29 results about "Objective analysis" patented technology

The invention discloses a college dormitory noise multi-dimensional evaluation method based on psychoacoustic and sound field simulation, and belongs to the field of building environment and noise control. The objective of the invention is to solve the problem that an existing dormitory noise evaluation method is single in index and neglects subjective perception and spatial distribution heterogeneity. The method comprises the following steps: acquiring physical data and original audio of dormitory noise through multi-point synchronous acquisition; calculating loudness, sharpness, roughness, fluctuation intensity and comprehensive psychoacoustic annoyance (PA) by using a psychoacoustic model, and scientifically quantifying subjective interference of noise; meanwhile, sound field simulation is carried out based on the accurately established three-dimensional dormitory model, a sound pressure level distribution cloud picture is generated, and a standing wave phenomenon and a sound energy accumulation area are visually revealed; and finally, generating a targeted noise reduction optimization strategy by integrating subjective and objective and spatial analysis results. According to the method, full-chain diagnosis of physical characteristics-subjective perception-spatial distribution of dormitory noise is realized, and a scientific basis is provided for accurate and efficient noise treatment.

The invention relates to the technical field of computer vision, in particular to a fabric color fastness analysis method and system based on image processing. Comprising the following steps: acquiring a multispectral image of a to-be-detected fabric, and synchronously acquiring multi-factor data; processing the multispectral image to generate an enhanced image; performing image recognition on the gray sample card in the enhanced image to generate a calibration image; performing spectral data projection on the calibration image through a dimension reduction algorithm, and performing color analysis and quantification on color change to generate color difference features; extracting texture features by using a conditional generative adversarial network, carrying out image recognition and calculation on the calibration image through a decoupling algorithm, and generating a region credibility graph; and inputting the chromatic aberration features, the texture features, the multi-factor data and the regional credibility map into an image feature fusion model for mapping, performing analysis through gradient visualization, and outputting an abnormal feature saliency map. According to the method, a color fastness objective analysis closed loop is created, so that the accuracy and the universality of an analysis result are improved.

The disclosure related to methods and systems for image data analysis for objectively analyzing joint motion data. In one embodiment, the methods and system disclosed herein use automated methods to capture image data, extract motion data, and determine an objective score for one or more joints in one or more motions. The system and methods disclosed herein comprise a motion capture system with one or more image capture devices to obtain motion data for a subject and using one or more regression models and analysis techniques, various objective scores can be generated related to the subject's joint motion.

The invention discloses a respiratory support aided decision-making method and system, and the method comprises the steps: obtaining lung image data of a patient, and carrying out the preprocessing of the lung image data, so as to extract a lung region of interest; inputting the lung region of interest into a prediction model to obtain image quantitative analysis data including lesion information; acquiring clinical index data of the patient, and fusing the clinical index data with the image quantitative analysis data to generate respiratory support comprehensive evaluation parameters; and outputting respiratory support decision auxiliary information based on the respiratory support comprehensive evaluation parameters. The respiratory support aided decision-making method and system provided by the invention aim at realizing automatic and objective analysis of the lung image, intelligently fusing the analysis result with the clinical indexes, and providing quantitative decision-making support for use of a respirator.

The present invention relates to: a composition for validating next generation sequencing (NGS) panels, comprising homozygote DNA and control genomic DNA; a kit for validation of NGS panels, comprising the composition; a validation method for NGS panels through the analysis of false negative variants, limit of detection, and false positive variants; and a method for providing information to enhance the specificity of NGS panels. In particular, the validation method of the present invention enables the objective analysis of the frequency of false negative variants, the frequency of false positive variants, and the limit of detection for NGS panels, making it effectively usable in the validation of NGS panels.

