12 results about "Probability curve" patented technology

This invention discloses an LSTM-driven dynamic prediction method and system for the operational resilience of integrated utility tunnels, relating to the field of deep learning. The method includes: acquiring vibration acceleration time histories, extracting peak acceleration and vibration energy accumulation time history curves, and generating a dynamic angle sequence; correcting the baseline vulnerability curve to obtain a dynamic damage probability curve, substituting the peak acceleration and vibration energy accumulation values ​​of the cross-section into the curve, and outputting a time-varying damage index sequence; identifying critical cross-sections with abrupt changes in the damage index, calculating the average damage index using the critical cross-section as a dividing point, and determining functional bottleneck sections; obtaining the average damage index and the redundancy of subsequent alternative paths, calculating the overall functional degradation rate of the utility tunnel, and obtaining the functional recovery multiple; calculating the initial recovery time, inputting it into a pre-trained dynamic time-series prediction model, and outputting the corrected final recovery time. This invention solves the problem of low prediction accuracy and high misjudgment rate in the prediction of the operational resilience of integrated utility tunnels due to the reliance on static experience for rough estimation in existing technologies.

This invention proposes an intelligent prediction method for performance degradation and durability limit state assessment of reinforced concrete structures, comprising: establishing a probabilistic model of random variables for environmental and structural parameters; constructing an intelligent prediction model for chloride ion diffusion by fusing convolutional neural networks, and using Monte Carlo sampling analysis to analyze the probability distribution of the initial corrosion time of the reinforcing bars to predict the depassivation time of the reinforcing bars; establishing a time-varying corrosion model of the reinforcing bars to obtain the change of the corrosion loss rate of the reinforcing bars over time; further, using an incremental static analysis method, establishing a multi-scale finite element model of the reinforced concrete structure under different corrosion rate conditions through random sampling; obtaining the critical load value under the limit state to obtain the failure probability curve under different corrosion degrees; and finally, by defining a bearing capacity reduction coefficient, obtaining the bearing capacity failure time of the reinforced concrete structure to achieve the durability limit state assessment of the reinforced concrete structure under corrosion.

The application provides an accident hidden danger road section dynamic identification method and platform based on multi-dimensional data fusion, relates to the technical field of traffic data identification, and comprises the following steps: converting a target road section area into a directed graph to determine a road network topology; generating a normal situation partition traffic trajectory through multi-dimensional road network data, judging a single vehicle trajectory and a traffic disturbance mode in combination with a real-time traffic trajectory, and generating a hidden danger identification instruction; positioning a hidden danger road section in the road network topology, performing combined inversion and state transition probability analysis based on random variables, and generating a hidden danger probability curve; performing hidden danger disturbance propagation analysis in the road network topology, determining hidden danger identification information, and performing traffic hidden danger early warning management. The application can solve the technical problem of affecting the accuracy of traffic hidden dangers in the prior art, realize dynamic hidden danger identification and prediction capability based on real-time traffic flow data and road network topology information, and achieve the technical effect of accurately identifying the space-time distribution of hidden dangers.

This fall probability prediction device has a computer function for outputting a numerical value calculated on the basis of an input value. The fall probability prediction device comprises a trained model that has been subjected to machine learning so as to output a fall probability curve, which represents a fall probability as a function of age, using the age of a plurality of individuals and the presence or absence of a fall for each of the plurality of individuals as explanatory variables. Upon input of the age of a subject as an input value, the fall probability prediction device outputs a fall probability curve for predicting the fall probability of the subject at a time later than the time of the input.

This application relates to the field of reservoir exploration technology, specifically to a method, device, storage medium, and electronic equipment for quantitative characterization of pore size across all scales, including: performing nuclear magnetic resonance analysis on the shale to be tested, obtaining the nuclear magnetic resonance T2 spectrum of the shale to be tested, and establishing a cumulative T2 probability curve; according to the formula r c =CT2 yields the pore size r corresponding to the T2 value with the same cumulative content. c Where C is the conversion coefficient; a cumulative pore size probability curve is established based on the pore size value and the corresponding cumulative content. This application can solve the problem of unclear pore size distribution in continental shale at all scales due to strong heterogeneity, and achieve quantitative characterization of pore size at all scales while avoiding damage to the shale under test.

The present application relates to the technical field of rock fracture modeling, and discloses a method and a processor for modeling a multi-scale fracture network of a tight reservoir. The method comprises: obtaining a strike attribute of a fracture; generating a fracture strike conforming to a preset mode according to a cumulative probability density curve of the strike; taking a fracture position interpreted from a core and an imaging logging as hard data of a fracture position in a fracture network; dividing the hard data into seed hard data and correction hard data; correcting a cumulative probability curve of fracture development intensity according to the seed hard data, and generating a fracture element set according to the corrected probability density curve; generating a discrete fracture network through similarity fusion criteria, mechanical cause fusion criteria and intersection criteria; comparing the established discrete fracture network with the correction hard data, and correcting the number of fracture elements and the cumulative probability curve. Through iterative inversion of the seed hard data and the correction hard data, a more optimal multi-scale fracture network model that is more consistent with the hard data is generated.

