20 results about "Structural index" patented technology

The invention relates to the technical field of early warning decision making, in particular to an agricultural disaster early warning decision making method and system, and the method comprises the following steps: obtaining a structure parameter set through a multispectral sensor, carrying out the smooth processing of a sliding window, marking the direction consistency, inputting a hidden Markov model, calculating the transition probability, recognizing a stage trigger point, and outputting a label. And matching a physiological and meteorological time sequence to generate a response mapping table, extracting a trend overlapping section mark impact window, judging a disaster grade in combination with multi-factor weighted sorting, and outputting an early warning instruction. According to the invention, through continuous observation of growth parameters and smooth processing of time sequence data, analysis of dynamic consistency of structural indexes, identification of growth mutation, alignment of physiological and meteorological factors, extraction of trend overlapping sensitive areas, and superposition of residual errors to lock risk time windows, the sensitivity and pertinence of disaster early warning are improved, and the risk of misinformation and missing report is reduced. Fine data support is provided for management, and risk management and control scientificity and initiative are enhanced.

The invention discloses a water conservancy project building full life cycle management method based on BIM technical analysis, and relates to the technical field of building full life cycle management. And respectively constructing three structural indexes, namely a microcrack expansion rate index F1, a damp-heat permeation combined degradation index F2 and a dynamic stiffness phase deviation index F3. The multi-dimensional feature system not only covers key risk sources such as material microcosmic degradation, environmental coupling effect and mechanical response mutation, but also breaks through a traditional extensive method which depends on single sensing data to perform health judgment, and provides a digital expression model with physical interpretation force for structural risks. According to the method, a unified health data standard system is established, and the health data standard system can be used as a core data source of upper-layer applications such as dynamic structure evaluation, an early warning model and visual mapping.

The invention relates to the technical field of intelligent operation and maintenance, in particular to a nuclear energy operation and maintenance optimization method based on atlas driving, which comprises the following steps: acquiring equipment data and maintenance records, marking state matching, screening a maintenance strategy and combining semantic reasoning to extract a fault path, and classifying deviations to generate adaptability labels. Uninterfered tasks are screened, work order mapping distribution is extracted, execution imbalance is judged, resource and task ratio recording period differences are calculated, and operation and maintenance progress monitoring structure indexes are generated. According to the method, the mapping relation between equipment and working conditions is constructed through operation data and maintenance records, accurate evaluation of the health state is achieved, the fault path and deviation degree are recognized in combination with semantic reasoning, the adaptability of a maintenance strategy and the working conditions is improved, task interference risks are avoided, load fluctuation is recognized by means of work order execution data, and resource and task quantity comparison is combined. And a resource investment benefit evaluation mechanism is constructed, so that the fault prediction accuracy, the task execution coordination and the resource configuration rationality are improved.

The invention discloses a multi-agent cooperation performance evaluation method and a medium. The method comprises the following steps: determining a task structure index according to task splitting information and task scheduling execution information; performing time sequence analysis and behavior pattern recognition according to the communication information among the intelligent agents to determine a cooperative communication index; determining a task consistency index according to the sub-task execution information and the main task execution information of each agent; determining a time resource efficiency index according to the task execution time information of each agent and the execution capability information of each agent; and determining an evaluation result of the multi-agent cooperation performance according to the task structure index, the cooperative communication index, the task consistency index and the time resource efficiency index. According to the invention, evaluation is carried out from multiple dimensions according to related information of multi-agent cooperation, the comprehensiveness of the evaluation result is improved, and the accuracy of the evaluation result is further improved.

The invention relates to the technical field of environment monitoring, in particular to a seawall ecologicalization real-time monitoring method based on multi-source data fusion. The method comprises the following steps: acquiring structure monitoring data, ecological monitoring data and water quality monitoring data of a seawall through an Internet of Things sensor, and acquiring remote sensing data of a seawall area in combination with satellite remote sensing and an unmanned aerial vehicle image; performing space-time registration and fusion on different data sources to generate a unified multi-source fusion data set; identifying a red tide abnormal event based on the fused data set, and constructing a red tide influence conduction model for predicting the damage degree of the red tide to the ecological system and the influence of the red tide to the stability of the embankment body; establishing a coupling evaluation model of ecological indexes and structural indexes, calculating a coupling coefficient by using a fuzzy comprehensive evaluation method, dynamically adjusting ecological and structural weights according to a model output result, and calculating a seawall comprehensive health index; graded early warning information is automatically generated according to a preset threshold value, and collaborative monitoring and intelligent early warning of seawall ecology and structure safety are achieved.

