634 results about "Factor analysis" patented technology

The invention relates to the technical field of data processing, and discloses a financial transaction anomaly detection and risk assessment method and device based on artificial intelligence. The method comprises the following steps: collecting and preprocessing multi-source financial transaction related data to obtain a multi-dimensional transaction data set; performing feature engineering and abnormal mode recognition on the multi-dimensional transaction data set to obtain an abnormal transaction recognition result; based on the abnormal transaction identification result and the multi-dimensional transaction data, constructing a financial domain knowledge graph to obtain a relation network between transaction entities; performing multi-dimensional risk assessment on the relation network among the transaction entities to obtain a risk score and risk description data; based on the risk score, performing time sequence analysis and trend extrapolation on the risk description data to obtain a dynamic risk early warning signal; and performing scene simulation and multi-factor analysis on the dynamic risk early warning signal to obtain a risk management strategy suggestion. The efficiency and accuracy of financial transaction anomaly detection and risk assessment based on artificial intelligence are improved.

The invention discloses a building ground real-time settlement monitoring device and a use method thereof, and belongs to the field of building structure safety monitoring. The monitoring device comprises a hierarchical sensor network which is used for carrying out multi-time-scale real-time data acquisition and comprehensively obtaining deformation data and related environmental parameters of a building structure; the data processing and analyzing module is used for performing real-time processing and intelligent analysis on the acquired data, and identifying and classifying abnormal deformation characteristics of the building structure in time; the deep learning prediction module is used for quantitatively predicting the probability state and the evolution trend of building settlement by constructing a multi-scale time sequence prediction model; the multi-factor analysis module is used for carrying out coupling modeling and comprehensive analysis on the environmental factors, the structural characteristics and the abnormal evolution process so as to identify key influence factors and action mechanisms thereof; and the risk assessment and early warning module is used for performing grading assessment on the building settlement risk based on the prediction and analysis result and generating corresponding early warning information and decision support schemes.

The invention provides an intelligent post matching and employee growth path planning system and method and a storage medium. The system comprises a data acquisition and processing module, an employee portrait construction module, an intelligent post matching module and an occupational path planning module. The data acquisition and processing module collects employee performance data, education background, work experience and other information from internal and external data sources. The employee portrait construction module constructs a comprehensive portrait of each employee by using a clustering analysis and factor analysis technology; the intelligent post matching module performs intelligent matching by applying a recommendation system algorithm according to post demands and employee portraits, and finds the posts most suitable for the current capabilities and development desires of the employees; the occupational path planning module analyzes the industry development trend and future skill requirements based on the prediction model, and provides personalized occupational development paths and training suggestions for employees. According to the invention, historical performance evaluation and industry trend analysis of employees can be integrated, and comprehensive post matching and occupational planning solutions are provided.

The invention discloses a land degradation supervision method based on remote sensing monitoring, and relates to the technical field of land degradation monitoring. The method is used for solving the problems of hidden degradation risk identification and dynamic propagation simulation. According to a multi-temporal thermal infrared remote sensing image, daily variation characteristics of surface temperature are extracted through spectral analysis, a recessive salinization risk distribution map is constructed by combining measured data of soil salinity, and interference of vegetation coverage is overcome. And multi-source remote sensing and geographic data are fused, dominant driving factors significantly associated with the salting risk are screened, and the degeneration cause is clarified. And in combination with ecological restoration potential and treatment resource constraints, calculating an antagonism index of a driving factor and the restoration potential, and dividing degradation threat levels. And a natural hydrology and human activity dual-path propagation model is constructed, a risk space-time evolution process is simulated, and a future hotspot migration path and an optimal blocking window are predicted. According to the method, accurate early warning and active prevention and control of land degradation are realized through a technical chain of risk identification, driving analysis, threat grading and dynamic simulation.