This invention discloses a water pollution data monitoring method and system based on multi-objective analysis, belonging to the field of water pollution source tracing technology. The method includes: collecting a GIS map of the target watershed and time series data on rainfall, flow, and water quality; hydrologically segmenting the flow series according to the opening and closing status of dams to obtain surface runoff and baseflow components; constructing a two-dimensional water quality observation matrix, setting pulse and delay constraints in conjunction with rainfall and runoff components, and using a non-negative matrix factorization algorithm with ratio constraints for source analysis to obtain a source feature matrix and a source contribution time coefficient matrix; identifying pollution source emission attributes based on the ammonia nitrogen to total phosphorus ratio and the correlation between the source contribution coefficient and rainfall in the source feature matrix, and marking them on the GIS map. This invention integrates dam-controlled hydrological segmentation with matrix factorization based on physicochemical constraints to achieve refined source tracing, improving the accuracy and interpretability of pollution source identification.

The invention discloses an outpatient nursing evaluation system. Relates to the technical field of outpatient nursing. Comprising a multi-source data acquisition module, a medical knowledge graph module, an intelligent evaluation module and a result analysis module, the multi-source data acquisition module is used for acquiring outpatient nursing whole-process data; the medical knowledge graph module processes the outpatient nursing full-process data, and establishes and updates a knowledge graph according to a processing result; the intelligent evaluation module completes comprehensive evaluation through a mixed evaluation model based on the updated knowledge graph and outputs an evaluation result; and the result analysis module analyzes the evaluation result, visually presents the evaluation result and generates an improvement suggestion. The evaluation data can be guaranteed to be comprehensive and accurate, and the evaluation basis is tamped; the construction of the double-track knowledge graph realizes semantic association modeling, so that the evaluation subjectivity is reduced, and the result consistency is improved; the hybrid evaluation model fuses subjective weight and objective analysis, outputs an accurate result with a confidence interval, and balances scientificity and practicability.

This invention discloses a method and system for assessing the autonomous capabilities of ground unmanned equipment, a computing device, and a storage medium. The method first constructs an assessment system for the autonomous capabilities of ground unmanned equipment, then evaluates the system using a fuzzy optimization model, a grey relational model, and fuzzy interaction entropy. The assessment results of the three models are then used as different information sources and fused using D-S evidence theory to obtain the final assessment result. The advantages of this invention are that it can accurately and quantitatively evaluate the grey, fuzzy, and difficult-to-quantify factors present in the assessment of autonomous capabilities, thereby providing a basis for objective analysis of the autonomous capability level of ground unmanned equipment.

The invention discloses a method and device for objectively dividing meteorological event intensity grades and a storage medium, and the method comprises the steps: carrying out the sorting processing of a meteorological event data set of a to-be-divided region, obtaining a linearized data sequence, endowing each data sample of the data sequence with a corresponding serial number, and obtaining the serial number of each data sample; establishing a fitting model of the data sequence by adopting a least square method and taking the serial number of each data sample as an independent variable, and substituting the serial number of each data sample of the data sequence into the fitting model for calculation to obtain a fitting sequence; calculating a difference value between each data sample of the data sequence and each data sample of the fitting sequence to obtain a difference sequence; analyzing a data sample of the difference sequence, and determining an intersection point set; and dividing the meteorological event data set according to the intersection point set to obtain a series of intervals of meteorological event intensity levels. The method can objectively analyze the internal statistical law and sudden change characteristics of the data from the data, and achieves the objective division of the meteorological event intensity grade.

The invention relates to the technical field of intelligent lawn maintenance and robot perception, and discloses a lawn health monitoring and diagnosis method based on machine vision and multi-sensor fusion, and the method comprises the steps: 1, starting a machine vision module to monitor a lawn, and collecting the image information of the lawn in real time; meanwhile, a soil humidity sensor is started to detect soil humidity of different positions of the lawn in real time, and soil humidity data is acquired; the image information comprises a color index, a density index and a health condition index of the lawn; and 2, processing the image information acquired in the step 1. A machine vision module and a soil humidity sensor are adopted to automatically collect image and humidity data, objective analysis and diagnosis are carried out through an image processing and data fusion algorithm, automatic, high-precision and all-weather lawn health monitoring is achieved, and the problems that in the prior art, subjective judgment depends on manual inspection, time and labor are consumed, subjectivity is high, and management efficiency is low are solved.