The invention relates to the technical field of oil and gas field development, in particular to a stratified shale oil reservoir fracture forming capacity evaluation method based on logging data, which comprises the following steps: acquiring horizontal principal stress data, Young modulus, Poisson's ratio, longitudinal wave velocity and transverse wave velocity of a stratified shale oil reservoir; determining a crustal stress index of the stratigraphic shale oil reservoir according to the horizontal principal stress data; determining the brittleness index of the stratified shale oil reservoir according to the Young modulus and the Poisson's ratio; determining the structural index of the stratified shale oil reservoir according to the longitudinal wave velocity and the transverse wave velocity; and according to the crustal stress index, the brittleness index and the structure index, determining the fracture forming ability index of the stratified shale oil reservoir. According to the method for evaluating the fracture forming capacity of the stratified shale oil reservoir, a theoretical basis is provided for shale oil reservoir dessert optimization, segment cluster combination matching and construction process parameter optimization, and accurate transformation and efficient development of the shale oil reservoir are guided.

The invention discloses an AR-GIS fusion-based underground pipeline three-dimensional visualization and real-time sensing system, and relates to the field of data analysis, and the system comprises an index definition module which is used for presetting a plurality of structure indexes and attribute indexes required by underground pipeline construction; the extraction and evaluation module is used for presetting indexes according to the index definition module, extracting associated data from the existing GIS image and the acquired data source, automatically evaluating the completion degree of data extraction, and identifying and marking missing points which are not obtained; the model construction module is used for comparing the data completion degree calculated by the extraction and evaluation module with a preset completion threshold value; three-dimensional visualization and real-time perception of the underground pipeline are achieved, data integrity and accuracy are remarkably improved by automatically evaluating data completeness and intelligently predicting missing point data, user interactive abnormal marking and dynamic region division are supported, accurate data supplementary collection is guided, and efficiency and safety of management and maintenance of the underground pipeline are improved.

The invention discloses a coal-bearing stratum three-property and mining design linkage anti-impact and seismic control method, which belongs to the technical field of coal mining, and specifically comprises the following steps: S1, collecting data, and classifying the collected data according to the physical property, the structural property and the occurrence, performing normalization processing on the classified data to obtain a plurality of corresponding physical indexes, structural indexes and occurrence indexes, performing multi-field coupling monitoring on the target mining area to obtain a plurality of dynamic monitoring coefficients, and performing weighted fusion on the plurality of physical indexes, structural indexes, occurrence indexes and dynamic monitoring coefficients to obtain a risk index R; dividing the mine area into a plurality of dangerous zones according to the R value; s2, formulating a corresponding three-property joint debugging strategy and a mining linkage scheme according to the dangerous zone; s3, three-property joint debugging operation is organized, and mining operation is conducted; the method can comprehensively and objectively characterize the mechanical characteristics, geologic structure characteristics and stress occurrence states of the coal and rock mass, and realizes accurate early warning grading of the mine area.

The invention relates to the field of intelligent structure monitoring, and discloses an intelligent building measurement system, which comprises the steps of collecting structure stress, deformation, micro-seismic response, material attenuation and environmental load curves of a building in different life cycle stages, generating a real-time structure degradation fingerprint vector, and calculating a real-time structure degradation fingerprint vector; the method comprises the following steps: identifying load, vibration, temperature anomaly and basic displacement from an extreme environment and an operation scene library, generating a local response sequence, combining the local response sequence with a real-time structure degradation fingerprint vector, generating a key load transmission topology, selecting a key path and nodes to form a local response event sequence, calculating structure offset and degradation, and generating a key load transmission topology. And generating an adaptive correction structure index set, constructing a long-period degradation track and performing trend prediction, and when a prediction index exceeds a safety threshold, generating a structure safety intervention scheme, a dynamic measurement optimization scheme and a data restoration scheme. The method has the advantage of improving the accuracy and safety of building structure state evaluation.

The invention provides a deep learning-based slope vegetation protection effect evaluation method and device fusing multi-modal features and a storage medium, and relates to the field of slope protection evaluation, and the method comprises the steps: obtaining a structure index, a time sequence index and an image index of a target slope; constructing an initial structure vector, an index value sequence, an index position sequence and an index time interval sequence; performing three-color channel combination on the land cover image and the root system vertical distribution pattern image to obtain an initial image tensor; inputting the initial structure vector, the index value sequence, the index position sequence, the index time interval sequence and the initial image tensor into a slope index scoring model to obtain a stability score and an ecological function score; and obtaining a slope vegetation protection effect evaluation index based on the stability score and the ecological function score, wherein the higher the evaluation index value is, the better the slope vegetation protection effect is. By adopting the evaluation method provided by the invention, rapid and efficient slope vegetation protection effect evaluation can be realized.

