effective regions, creating a YOLO v8 deep learning model, training to gradually adjust network weight, and outputting a prediction result of the YOLO v8 deep learning model; and analyzing and calculating whether the prediction evaluation index information of the YOLO v8 deep learning model accords with a training target evaluation index, completing model training, and carrying out accurate positioning and pixel-level region identification detection on PD-L1 immunohistochemical pathological section tumor cells.

The invention discloses a shadowboxing action coordination evaluation method and device based on gravity center height detection, equipment and a medium, and the method comprises the following steps: obtaining a shadowboxing action video, and recognizing the three-dimensional coordinates of human joints; based on the three-dimensional coordinates, identifying the motion style of the shadowboxing; calculating gravity center data corresponding to each action style based on the three-dimensional coordinates; the gravity center data comprises a human body gravity center point and a human body gravity center height difference; and performing coordination scoring according to the constructed standard data and the gravity center data of the shadowboxing action video. According to the method, subjective errors in manual scoring are effectively reduced, the scientificity and fairness of action scoring are improved, meanwhile, a practicer is helped to find and improve unstable points in actions, and the practice effect is improved.

The invention discloses a Parkinson degree identification system based on a UPDRS scale, and the system comprises a camera module which is used for shooting a video and converting the video into a format for analysis; the visual analysis module is used for acquiring key points of each frame of image in the video and analyzing the key points to generate probability scores serving as function evaluation indexes; and a fusion analysis module. According to the system, the visual analysis module analyzes portraits in videos, and compared with manual evaluation according to experience, the system can quantify identification standards, shorten the diagnosis time of doctors on illness conditions, reduce subjective errors and improve the accuracy of results; visual analysis is carried out on the video, a machine learning and deep learning method is applied, judgment of a doctor on the severity degree of the Parkinson's disease based on a UPDRS scale is quantified, and the severity degree of the Parkinson's disease is judged by analyzing the sitting posture, the hand tremor under the standing posture, the muscle stiffness state during standing up, the walking pace and the arm swinging action of the portrait in the RGB video. And automatic evaluation of the Parkinson's disease is realized.

The invention discloses a crop leaf disease quantification method based on deep learning, belongs to the technical field of leaf disease detection, and solves the problems that time and labor are wasted and errors are large when crop leaf disease degree is manually observed and measured in the prior art. According to the method, the RGB image and the depth map are simultaneously input through the RDA U-Net model, so that interference of other leaves is eliminated, the leaf closest to the lens is conveniently found, the disease degree of the crop leaves is automatically quantified by using the deep learning technology, the efficiency and accuracy of disease degree detection are improved, subjective errors of manual observation and measurement are reduced, and the detection accuracy is improved. The method is suitable for large-scale farmland environment.

The embodiment of the invention provides a fish disease detection method and device, equipment and a storage medium. The method comprises the following steps: acquiring video data of fish; according to the video data, dynamic feature data of the fish are determined, and the dynamic feature data comprise the motion trail, the speed and the acceleration; inputting the dynamic feature data into a pre-trained behavior classifier to obtain a behavior type of the fish, determining whether the fish carries a disease according to the behavior type of the fish, and if the fish carries the disease, determining a disease type corresponding to the dynamic feature data and outputting the disease type; whether the fish carries the disease or not is determined according to the behavior type of the fish identified from the video data, the disease detection efficiency and accuracy are improved, the manual monitoring cost and subjective errors are reduced, fish diseases can be found and treated in time, and a farmer can take measures in time.

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 relates to a tongue feature analysis method and device, electronic equipment and a storage medium. A trained tongue detection model and a trained tongue segmentation model are fused to obtain a target segmentation model, an image data set is segmented through the target segmentation model to obtain a tongue segmentation image data set, and the tongue segmentation image data set is labeled to obtain a tongue feature classification data set. Training a tongue feature classification model based on the tongue feature classification data set to obtain a trained tongue feature classification model, obtaining to-be-detected target image data, inputting the target image data into a target segmentation model to obtain tongue segmentation image data of the to-be-detected target image data, and obtaining the tongue segmentation image data of the to-be-detected target image data. According to the method, the trained tongue feature classification model is used for performing feature analysis on the tongue segmentation image data to obtain a tongue feature analysis result, so that the training efficiency of the model is improved, and the classification stability and reliability are also remarkably improved.