The application provides a pump-driven two-phase flow loop fault diagnosis method and device based on Naive Bayes, a medium and equipment. The method comprises the following steps: acquiring real-time monitoring data of a pump-driven two-phase flow loop; converting a target characteristic parameter of the real-time monitoring data into a discrete state value based on a preset discretization rule; inputting the discrete state value into a pre-trained fault diagnosis model to generate a probability curve corresponding to each of a plurality of fault types defined by the fault diagnosis model; determining a diagnosis result of the pump-driven two-phase flow loop based on a preset multi-threshold decision rule and the probability curve; wherein the multi-threshold decision rule is used to determine a fault occurrence, and the multi-threshold decision rule comprises an absolute probability condition, a relative probability value condition and a relative threshold length condition that need to be met by the probability curve of each fault type. The application constrains the model through the multi-threshold decision rule, improves the accuracy of the fault diagnosis result, and the obtained result has interpretability.

The present application relates to a kind of permeability model construction method and system and electronic equipment, the method includes: the core porosity data and permeability data obtained according to porosity data size range, core porosity data and permeability data are evenly divided into several pore permeability data group, and make the porosity interval of each pore permeability data group not more than first preset threshold;Judge the sample number of each pore permeability data group whether greater than second preset threshold, if yes, then the pore permeability data group is determined as high confidence group, if not, then the pore permeability data group is determined as low confidence group;Determine the permeability value corresponding to each porosity data under each preset cumulative distribution probability, obtain corresponding multiple permeability cumulative distribution probability curve, interpolation calculation is carried out according to cumulative distribution probability curve data, to obtain permeability probability distribution atlas. It can realize the permeability probabilistic prediction based on porosity data, provides theoretical basis and quantitative method for the characterization of permeability uncertainty.

This invention discloses a method and system for dynamic optimization of live streaming scenarios. The method includes: real-time collection of urban governance events, network traffic, and power grid operation data, transforming them into unified spatiotemporal features; based on these unified spatiotemporal features, fusing an event impact quantification model and an LSTM network to predict resource demand curves, network congestion probability curves, and power grid safety margin curves to obtain prediction results; integrating the prediction results using reinforcement learning methods to dynamically adjust resource scheduling, power grid power, and network congestion thresholds to obtain an optimized strategy; synchronously distributing the optimized strategy to cloud resources, the power grid, and network controllers, collecting and feeding back the execution results to the fusion event impact quantification model and LSTM network, and the dynamic adjustment process. By implementing this method, it is possible to comprehensively consider urban governance events, live streaming service KPI data, power grid section operation status, and multi-cloud pool resource indicators, more effectively predicting and responding to various changes, ensuring the quality of live streaming services, while guaranteeing network stability and power system security.

This application proposes a method, device, server, and computer-readable storage medium for predicting the lifespan of a CT tube, relating to the field of medical devices. The tube lifespan curve is generated based on multiple cumulative exposure times and the predicted failure probability corresponding to each cumulative exposure time. The predicted failure probability is generated by processing the cumulative exposure times into a pre-generated failure prediction model, which is trained based on multiple sample lifespan data and corresponding failure probability labels. The current cumulative exposure time of the CT tube and the target tube lifespan curve are obtained. Based on the target tube lifespan curve, the current failure probability of the CT tube is determined according to the current cumulative exposure time. If the current failure probability exceeds a preset failure threshold corresponding to the CT tube, a failure alarm is issued. This method captures the true degradation characteristics of the CT tube, improves the fitting accuracy of the CT tube failure probability curve, and ensures the normal use of the CT equipment.

This invention relates to the field of underground soil detection and discloses a method for delineating the range of a pollution plume based on pollutants and their metabolites in the soil. The method includes: setting up sampling points at the site to be investigated as needed; drilling holes at the determined sampling points and collecting and detecting gas concentrations in the soil using a detection device; fitting the collected gas concentration data to a curve using a curve fitting model, dividing the cumulative probability curve into intervals, analyzing and determining the optimal regression curve for each interval, and then using the intersection of the regression curves of two adjacent intervals in the cumulative probability curve as the threshold for partitioning; and drawing a planar distribution map of each pollution indicator based on the partition threshold and using an interpolation method. By achieving minimally invasive operation on only about 50 cm of the soil surface, the method can detect relevant gas concentrations in the underground soil layers and quickly delineate the range of the pollution plume. The operation is simple and convenient, greatly reducing the time and cost of site investigation.