A transformer-based student performance prediction and reasoning intervention is disclosed. The system comprises a data repository coupled to a transformer-based prediction module that processes student data through multi-head attention mechanisms to generate performance predictions and identify potential learning shortfalls. A reasoning-enhanced large language model algorithmically generates personalized corrective action plans by applying structured decomposition of learning challenges, multi-step reasoning, and hypothesis testing. An algorithmic prompt formulation system optimizes inputs using field-specific, level-specific, and shortfall-specific templates. The system implements a workflow including shortfall detection against educational thresholds, causal factor analysis, intervention generation, and adaptive refinement based on outcomes. This approach enables early identification of academic challenges and timely implementation of personalized interventions to improve student learning outcomes.

The invention discloses a slope disease identification method and device based on an unmanned aerial vehicle and a machine vision technology, and the method comprises the steps: carrying out the multi-angle image collection of a slope region on a preset flight path through an unmanned aerial vehicle carrying a high-resolution camera; preprocessing the image, and performing feature extraction and image enhancement; recognizing and classifying disease features by combining an improved YOLOv8-seg instance segmentation algorithm, wherein slope diseases such as cracks, landslides, collapse and the like are covered; quantifying the risk level of the slope disease by using a rule model or a multi-factor analysis method based on the identification result; and carrying out dynamic change prediction on the acquired disease time sequence image by adopting a long-short term memory network (LSTM), and predicting the development trend and the potential instability time of the disease. On the basis of image data collected by the unmanned aerial vehicle, real-time monitoring and dynamic analysis are carried out on the slope diseases, potential hidden danger areas are found in time, disease features can be accurately recognized, and subjective errors possibly caused by manual judgment in a traditional method are avoided.

The invention discloses an operating vehicle risk prediction method and platform based on multi-factor analysis, and relates to the technical field of automobile information processing, and the method comprises the steps: collecting and obtaining a vehicle multi-dimensional operation data stream; extracting and obtaining risk factor information; crawling based on the risk factor information to obtain a risk data set, and carrying out weight distribution on the risk factor information by adopting the risk data set to obtain weight distribution information; training and optimizing the risk data set based on the weight distribution information, and constructing a vehicle risk prediction model; and performing risk prediction on the vehicle multi-dimensional operation data flow by using the vehicle risk prediction model, outputting a vehicle risk prediction score, and performing vehicle risk strategy management based on the vehicle risk prediction score. The technical problems of dependence of a single factor and incomplete data in an existing vehicle risk scoring system are solved, and the technical effects of providing more accurate and comprehensive vehicle risk prediction through multi-dimensional factor analysis and realizing personalized risk management are achieved.

The invention relates to the technical field of power state monitoring, in particular to an intelligent electric energy meter capable of monitoring and reporting operation errors, and the electric energy meter comprises an error operation parameter monitoring module, an error space-time correlation modeling module, an error propagation path analysis module, an error dynamic regulation and control module, an error adaptability verification module and an error transmission state summarization module. According to the method, the operation deviation is captured in real time, the abnormal time period and nodes are marked, the abnormal event positioning and recording capacity is improved, an abnormal distribution panorama is constructed, path optimization is supported, a high-error path is screened by analyzing a weight value and an influence factor, an error propagation hierarchical structure is optimized, and the load proportion and the connection path are dynamically adjusted; multiple paths are configured to reduce the transmission risk, the system robustness is enhanced, the state response and error correction precision is improved through real-time monitoring and abnormal verification, key information is archived, analyzed and extracted to generate a report record, and intelligence and high efficiency of electric energy meter error monitoring are achieved.

A prefabricated building component stability monitoring and early warning method and system, relating to the technical field of engineering project management. The method comprises: on the basis of a target building engineering drawing, constructing a construction node sequence; generating a plurality of engineering construction simulation models; acquiring a sample data set of a plurality of construction nodes, performing stability-associated factor analysis, and constructing a node factor mapping sequence; by using the node factor mapping sequence as monitoring data, performing construction monitoring, and determining construction monitoring data of a first construction node; on the basis of the construction monitoring data, performing mapping and acquiring a first engineering construction simulation model, performing error analysis by means of a twin comparison channel, and determining a first construction error; and on the basis of the first construction error, generating a construction early warning signal, and, on the basis of the construction early warning signal, acquiring a first construction compensation parameter and performing subsequent construction compensation for target building engineering, thereby achieving the technical effects of improving the safety and reliability of a prefabricated building structure and reducing potential risks and losses.