The invention discloses an anomaly detection method for key parts of a coal mine hoist, which comprises the following steps of: recording operation videos of five key parts of the coal mine hoist and extracting images to construct a picture set; and carrying out brightness enhancement on the images by utilizing a trained image enhancement model, and making a training and testing data set. And training an anomaly detection model based on the convolutional neural network, wherein the model can identify the state of the key part of the elevator. In practical application, the key part of the elevator is monitored in real time by using the enhanced image and the anomaly detection model. By comparing the detected position coordinate of the key part with the center coordinate under the normal condition, the abnormal state can be found in time. Once abnormality is detected, the system stops the operation of the elevator immediately, and gives an audible and visual alarm to prompt a specific abnormal position. According to the method, the detection precision is remarkably improved, the false detection rate is reduced, the operation and maintenance cost of the underground coal mine elevator is greatly reduced, and meanwhile the overhaul and maintenance efficiency and safety are improved.

The invention relates to the technical field of mine management and control, and particularly discloses an intelligent mine management and control optimization method based on digital twinning. By monitoring and analyzing the geological condition, the microseismic influence, the slope form and the support instability of the monitoring area where the target mine slope is located corresponding to the current monitoring time period and calculating the deformation degree evaluation coefficient of the monitoring area where the target mine slope is located corresponding to the current monitoring time period, the deformation condition of the target mine slope is visually displayed. The problems of complexity, time consumption, labor consumption and the like of periodic patrol and measurement of mine management personnel are effectively solved, subjective errors of mine slope deformation analysis are reduced to a certain extent, the accuracy, reliability and rationality of a mine slope deformation analysis result are ensured, and meanwhile, the mining safety of the mine personnel is effectively ensured.

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 utility model discloses a vehicle -mounted multifunctional soil detection device. The detection device includes vehicle -mounted fixing frame, liftable slide support and the first drive mechanism of driving its lift. The liftable slide of being equipped with on slide support and the second drive mechanism of driving its lift. The integrated soil taking mechanism for drilling the soil and the in -situ sensor for detecting the physicochemical parameter of soil are on the slide. The device is independently provided with loose degree measurement component. When operating, the first drive mechanism adjusts the whole machine ground clearance, and the second drive mechanism drives the slide to drop, makes the soil taking mechanism and in -situ sensor synchronous insertion soil, completes sampling and physicochemical parameter detection, and the loose degree measurement component independently carries out soil loose degree measurement. The utility model discloses through modularization and multistage drive design, has realized the automation collaborative operation of soil taking, pH value, conductivity, loose degree and other multiple detection tasks, and the structure is reasonable, convenient operation, and the detection efficiency and data accuracy are high.

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.

The invention discloses a fruit fly insect eclosion monitoring method and device, and the method comprises the steps: obtaining the illumination intensity in an adult cabin in response to an infrared signal transmitted by an infrared sensor; a first instruction is sent out, the first instruction is used for controlling a camera to shoot a first image of the plane where the joint of the eclosion bin and the adult bin is located and recording the first sending moment of the first instruction, and under the condition that the illumination intensity is smaller than a preset light intensity threshold value, a light supplementing lamp is controlled to be turned on; performing image recognition on the first image to obtain a first number of the fruit fly insects in a plane where the joint of the eclosion bin and the adult bin is located and features before wing spreading; determining the eclosion cycle of the fruit fly insects, and calculating the difference value between the first number and the second number; and determining the current eclosion condition of the fruit fly insects according to the difference value and the eclosion period, wherein the current eclosion condition comprises the eclosion rate and the eclosion rate. According to the application, the accuracy of monitoring the eclosion stage of the fruit fly insects can be effectively improved.

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.