The present application relates to the technical field of image processing and style transfer, and specifically discloses a cross-scene special effect style self-adaptive method based on meta-learning, which constructs an implicit high-dimensional meta style prototype vector library offline, dynamically selects and fuses the prototype vector according to the input scene semantic description in real time, and enhances the universality and flexibility of style transfer; scene analysis and saliency detection are used to differentially process different regions with different style transformation tolerance, thereby reducing the risk of distortion of key content; a human-computer interaction interface is introduced to balance automation and subjective setting by combining user subjective style preference with scene objective analysis results, thereby improving the controllability of the results; finally, multi-scale features are extracted through a multi-layer convolutional network, linear affine adjustment is performed in combination with a differential style field, and a target picture that maintains content recognition and is rich in artistic style is rendered. The present application improves the adaptability and effect quality of style transfer.

The invention relates to the technical field of digital gaps, and provides a specific crowd digital gap level analysis table determination method and device and electronic device.The method comprises the steps that initial text data are obtained, and an initial entry pool containing three types of initial entries is generated according to the initial text data, the entry type of the initial entry comprises an access trench entry, a use trench entry and an effect trench entry; consultation information aiming at the initial entry pool is acquired, the initial entry is modified according to the consultation information, and candidate entries and an initial scale are generated; performing multi-dimensional evaluation on each candidate entry in the initial scale, and screening out a target entry from the candidate entries according to a multi-dimensional evaluation result; and verifying whether the target scale reaches the standard or not, and outputting the target scale when the target scale reaches the standard. According to the technical scheme provided by one or more embodiments, a reliable evaluation tool suitable for the digital gap level of the specific crowd can be constructed, and comprehensive and objective analysis of the digital gap level of the specific crowd is realized.

The invention discloses a wine brewing rice hull quality digital analysis and discrimination system and method, and relates to the technical field of wine brewing auxiliary material quality analysis and evaluation, and the system comprises a flavor analysis module, a texture analysis module, a data processing module and a quality digital discrimination module. According to the method, GC-IMS flavor detection and texture analyzer quantitative analysis are introduced into wine brewing rice hull quality evaluation for the first time, multi-dimensional and multi-view quality scientific characterization is achieved, the blank of a traditional evaluation technology is filled up, a rice hull quality digital judgment model is constructed based on machine learning, and the evaluation accuracy is improved. Sensory evaluation depending on experience is converted into a quantitative index system based on flavor composition and texture characteristics, objective analysis and data-driven evaluation of rice hull quality are achieved, the bottleneck of traditional evaluation timeliness and precision is broken through, the efficient and accurate screening requirement is met, a scientific foundation is laid for accurate regulation and control and raw material management and control of the rice hull steaming process, and the method is suitable for large-scale popularization and application. A new technical support is provided for guaranteeing the fermentation stability and the flavor consistency of the wine body.

The invention discloses a spectrum-based tobacco leaf analysis method, device and equipment and a storage medium, and relates to the technical field of intelligent tobacco leaf analysis. The method comprises the following steps: acquiring spectral data of a tobacco leaf sample; preprocessing the spectral data to obtain a preprocessed characteristic spectrogram; training a multi-class machine learning model set according to the preprocessed characteristic spectrogram and a plurality of sensory dimension classification labels corresponding to the preprocessed characteristic spectrogram to obtain a trained multi-class machine learning model set; and predicting the spectral data of the new tobacco leaf sample according to the trained multi-class machine learning model set to obtain a plurality of sensory dimension classification prediction values. Therefore, by preprocessing the original spectral data, the capturing accuracy of the tobacco features in the model training process is improved, so that the model prediction accuracy is improved; meanwhile, the machine learning model is used for tobacco leaf analysis, so that the analysis efficiency can be improved, to-be-analyzed tobacco leaves are prevented from being damaged, an objective analysis result is obtained, and the analysis cost is reduced.