The invention discloses a geological static and mining disturbance dynamic factor coupling disaster-causing risk prediction method, and relates to the technical field of geological risk prediction methods. The method comprises the following steps of S01, geological static factor analysis, S02, mining disturbance dynamic factor analysis, S03, coupling disaster-causing risk prediction model construction, S04, risk assessment and early warning system development, S05, early warning mechanism establishment, S06, model verification and S07, risk management and decision support. The accuracy of geological disaster risk prediction is remarkably improved; according to the method, a comprehensive risk prediction model is constructed by quantifying the complexity of a geological structure, such as an area coefficient, a structural area loss coefficient and the like, and combining ground stress changes and underground water level changes caused by mining activities; the data-driven method can accurately predict the occurrence probability of geological disasters, provides more scientific decision support for mine safety management, and effectively reduces the occurrence risk of geological disasters.

The invention provides a lake water chemical oxygen demand internal and external source pollution quantitative tracing method and system, and relates to the technical field of pollution quantitative tracing. The method comprises the following steps: performing three-dimensional fluorescence spectrum analysis on water samples at different point positions of a lake to obtain fluorescence excitation / emission matrix spectrum EEMS data; performing parallel factor analysis on the EEMS data, identifying and classifying the fluorescent components of the dissolved organic matter DOM, and determining the type characteristics of each component; based on the maximum fluorescence intensity and the corresponding COD value of the fluorescence component, establishing a standardized multiple linear regression equation by adopting ridge regression analysis, and calculating the contribution percentage of each fluorescence component to the COD of the lake water body; analyzing influence paths of potential variables on DOM migration and transformation, and quantifying direct contributions and indirect contributions of different pollution sources to each fluorescent component through path coefficients; and calculating the total contribution of lake-entering rivers, bottom mud release and phytoplankton to the COD of the lake water body.

The invention discloses a potential landslide identification method and system based on surface deformation cause analysis. The method comprises the following steps: acquiring radar data and preprocessing the radar data to obtain surface deformation phase data; an IDANet-1 network is constructed and trained, and an earth surface deformation abnormal area is obtained through prediction; respectively processing the terrain DEM data, the multi-temporal MODIS data and the optical image data to obtain ground surface settlement type, glacier snow melting type and mine collapse type ground surface deformation areas, performing union set operation, and performing difference set operation on the union set operation and a ground surface deformation abnormal area to obtain a landslide type ground surface deformation target; training an IDANet-2 network by using the landslide type surface deformation identification data set; and inputting the wide-area comprehensive remote sensing data into the IDANet-2 network model for reasoning to obtain wide-area potential landslide hidden danger distribution information. Through the technical scheme of the invention, multi-level combination of multi-source and multi-mode remote sensing data is realized, accurate identification of earth surface deformation information is realized, and the identification efficiency of large-area potential landslide hidden dangers is effectively improved.

The invention discloses a bolt loosening automatic acquisition and analysis method and equipment based on a 5G sensing network, and relates to the technical field of bolt state intelligent acquisition. The bolt loosening automatic acquisition and analysis method and device based on the 5G sensing network comprises the steps that S1, multi-source data in the bolt use process is acquired and preprocessed, and a standardized bolt channel state data set is constructed; s2, evaluating the state of a bolt loosening evolution stage in combination with an angular displacement change trend, and driving the sampling frequency to be dynamically switched; s3, analyzing the response intensity change of the three types of channels, evaluating the channel fusion state, and adjusting the monitoring priority; s4, comprehensively evaluating a response offset trend, and recombining monitoring resources; and S5, constructing an attribution process based on the fusion state value and the high-risk transition value, and generating an evolution link structure. The problem that a high-risk section cannot be recognized in advance due to the fact that a multi-stage multi-channel driving factor analysis and cross-time-domain expression mechanism lacks in the evolution process of bolt loosening is solved.

