41 results about "Analysis factors" patented technology

The invention discloses a nowcasting method and system of a thunder cloud cluster based on the boundary recognition and tracer technique. The method performs the cloud cluster boundary recognition and topology process to establish the relationship between cloud cluster life timing and clan pedigree by using the pattern recognition technique. The method comprises the steps of performing the boundary recognition on the pre-processed radar data; respectively recognizing the life timing of each cloud cluster and the information of moving direction, velocity, area and strong center and the like ofeach cloud cluster through the six judging factors of the quadtree matching analysis factor, the overlap factor, the area factor, the circum-rectangle factor, the outline comprehensive factor and thelocal similarity determination factor; and performing linear extrapolation on the moving direction, velocity, area and the strength of the cloud cluster. The preliminary result indicates that the system and the method of the invention can realize better recognition and extrapolation forecast of the thunder cloud cluster.

Methods, machine-readable media, and devices for face image prioritization based on face quality analysis are described herein. For example, one or more embodiments include detecting a facial image in an image that has been acquired by a camera that monitors a scene, passing the facial image through a number of quality analysis filters that include a number of quality analysis factors, wherein processing complexity associated with the number of quality analysis factors increases consecutively, and submitting the facial image to a facial recognition program upon a determination that the facial image has passed the number of quality analysis filters.

The invention discloses a dynamic screening and comprehensive weight setting method for low-voltage transformer area line loss rate analysis indexes, including the following steps of: collecting and summarizing low-voltage transformer area line loss rate analysis indexes, and establishing a first-level index system of a low-voltage transformer area line loss rate; dynamically screening the first-level index system by a principal component analysis, screening out a low-dimensionality second-level index system of the low-voltage transformer area line loss rate, using the low-voltage transformer area line loss rate as a third-level index, and thereby obtaining the dynamic index system, including three levels, of the low-voltage transformer area line loss rate; working out the comprehensive weight of the second-level indexes to the third-level indexes in the dynamic index system by a step analysis-factor analysis method, and assessing the contribution rate of each second-level index to the low-voltage transformer area line loss rate according to the obtain comprehensive weight. According to the dynamic screening and comprehensive weight setting method provided by the invention, on the one hand, a reference base is provided for analyzing the low-voltage transformer area line loss rate by a power supply enterprise; in addition, the contribution rates of the indexes to the line loss rate are assessed, thus being conductive to assisting the power supply enterprise to perform targeted management and transformation to the low-voltage transformer area line loss rate.

The invention provides an on-demand computing-based data analysis device and method for analysis factors. The on-demand computing-based data analysis device for the analysis factors comprises a configuration module, a data extraction module, an analysis factor computing module, a data analysis processing module and an analysis result output module. The on-demand computing-based data analysis device and method for the analysis factors, which are disclosed by the invention, have the advantages that the analysis factors and analysis rules are easy to expand, the timeliness and the accuracy can be guaranteed, the analysis factors can be computed on demand, and thereby the work efficiency and the performance of a system are markedly improved.

The invention provides a fatigue detection method. The fatigue detection method includes: continuously acquiring face images of a personnel, and extracting characteristic point information of eyes and the mouth; respectively calculating closing proportions of the right eye and the left eye and the opening time of the mouth; and determining whether the calculated closing proportions of the right eye and the left eye are greater than a preset closing proportion threshold and determining whether the calculated opening time of the mouth is greater than a preset opening time threshold, determining the fact that the personnel is not in a fatigue state if the closing proportions of the right eye and the left eye and the opening time of the mouth are both not greater than the corresponding closing proportion threshold and the opening time threshold, and determining the fact that the personnel is in a fatigue state if one or more of the closing proportions of the right eye and the left eye and the opening time of the mouth is greater than the corresponding closing proportion threshold and the opening time threshold. According to the fatigue detection method, analysis factors are comprehensive, and the identification accuracy is high.