The invention relates to a pollution and carbon reduction and technical economy multi-target analysis method and system, belongs to the field of industrial environment and economy collaborative evaluation, and is suitable for high-pollution and high-carbon emission industries such as thermal power, cement and steel. According to the scheme, three industrial production systems and whole-process emission category accounting are covered, a hybrid model is adopted to calculate emission, an equivalent value, a collaborative emission reduction degree and marginal emission reduction cost, and a high-dimensional data output technology priority is processed through a random forest, a support vector machine and LASSO linear regression. The problems of incomplete accounting, low precision, difficult high-dimensional processing and the like in the prior art are solved, quantitative reference is provided for technology screening and policy making, and pollution reduction, carbon reduction and economic collaborative development are promoted.

The invention relates to the cross technical field of artificial intelligence and sports science, and discloses a martial art learning process intelligent analysis system based on multi-modal deep learning, which comprises a data acquisition module, an analysis module, a result generation module and a feedback generation module. The data acquisition module acquires the dynamic effect characteristic flow of the learner by using visual acquisition equipment, and synchronously acquires the intention characteristic flow by combining with a surface myoelectricity sensor, a pressure-sensitive insole and a respiration sensor. The analysis module is integrated with a cross-modal collaborative perception model, and the model adopts a double-flow encoder to process dynamic effect and intention features in parallel. The core of the method is that a bidirectional cross-modal attention mechanism is introduced, two types of features are deeply fused through bidirectional query of intention to dynamic effect and dynamic effect to intention, and the collaborative relationship between the appearance and the internal intention in martial arts actions is disclosed. Quantitative and objective analysis of martial arts actions is realized, and scientific, accurate and real-time training guidance is provided for martial arts learners.

The invention discloses a tunnel unmanned water detection method and system based on transient electromagnetic detection data, and relates to the field of tunnel water detection, initial depth geological parameter marking is carried out on a signal curve based on a landmark curve segment comparison library, and real geological parameters are obtained and marked through verification such as drilling coring, geological radar and sound wave detection; forming a reference data group with a real label; constructing an analysis reference data set through curve clustering, key difference feature extraction and a retrieval structure, and establishing a geological analysis model; the model is used for automatically explaining real-time collected signals, a three-dimensional real-time geological representation model in front of a tunnel face is constructed in combination with a spatial interpolation algorithm, and accurate recognition and connectivity evaluation of a water-rich abnormal area are achieved. According to the invention, completely unmanned and flexible acquisition in the water exploration process is realized, the precision, real-time performance and safety are remarkably improved through data-driven objective analysis, and the risk of water and mud bursting in the tunnel is effectively reduced.

The invention relates to an insulation state evaluation method and system based on space charge characteristics, and the method comprises the steps: obtaining an original image of space charge density distribution, extracting a curve contour, and recognizing a curve type; based on the curve contour and the curve category, extracting a local average change rate and a peak value feature of the curve; and on the basis of the local average change rate and the peak value characteristics, in combination with the constructed sample database, performing insulation state evaluation in a weighted form to obtain an insulation state evaluation result. According to the invention, by introducing an image recognition technology, key characteristic quantities in a space charge distribution diagram can be extracted for insulating material samples in different aging states, and objective analysis and quantitative evaluation of space charge information are realized.

The invention discloses a comprehensive energy intelligent operation and maintenance system based on cloud-side human-end cooperation and a dynamic decision-making method, and aims to solve the problems that an existing energy management system is split in architecture, unreliable in communication and single in decision-making dimension. The system comprises a cloud service layer, an edge system layer, a human field layer and an end equipment layer. The cloud service layer constructs an interactive digital twin platform and is used for equipment full-life-cycle simulation and management; the edge system layer deploys an AI-driven situation analysis module through an edge computing node, and local energy distribution is optimized in real time; the personnel field layer is configured with an expert mobile terminal and an AR auxiliary tool to realize closed-loop processing of an operation and maintenance work order; and the end equipment layer realizes multi-modal data acquisition of distributed energy equipment through a three-level Internet of Things. The invention further provides a dynamic decision-making method, the method constructs a multi-dimensional evaluation decision-making model fusing subjective scoring of expert experience and objective analysis of the AI model, the operation and maintenance priority is calculated through a fusion formula, and a decision-making instruction is generated and issued. Through a deep cooperation and innovation decision-making mechanism of a four-layer architecture, the energy utilization efficiency can be remarkably improved (more than 20%), and the response time of sudden failures is greatly shortened (more than 50%).