The invention relates to an urban rail transit passenger flow prediction method based on a multi-factor algorithm. The method comprises the following steps: collecting urban rail transit historical passenger flow data and related index factor sample data influencing passenger flow; constructing a factor analysis mathematical model, analyzing a plurality of index factors influencing the passenger flow, determining a common factor, and calculating a score; constructing a BP neural network model, taking the common factor score as input, and obtaining an optimal parameter set through a particle swarm optimization algorithm; generating a BP neural network prediction model for urban rail transit passenger flow prediction based on the optimal parameter set, and training through sample data to obtain an optimal BP neural network prediction model; and performing real-time passenger flow prediction based on the trained BP neural network prediction model. According to the method, the problems of insufficient prediction precision, low calculation efficiency and the like in the prior art can be solved, and the purpose of rapidly and accurately predicting the passenger flow of the station, the line and the line network is achieved.

The invention discloses an ozone concentration forecasting method based on peak value perception and influence factor analysis, which utilizes continuous online observation data and meteorological observation data of multi-site pollutants, processes abnormal and missing data, and combines with boundary layer height, radiation and sea level air pressure provided by an ECMWF meteorological forecasting product to forecast ozone concentration. Building a high-quality Yunnan region large database for supporting deep learning according to hour-scale meteorological numerical simulation data of vertical wind speed, and building an ozone forecasting model based on influence factor analysis and peak value perception; capturing a global dependency relationship among historical meteorology, pollutant data and an ozone long-time sequence and a nonlinear relationship between ozone and an influence factor, so that the model can better understand context information and a related mechanism influencing ozone generation; the defect of long-time sequence dependence of a traditional deep learning method is overcome, the influence of influence factors on ozone concentration prediction is ignored, and the accuracy of ozone prediction is improved.

The invention discloses an intelligent fire-fighting risk prediction and dynamic early warning method and system based on multi-source data fusion, and relates to the technical field of intelligent fire-fighting risk prediction, and the method comprises the steps: collecting multi-source fire-fighting data, and constructing a fire-fighting safety state multi-dimensional data matrix; constructing a risk prediction model based on the fire safety state multi-dimensional data matrix to carry out real-time risk scoring; and executing dynamic early warning through a risk prediction result. According to the method, through the multi-source data acquisition and fusion module, multi-type fire-fighting data are comprehensively acquired and standardized, a structured and multi-dimensional fire-fighting safety state matrix is constructed, and the problems of data dispersion and information splitting are solved. In combination with a risk collaborative prediction and scoring module, the risk level of each region is dynamically output based on multi-factor analysis, and the accuracy and foresight of risk prediction are improved. Through a dynamic early warning generation and linkage module, real-time grading early warning and linkage of a fire fighting system are realized, and rapid response and intelligent prevention and control of a high-risk area are realized.

The invention discloses a case factor intelligent analysis and processing method based on intelligent semantic understanding, and belongs to the field of artificial intelligence, natural language processing and public security judicial informationization, and the method comprises the following steps: 1, preprocessing public security judicial document information, and supporting text analysis, format standardization and structured data conversion; 2, carrying out refined extraction on case elements based on a large language model, and identifying case constituent elements, hazard degrees and processing results; 3, fusing logical reasoning of a public security judicial knowledge graph, and enhancing cross-cause semantic understanding and reasoning accuracy; and 4, performing intelligent auxiliary processing, generating class case recommendation and penalty interval prediction, and performing interactive regulation question and answer. According to the method, efficient, accurate and automatic case factor analysis is successfully realized, and the public security judicial document processing efficiency and decision accuracy are remarkably improved.