The invention discloses a model building system of a concrete continuous box girder bridge. The model building system comprises the specific steps that 1, comparison and selection are conducted on a bridge type building scheme, and a bridge construction scheme is drafted; step 2, drawing up the size of the upper structure of the bridge; 3, establishing a space finite element model of the bridge structure by applying midas civil software; 4, performing curve continuous box girder counter-force checking calculation, and continuously adjusting the structure size or reinforcing bars until all checking calculations meet the standard; 5, structural internal force calculation and internal force combination are carried out; the invention relates to the technical field of bridge construction. According to the model building system of the concrete continuous box girder bridge, the problems that an existing data simulation system is too tedious in process, large in error and incomplete in analysis factor are solved.

The invention provides a method for quickly identifying factors for analyzing a target pollutant source. The method comprises the following steps: collecting published documents related to the targetpollutant source analysis in contents; extracting graphs and data related to sources corresponding to target pollutant source analysis factors from the published documents, and constructing a trainingset and a test set for training learning to obtain a preliminary training model; verifying the training model by adopting a cross validation method based on an artificial neural network to obtain a final factor recognition model; analyzing a to-be-detected sample, and calculating unrecognized factors; calculating the unrecognized factors by using a factor recognition model, and analyzing a pollutant source corresponding to the factors. According to the method for quickly identifying PMF factors, a source corresponding to each factor is automatically identified by an artificial neural networkdeep learning algorithm, so that the factor identification speed and the factor identification accuracy are greatly improved. Meanwhile, the subjectivity of manual selection is avoided.

The present invention provides an enterprise credit risk assessment method and an apparatus. The method comprises the steps of extracting an objective factor out of an objective circumstance survey report of a to-be-trusted enterprise, and adopting the objective factor of the objective circumstance survey report as a first objective factor; inputting the first objective factor into each pre-stored risk analysis model, and outputting a first analysis factor corresponding to each risk analysis model; inputting the first objective factor and the first analysis factor into each pre-stored risk judgment model and outputting a first conclusion factor corresponding to each risk judgment model; executing the operation till the outputted first conclusion factor does not change at all. The above operation comprises the steps of inputting the first objective factor, the first analysis factor and the first conclusion factor of the last output result into each pre-stored risk judgment model, outputting the first conclusion factor corresponding to each risk judgment model, and combining the first objective factor, the first analysis factor and all first conclusion factors to form a credit risk view.

The invention provides a data analysis device and a data analysis method based on dynamically expandable analysis factors. The data analysis device based on the dynamically expandable analysis factors comprises a configuration module, a data extraction module, an analysis factor calculation module, a data analysis and processing module and an analysis result output module. By the aid of the data analysis device and the data analysis method based on the dynamically expandable analysis factors, expansion is easy, instantaneity and accuracy can be guaranteed, and systematic operating efficiency and performance can be enhanced.

The invention discloses a method and a device for analyzing a reasonable value of a line loss benchmark of a power distribution network. The method comprises the steps of determining line loss data sample sets of a plurality of analysis dimensions based on equipment parameter information and theoretical line loss result data required by theoretical line loss of a power distribution network; performing system clustering analysis on the line loss data sample set under different analysis dimensions to form a reference clustering value scale; analyzing the line loss data sample set under differentanalysis dimensions by adopting a preset clustering algorithm based on the reference clustering value scale so as to select a target clustering value; determining a plurality of line loss reference domains based on the target clustering value, each line loss reference domain comprising a plurality of sub-analysis factors; and determining a line loss benchmark reasonable value corresponding to each analysis dimension by utilizing the data analysis decision tree and the plurality of line loss reference domains.

Systems and methods for additive manufacturing of vehicles are provided. An additive manufacturing apparatus can include a printer that additively manufactures structures for a vehicle, and multiple analysis components. Each analysis component can receive information based on a design model of the vehicle and analyze the information based on an analysis factor. Each analysis component analyzes theinformation based on a different analysis factor. An integrator can receives the analyzed information from the analysis components, update the design model based on the analyzed information, and determine whether the updated design model satisfies criteria. If the updated design model satisfies the criteria, the integrator determines printing instructions for the printer to print one or more structures of the vehicle based on the updated design model, and if the updated design model does not satisfy the criteria, the integrator sends information based on the updated design model to the analysis components.