The application discloses a hair perming device with a scalp health detection system and a detection method. Through an image processing system integrating a camera array and a processor, quantitative detection and objective analysis of key indicators of the scalp (such as hair grease, dandruff, redness, etc.) can be performed before, during and after hair perming, which completely changes the traditional subjective and rough judgment mode relying on naked eyes and experience and provides scientific data basis. Moreover, through real-time monitoring, the sub-health or risk state (such as inflammation, sensitivity, damage, etc.) of the scalp can be identified in time, and when high risk is detected, the system can automatically issue an alarm or intervention, thereby effectively preventing safety problems such as contact dermatitis, hair follicle damage and aggravated hair loss caused by improper hair perming operation.

Technical field: The invention relates to the use of artificial intelligence (AI) in education for the objective evaluation of student performance and the generation of detailed feedback. Abstract: The present invention describes an AI-supported assessment system for the objective analysis and evaluation of student performance. The system comprises a data acquisition component for collecting performance data from various sources such as tests, homework, and oral contributions. A machine learning component analyzes this data to assess specific learning aspects such as comprehension, application, and creativity. A natural language processing (NLP) component evaluates students' written and oral contributions with regard to their language skills and content comprehension. Based on these analyses, a feedback generation component generates personalized feedback that includes strengths, weaknesses, and suggestions for improvement. A teacher adaptation component provides teachers with insights into the performance data and suggests targeted teaching strategies and materials to address the individual learning needs of the students. Inventive achievement: The proposed system improves the objectivity and detail of student assessments through the use of advanced AI technologies and offers personalized feedback as well as targeted lesson adjustments.

The invention relates to the technical field of image analysis and automatic testing, in particular to a shrimp disease resistance character intelligent testing system and method based on automatic image acquisition and analysis. The system comprises an automatic inspection function module, a data transmission function module and an information processing function module. According to the method, the fundamental transformation of the shrimp disease resistance character test from manual observation recording to automatic and intelligent judgment is realized. By integrating an autonomous navigation inspection robot, a multi-view image acquisition device and an intelligent analysis system based on machine learning, a set of full-automatic and continuous operation test system is constructed. The system can automatically complete timed image acquisition of a large number of tested individuals, objective analysis is carried out on prawn body postures by using the trained YOLO model, the survival state and death time are accurately judged, and the inherent defects of high labor intensity, high subjective judgment error, high recording error rate and the like of manual guarding are effectively overcome.

According to one aspect, there is provided a method performed by one or more computers of generating a product of a synthesis process, comprising: selecting a parent of the product; identifying at least two objectives of the product, wherein each objective of the at least two objectives is based on a techno-economic analysis of the synthesis process; and determining a variant of the product based on the techno-economic analysis of the synthesis process.

The invention discloses a patient monitoring system and method, and the method comprises the steps: collecting video data including a patient through an image collection device, analyzing the video data to obtain the activity information of the patient, and displaying a monitoring result and the activity information corresponding to the same patient and generated in the same preset time interval on a display unit on the same screen, or an activity correlation result of the monitoring result and the movement amplitude of the patient is calculated, and the monitoring result and the activity correlation result which correspond to the same patient and correspond to the same patient in time are displayed on the same screen of the display unit, so that the medical care can be effectively assisted to objectively analyze and evaluate the activity of the patient in the diagnosis and treatment process.