The invention provides an evaluation method of polyethylene powder for chemical modification. The evaluation method comprises the following steps: S1, obtaining a performance test result of a to-be-tested sample according to polyethylene evaluation indexes; wherein the polyethylene evaluation indexes comprise a basic index, a fluidity index, a uniformity index, a structure index and a processing performance index; s2, on the basis of the performance test result of the to-be-tested sample, determining the score of the to-be-tested sample in each evaluation index according to the evaluation standard corresponding to each evaluation index; s3, performing summation calculation on the scores of the to-be-tested sample in each evaluation index to obtain a total score; and S4, evaluating the comprehensive performance of polyethylene according to the total score. By comprehensively evaluating and judging the use performance of the polyethylene powder in the polyethylene application field, compared with the prior art, the problem that common product indexes are insufficient to completely reveal the use performance of the polyethylene powder can be avoided.

The application discloses a severe patient acute lung injury evaluation system and method based on machine learning, relates to the field of acute lung injury evaluation, and comprises the following steps: acquiring chest multi-modal image data of a severe patient and performing standardized processing to generate a standard data set; dividing a lung region into structural blocks and extracting structural indexes, compensating for missing data through cross-modal space mapping or candidate block selection; identifying a modal conflict region and a structural mutation region to construct a scoring key region; after applying disturbance to screen an effective region, selecting a mean value or a weighted fusion strategy according to a consistency index; and combining dynamic weight to calculate a lung injury score and performing iterative optimization; the application can improve the identification accuracy of a key region of acute lung injury, and realizes accurate quantitative evaluation of the degree of lung injury.

The invention provides a national oil and gas resource condition investigation and evaluation method based on public welfare data. The method comprises the following steps: (1) collecting resource quantity, reserves, yield, quality, structure and spatial distribution data of oil and gas resources; (2) establishing a quantity index feature assignment template; (3) establishing a quality index feature assignment template; (4) establishing a structure index feature assignment template; (5) establishing a spatial index feature assignment template; and (6) determining a national oil and gas resource favorable potential area. The method is reasonable in conception, can meet the assignment requirements of oil and gas resource quantity indexes, the assignment requirements of oil and gas resource quality indexes, the value requirements of oil and gas resource structure indexes and the value requirements of different types of oil and gas resource space indexes, and is suitable for popularization and application.

This invention discloses a morphology-based method and system for classifying pores in tight reservoir porous media, comprising: acquiring microstructural images of rock tight reservoir samples using field emission scanning electron microscopy; labeling the SEM images with two types of samples, pores and matrix / background, as training sets, and training a segmentation model for rock samples; segmenting the sample SEM images using the trained segmentation model to generate binary images that identify the background and pores; automatically measuring pore morphology parameters based on the binary images to obtain pore roundness and aspect ratio; classifying pore shapes according to preset quantitative classification thresholds for pore roundness and aspect ratio, and outputting statistical classification results; this invention achieves a systematic and objective characterization of pore cross-sectional shape, transforming the traditional subjective judgment-dependent pore morphology description into a quantifiable and comparable structural index, solving the problem that traditional pore morphology classification relies on subjective judgment and lacks unified quantitative standards.

The application relates to the technical field of data processing, and discloses a large-span steel structure index prediction method based on Revit, which comprises the following steps: analyzing a target steel structure three-dimensional building information model, and extracting geometric entities and material attribute information; obtaining a key stress component set through structure topology analysis; performing characteristic engineering reconstruction on the components in the key stress component set to obtain cross-section characteristic parameters, connection node types and spatial positioning information; constructing a component relationship topology graph according to the connection node types and the spatial positioning information; parameterizing and packaging the component relationship topology graph, the cross-section characteristic parameters and the material attribute information to obtain a structured characteristic spectrum; correlating and mining the structured characteristic spectrum with a historical engineering case characteristic spectrum to obtain a performance index prediction conclusion, and mapping the conclusion to the corresponding components of the model and visually marking the conclusion; and the application can improve the efficiency of large-span steel structure index prediction based on Revit.