The invention discloses a method for optimizing pressing rivet quality based on grey correlation and a factor analysis method, and relates to the technical field of pressing rivet quality optimization. Firstly, a taguchi method is adopted, a press riveting quality test model with busbar design parameters and control parameters of an automatic squeeze riveter as design optimization variables and with push-out force and twisting force as responses is constructed, a grey correlation method is adopted, the correlation degree in all responses is analyzed, dimension reduction is conducted on multiple indexes in combination with a factor analysis method, weights of different indexes are obtained, and a press riveting quality test model is obtained. Reducing multiple indexes into a single index, then calculating a weighted grey correlation degree, carrying out comprehensive evaluation, finally obtaining an optimal parameter level combination, and analyzing a factor with the maximum influence degree on the indexes. According to the method, the factors which have the most influence on the pressing rivet quality can be quickly selected from the numerous parameters, and when the pressing rivet quality is evaluated by the push-out force and the twisting force in the pressing rivet quality evaluation indexes, the problems of information overlapping of multiple evaluation indexes and the like can be solved.

The invention belongs to the technical field of crop growth prediction, and particularly relates to a crop growth prediction method and system based on multispectral unmanned aerial vehicle monitoring. The method comprises the following steps: acquiring multi-dimensional data of real crops in different growth stages under different soil water contents and disease and insect pest states, determining the contribution degree of each vegetation index to crop growth through a factor analysis algorithm, carrying out dimension reduction on hyperspectral data by using a principal component analysis algorithm, fusing with the multi-spectral data, constructing a multi-spectral resolution characteristic space, and carrying out multi-spectral analysis on the hyperspectral data. The method comprises the following steps: firstly, obtaining a real plant height growth fitting function of crops through analogue simulation by combining LiDAR data and vegetation indexes, thirdly, calculating a real growth vegetation index space and obtaining a real growth state space of a standard staged growth period, and finally, inputting the calculated space and function into a model constructed by a reinforcement learning algorithm for training, and accurate prediction of the crop growth state is realized.

The invention discloses a treatment response prediction method and system, a storage medium and a terminal, and the method comprises the steps: carrying out the heterogeneity analysis and cell division of first single cell sequencing data of a patient through employing a clustering algorithm, and obtaining a plurality of corresponding cell subgroups; performing feature extraction and biomarker recognition on all the cell subgroups to obtain corresponding cell subgroup features; wherein the cell subset characteristics comprise a cell development trajectory and a plurality of cell markers; and inputting the clinical information and the cell subset characteristics of the patient into the treatment response prediction model, so that the model performs multi-factor analysis and outputs a treatment response prediction result of the patient for each treatment scheme. By adopting the embodiment of the invention, the tiny key difference between the cells is captured, the cells are divided based on the difference between the cells, and then feature extraction and biomarker recognition are performed on each cell subset, so that the specific gene expression modes of different cell subpopulations corresponding to diseases can be recognized, and the treatment response prediction accuracy is further improved.

The invention relates to the technical field of flight ability evaluation, in particular to a pilot emergency processing ability evaluation method and system fused with physiological signals. Initial ability indexes are extracted based on literature research and physical examination standards, and physiological data of a pilot in a normal state and a simulated emergency state are collected; the method comprises the following steps: extracting potential ability indexes through dimensionality reduction and denoising of a component analysis algorithm and factor analysis (FA), and screening remarkably changing physiological indexes (plt; 0.05), and performing correlation combination with the initial index (a correlation coefficient gt; 0.8) constructing an emergency capability evaluation index system including situation awareness, decision making, communication, pressure management, team cooperation, reaction time, strain and flexibility; the pilot emergency ability assessment method and system are constructed through multi-source physiological signal fusion and dynamic modeling, accurate quantitative analysis and personalized training guidance are achieved, and civil aviation safety specifications are adapted.

