6 results about "Hazard model" patented technology

Concepts are proposed for augmenting a plurality of climate impact and hazard models. In particular, the plurality of climate impact and hazard models are modified / altered based on a user-specified requirement (i.e., a specified geographical location, temporal location or hazard type) and at least one intelligent (i.e., AI-enabled) workflow. Each modified climate impact and hazard model is then analyzed / processed using a machine learning model to identify model parameters which may improve the climate impact and machine learning model. An operation is then identified and executed based on the intelligent workflow, and model parameter. In this way, speed, efficiency and accuracy of climate impact and hazard models may be improved at scale.

The present application relates to the field of prediction and health management of mechanical equipment, and particularly relates to a joint remaining useful life prediction method based on a three-stage proportional hazard model and a transformer network and an electronic device, comprising the following steps: S1, constructing a three-stage proportional hazard PH model and initially predicting a remaining useful life RUL; S2, RUL correction based on a transformer neural network; the present application firstly constructs a three-stage PH model based on a three-stage Gamma process covariant, incorporates the stage-dependent degradation characteristics of mechanical equipment into a statistical modeling framework, and then introduces a transformer neural network error compensation mechanism for learning the systematic deviation between the predicted value of the three-stage PH model and the actual remaining life, thereby realizing dynamic correction of the prediction deviation and effectively reducing the overall error in the entire prediction process.

This invention discloses a statistical system and method for monitoring the condition of construction machinery based on narrowband Internet of Things (IoT) technology. The system includes the following steps: collecting operating status signals of construction machinery and generating continuous raw time-series data; preprocessing the data to construct a cumulative deviation statistical sequence; performing change point detection at the edge to generate structured change point events; transmitting the structured change point events to the cloud via narrowband IoT; constructing a change point event sequence based on the received structured change point events in chronological order; defining stable operating segments based on the time intervals between adjacent change points; constructing time-dependent covariates within each stable operating segment and reconstructing them as new change point events arrive; inputting the time-dependent covariates into a proportional hazards model and using the change point time as a risk function to generate a risk value sequence segmented by nodes; forming a equipment risk ranking result and outputting a maintenance priority list. This invention enables remote monitoring, statistical analysis, and maintenance decision-making for the operating status of construction machinery.

This invention discloses a method and apparatus for generating hazard models based on intelligent recognition and spatial grid algorithms. The method includes: preprocessing and differentially labeling an initial image set to obtain a structured labeled dataset, then training a corresponding hazard recognition neural network; identifying historical hazard image sets using the hazard recognition neural network to obtain hazard identification results and target risk intervals for hazard areas; constructing a corresponding hazard scene model and inferring a spatial Gaussian scene; dynamically adjusting the basic risk weights of key areas in the hazard scene model and matching the spatial Gaussian scene with the hazard scene model to obtain a corresponding target hazard scene model. This method automatically generates simulation scene models corresponding to real-world hazards through virtual simulation and 3D interactive technology. Users can intuitively learn hazard characteristics and practice / assess in a virtual environment, improving the efficiency of user training and enhancing the immersion and practicality of the training.

This invention relates to the field of industrial robot maintenance technology, specifically to a method for industrial robot condition monitoring and predictive maintenance. The method includes: acquiring and fusing multidimensional operational and environmental data; calculating the Mahalanobis distance to the healthy centroid using kernel principal component analysis projection as a first state covariate; constructing a Weibull baseline risk function by classifying historical task types; calculating the current kurtosis and summing it with a forgetting factor to obtain a second time-varying covariate; constructing a proportional hazards model; combining the current task baseline function and the two types of covariates to output the remaining lifetime probability distribution and expectation; defining a utility function that includes spare parts, downtime losses, and expected failure risk costs; optimizing the maintenance window to determine the maintenance timing that maximizes utility and generating instructions. In other words, the solution of this invention can improve the accuracy of condition assessment and lifetime prediction, achieve optimal allocation of maintenance resources, and ensure production continuity.

This invention provides a dynamic risk assessment method for vehicle transportation based on a closed-loop coupling of FTA and the exponential method. It employs a multi-subjective and multi-objective weighting method to eliminate reliance on subjective experience and utilizes an exponential decay model to dynamically adjust static weights, thereby driving the construction of a dynamic risk quantification system. Simultaneously, it overcomes the static limitations of traditional fault trees by adding real-time data-driven dynamic risk perception capabilities through the exponential method. Furthermore, it combines the time-varying analysis capabilities of the Cox proportional hazards model with the logical structure of fault trees to construct a novel dynamic failure rate update framework. Finally, it introduces an adaptive threshold function to dynamically classify warning levels, analyze the contribution of each event in real time, accurately locate key risk transmission paths, and ultimately achieve a complete closed loop from real-time monitoring and accurate assessment to rapid tracing and handling.