The invention discloses a pulse wave signal break point standardized detection method, which relates to the technical field of pulse condition detection, and comprises the following steps: firstly, traversing a signal by using a sliding window, determining the position of a wave crest, and further determining the position of a wave trough between the wave crests; a segmentation point is between a wave trough and a wave crest, so that a proper interval is selected in the signal segment to carry out next-step detection; in the selected interval, according to the position of a signal minimum value point, the change condition of a first-order derivative and the like, carrying out preliminary determination on a segmentation point; due to the fact that the pulse wave signals have high periodicity, in most cases, the ascending duration of each period of the same person needs to meet normal distribution; the preliminarily determined segmentation points can be evaluated by using outlier detection, possible detection errors are screened out, and further detection is carried out. According to the method, relatively high detection accuracy can be kept under relatively high interference such as respiratory waves and human body jittering, and the consistency of single-cycle segmentation is ensured, so that the accuracy of subsequent objective analysis is enhanced.

This application discloses a method, apparatus, equipment, and storage medium for generating geological disaster monitoring views, relating to the field of big data, and aims to solve the problem of inaccurate and inefficient monitoring of geological disasters. The method includes: extracting rasterized features by fusing multi-source data, quantifying various geological environmental features into feature values, converting them into information based on their historical correlation with geological disasters, and then obtaining the comprehensive state information of each unit through weighted fusion, finally outputting a monitoring view. This method replaces manual experience-based interpretation with data-driven objective analysis, which can significantly improve the accuracy and efficiency of geological disaster area monitoring.

The invention discloses a BIM-based building structure intelligent design analysis system, and relates to the technical field of building information model and electric digital data processing, and the system comprises a model conversion module which is used for receiving original BIM model data, and mapping building components in an original BIM model and an association relationship between the components into a multi-layer attribute graph model. According to the intelligent design analysis system for the building structure based on the BIM, the BIM model is converted into a multi-layer attribute graph structure, and a parallel computing architecture is adopted for processing, so that the multi-target analysis efficiency of a large-scale building model is effectively improved. By means of a double-verification mechanism and a dynamic feedback correction process, the design optimization scheme can pass physical performance verification and standard conformity screening while the calculation speed is kept, and the accuracy of an analysis result and engineering feasibility are ensured.

The invention discloses a multi-source sample database building method and system for sodium carbonate production, and relates to the technical field of sodium carbonate chemical industry and big data. Comprising the following steps: repairing damaged sodium carbonate historical data, and eliminating abnormal prompts in system operation; collecting and extracting current data in real time, and depicting the operation state of sodium carbonate production equipment based on current characteristics; data formats are unified, and a sodium carbonate sample database is constructed. According to the method, the unreadable historical data file is recovered, and the pop-up window exception in the program running process is eliminated; an equipment operation state characterization method based on a dynamic threshold value is constructed, and the operation state of sodium carbonate production equipment is accurately described; space-time association of multi-source data is realized, information query, calling and use are facilitated, and comprehensive and objective analysis of a soda ash production process is facilitated; real-time data acquisition and real-time sample database information updating are achieved, and the method has important significance on follow-up intelligent workshop dynamic production optimization.

The invention relates to a cigarette consumer group analysis method based on video data, and aims to overcome the limitations of high cost, low efficiency and strong data subjectivity of a traditional investigation mode. Video acquisition equipment is deployed at a key position of a cigarette retail terminal, field video data is acquired in real time and transmitted to a storage module, videos are preprocessed, human body targets in the videos are identified by using a target detection algorithm, a unique identifier is allocated to each target through a multi-target tracking technology, and the motion trail of each target is continuously recorded. And automatically identifying cigarette consumers based on a preset consumption behavior rule. Then, multi-dimensional attributes of the crowd are extracted; and finally, constructing a consumer portrait according to the features, performing classified statistics and behavior rule analysis, and displaying a result through a visual chart. According to the method, automatic and objective analysis is realized, the data accuracy and efficiency are remarkably improved, and a scientific basis is provided for a tobacco enterprise to formulate a precise marketing strategy.