The invention discloses a brand propagation index construction system, and particularly relates to the technical field of brand analysis. A data processing system; an analysis model system; a visualization system; a feedback optimization system; and dynamically updating the system. By constructing a full-link intelligent closed-loop system, the problems of data splitting and strategy lagging in traditional brand propagation analysis are solved, the system integrates cross-platform data sources to achieve efficient fusion of multi-source heterogeneous data, the limitation of data islands is broken through, the collaborative application of dynamic factor analysis and generative adversarial networks is achieved, and the analysis efficiency is improved. The system can perceive propagation scene changes in real time, dynamically adjust data weights and model parameters and ensure that analysis results are adaptively optimized along with market situations, meanwhile, a linkage mechanism of an A / B test engine and a genetic algorithm enables strategy recommendation, parameter optimization and version iteration to be automatic, decision response time is remarkably shortened to the minute level, and the market development efficiency is improved. And real-time and accurate decision support is provided for brand management.

The invention provides a method and a system for evaluating the biological health condition of cultivated land soil based on exploratory factor analysis, and relates to the technical field of evaluation of the biological health condition of soil. The precise analysis of the complex interaction relationship between the soil microorganism function and the environmental factor is realized; through multi-dimensional index fusion and deep mining of a factor model, key biological function factors are effectively discriminated and quantified, and the scientific evaluation capability of soil ecological functions is improved; in combination with causal path analysis, the scheme can reveal the influence of environmental pressure on a microbial regulation and control mechanism, and intelligent attribution and traceability of abnormal health conditions are realized; based on dynamic fusion and grading judgment of function and environment scores, the scheme supports accurate health condition evaluation and hierarchical intervention, the early warning capability and decision scientificity of soil management are enhanced, and the agricultural sustainable development guarantee level is remarkably improved.

The invention discloses a water conservancy data acquisition device, and relates to the technical field of Internet of Things, and the device comprises the following steps: dividing a target water area into a plurality of monitoring water areas; performing characteristic factor analysis on each monitored water area to obtain a characteristic factor analysis result; adjusting the installation number and positions of the sensors in the monitored water area according to the characteristic factor analysis result, and obtaining hydrological data monitored in real time; performing edge calculation analysis on the hydrological data monitored in real time to obtain an edge analysis result; performing data synchronization on the edge analysis result and uploading the edge analysis result to a cloud platform to obtain cloud data; performing intelligent analysis on the cloud data to obtain a water area dynamic report; performing risk early warning according to the water area dynamic report; the acquisition device collects and analyzes hydrological data of different water areas by combining real-time data acquisition of a sensor and an edge calculation and analysis method. According to the method, loss and errors of hydrological data are reduced, and consistency, high efficiency, accuracy and timeliness of the hydrological data are enhanced.

The invention provides an abutment tooth selection multi-element analysis method based on improved YOLOv11. Comprising the following steps: preprocessing an original desensitized tooth panoramic x-ray film, constructing and marking a self-made data set, and carrying out target detection and tooth key point detection; in the target detection, a dynamic hybrid convolutional network (Dynamic Inception Mixer) and a small object enhanced pyramid (SOEP) are introduced into an original model, and the feature extraction fusion capability is enhanced; a frequency domain-space attention mechanism (FSA) and a ShapeIoU loss function are integrated, the small target recognition and bounding box regression precision is improved, and a DSF-YOLOv11s-detect model is generated. In key point detection, a high-frequency enhanced residual block (HFERB) and a multi-scale attention mechanism (MSGA) are introduced, so that the positioning accuracy is improved, an SIoU loss function is adopted to enhance the sensitivity to tooth direction features, and an HMS-YOLOv11s-pose model is generated. Experiments show that the improved model is better in recognition and positioning effect. The abutment tooth selection multi-element analysis method integrates two models, solves the problems of low minimal lesion recognition rate and insufficient multi-element comprehensive analysis ability of an original model, and promotes precise and intelligent auxiliary decision making of the dental department.

The invention provides an atmospheric fine particulate matter PM2.5 influence factor analysis method and device, and belongs to the technical field of data processing, and the method comprises the steps: obtaining multi-source heterogeneous data of a target region in a target time period, the multi-source heterogeneous data comprising spatial distribution data of atmospheric fine particulate matter PM2.5 concentration, natural environment data and social economic activity data; preprocessing the multi-source heterogeneous data to obtain grid data with a preset temporal-spatial resolution, the grid data including grid data of PM2.5 concentration and grid data of each influence factor of PM2.5; and inputting the grid data of the PM2.5 concentration and the grid data of each influence factor into a pre-trained influence factor analysis model to obtain a spatial distribution pattern of each influence factor output by the influence factor analysis model. According to the method, the nonlinear-dynamic effect of multiple factors can be fully considered, each influence factor is quantitatively analyzed, and the precision of PM2.5 influence factor analysis is improved.