The invention discloses an intertidal zone wetland lateral hydrological communication structure index construction method, and relates to the technical field of wetland ecological hydrological communication, and the intertidal zone wetland lateral hydrological communication structure index construction method comprises the following steps: arranging uniform sampling points in an intertidal zone, and collecting structure index data; performing Z score centralization processing on the structure index data; and acquiring coefficients of the structure index on the first two axes by using a principal component analysis method, constructing a comprehensive hydrological connectivity index, and determining the hydrological connectivity. According to the method, the structure indexes playing an important role in tidal water movement are screened, the comprehensive hydrological connectivity index based on the tidal flat structure indexes is constructed, the hydrological connectivity of different positions of the surface of the intertidal wetland is quantified, and a grasp is provided for repairing, protecting and managing the coastal wetland.

The invention discloses a formula optimization method and device of a liquid impregnant for a power capacitor, equipment and a medium, and relates to the technical field of computers. Comprising the following steps: determining basic structure parameters based on molecular structure data of impregnant molecules, and calculating structure indexes for representing an inspiration mechanism based on the basic structure parameters; constructing an initial model based on the structure indexes, and adjusting weight parameters of the model to obtain an inspiration scoring model; the inspiration scoring model comprises a first-layer model for outputting an inspiration active site score and a second-layer model for outputting a structural inspiration score; determining an inspiration score of the to-be-evaluated impregnant molecule based on the structure index of the to-be-evaluated impregnant molecule by using an inspiration scoring model; and determining the inspiration performance of the to-be-evaluated impregnant molecule based on the inspiration score, and optimizing the formula of the to-be-evaluated impregnant molecule based on the inspiration performance. Therefore, the evaluation efficiency of the air suction performance of the impregnant can be improved, and screening and formula optimization of liquid impregnant molecules are realized.

This application proposes an analytical device and equipment for mental illness. The device divides the whole brain of the subject into Regions of Interest (ROIs) and obtains the centroid coordinates and BOLD time series of each ROI. It processes fMRI data using a sliding window method, calculates the correlation between ROIs within the time window to obtain a time-resolved functional connectivity (FC) matrix, processes DTI data using a probabilistic tracking method to obtain a structural functional connectivity (SC) matrix, obtains a Euclidean distance matrix based on the ROI centroid coordinates, and obtains several structural index matrices based on the SC matrix. It constructs an edge-pair feature matrix based on these matrices, where each element corresponds to a pair of ROIs. It performs PCA on the edge-pair feature matrix and maps the PCA results back to ROI pairs to obtain a secondary edge-pair feature matrix. Based on multiple linear regression, it estimates the predictive power from the secondary edge-pair feature matrix to the FC matrix, obtaining a dynamic structural-functional coupling time series matrix for each subject. Based on this matrix, mental illness analysis can be performed, fully considering the dynamic changes in functional connectivity over time, which is beneficial for comprehensively assessing brain network abnormalities related to mental illness.

The invention provides a mental disease analysis device and equipment. ROI is divided for a tested whole brain, ROI centroid coordinates and a BOLD time sequence are obtained, fMRI data are processed through a sliding window method, correlation between ROIs in a time window is calculated, a time-resolved FC matrix is obtained, DTI data are processed through a probability tracking method, an SC matrix is obtained, and the SC matrix is used for analyzing mental diseases. An Euclidean distance matrix is obtained based on ROI centroid coordinates, a plurality of structure index matrixes are obtained based on an SC matrix, an edge pair feature matrix is constructed based on the matrixes, each element corresponds to a pair of ROIs, PCA is executed on the edge pair feature matrix, a PCA result is mapped back to the ROI pairs, a secondary edge pair feature matrix is obtained, and the secondary edge pair feature matrix is obtained based on multiple linear regression. According to the method, the predictive capacity from the secondary side to the feature matrix to the FC matrix is estimated, a dynamic structure function coupling time sequence matrix of each tested object is obtained, mental disease analysis can be performed based on the matrix, the dynamic change characteristic of function connection along with time is fully considered, and brain network abnormity related to mental diseases can be comprehensively evaluated.

This invention relates to a method and process for shale oil reservoir lithofacies classification based on RGB fusion of well logging information. The method uses the first derivative of the reflection coefficient curve to divide rock units. For each rock unit, lithological indices are calculated using the root mean square of natural gamma logging, sedimentary structural indices are calculated using the roughness of resistivity logging curves, and oil-bearing indices are calculated using the root mean square of resistivity curves. The RGB color model is used to perform parameter fusion and color representation of the lithological, sedimentary structural, and oil-bearing indices of the shale reservoir. Different types of lithofacies color templates established using core sections are used to identify the lithofacies types of shale oil and gas reservoirs throughout the well section. This method achieves three-parameter fusion identification of shale reservoir lithofacies types, which is more intuitive and convenient.