The invention discloses a battery carbon emission influence factor analysis method and device and a storage medium, and belongs to the technical field of data processing. Comprising the following steps: acquiring life cycle data of a battery at the current moment from a data source, constructing a knowledge fusion atlas database of the battery based on the life cycle data through an atlas construction model, generating a carbon emission prediction model according to the knowledge fusion atlas database, and predicting the carbon emission through a feature contribution degree analysis algorithm. Calculating a prediction contribution degree of each factor variable in the carbon emission prediction model, determining a target factor variable according to the prediction contribution degree, constructing a directed acyclic graph of the target factor variable based on a preset electrochemical constraint condition, and calculating conditional probability distribution of the electrochemical constraint condition in the directed acyclic graph, and forming a coupling relation model of the target factor variables, and calculating the sensitivity coefficient of the factor variables in the coupling relation model to the carbon emission. According to the method, the accuracy of the analysis result is improved by quantifying the sensitivity coefficient of each carbon emission factor under the coupling relationship.

The invention discloses a mixing parameter regulation and control method and system for an SBS-T modified asphalt mixture and a terminal. The method comprises the following steps: analyzing a modification mechanism through a scanning electron microscope and X-ray photoelectron spectroscopy combined technology, dividing three stages of rapid melting and the like, and establishing a temperature-viscosity model; constructing a multi-scale capture system by using an atomic force microscope, a Fourier transform infrared spectrum and a dynamic shear rheometer; designing a five-factor three-level test matrix by adopting a response surface method, and establishing a road performance prediction model in combination with a BP neural network; mixing parameters are optimized in a multi-objective mode based on a genetic algorithm, and dynamic correction is achieved through an Internet of Things sensor and Kalman filtering. The system comprises a mechanism analysis module, a multi-scale detection module, a terminal integrated storage unit, a processing unit and a man-machine interaction unit. According to the scheme, the limitation of traditional single-factor analysis is broken through, microscopic and macroscopic collaborative optimization and dynamic parameter regulation and control are achieved, and the construction adaptability and precision are improved.

The invention relates to the technical field of human factor engineering and intelligent evaluation, in particular to an interruption response capability evaluation data processing method and system based on response time characteristics and task modeling. The method comprises the steps of constructing an experimental process scheduling structure comprising a main task and an interrupt task, collecting and constructing a complete behavior data stream, extracting time characteristics in a task switching and recovery process, executing standardization and disturbance labeling, performing factor analysis in combination with a task label, and generating a capability factor parameter set. And then matching a scoring template to calculate an individual interruption response capability score, generating a grade label according to the score, and completing task capability adaptation and recommendation. According to the method, the structured, quantitative and closed-loop feedback of capability evaluation is realized, and the evaluation accuracy and the task matching intelligent level are improved.

The invention relates to a cold region wetland water quality risk factor screening method, and belongs to the technical field of water ecological water quality screening. Establishing a water quality monitoring data file; the water quality screening calculation comprises the steps of data file selection and identification, seasonal screening calculation, data file establishment, principal component scoring, main factor direct calculation score, non-rotation factor sample direct scoring, variance maximum orthogonal rotation, factor post-rotation scoring, Bartlett inspection, risk factor probability screening and conjecturing of over-standard elements in each month of the next year. The method has the advantages that the problem that risk factors cannot be quantitatively analyzed in a factor analysis method can be solved, and identification and analysis can be independently carried out in different seasons. According to a method for performing factor analysis and probability calculation according to seasons, the water quality risk factors are screened by combining factor analysis scores with probable probabilities.