17 results about "Hazard model" patented technology

The invention relates to a construction safety management technology, and discloses a construction potential safety hazard automatic identification method and system based on deep learning to improve the efficiency and accuracy of construction potential safety hazard identification. The method comprises the steps of performing feature extraction on historical construction anomaly information to obtain time period features, personnel behavior features, environment parameter features and equipment operation features, and training a preset construction anomaly hidden danger model to obtain a construction anomaly hidden danger model. And processing the personnel behavior information, the equipment operation information and the environment parameter information to obtain construction precursor data. Inputting the construction precursor data into the construction abnormal hidden danger model for identification to obtain a hidden danger identification result, if at least one abnormity exists in the hidden danger identification result, determining an area position with the hidden danger abnormity, performing hidden danger grade analysis on the area position to obtain a hidden danger abnormity grade, and if the hidden danger abnormity grade meets a preset hidden danger grade, determining that the hidden danger abnormity exists. And if yes, generating hidden danger abnormal early warning.

The present invention discloses an operation and maintenance management method and system for an air quality monitoring platform based on the Internet of Things, which relates to the technical field of air quality monitoring platform management; the present invention corrects data drift in real time through an environmental parameter compensation algorithm to improve monitoring accuracy; uses a carp population optimization algorithm to obtain an improved air quality prediction network to optimize air quality prediction performance; constructs a hybrid network of LoRaWAN and NB-IoT, dynamically adjusts the acquisition frequency according to the risk level, and reduces the communication load; combines the Internet of Things health score model with the Cox proportional hazards model to predict equipment failures, plans the optimal maintenance path through the TSP algorithm, and improves the operation and maintenance efficiency; dynamically optimizes equipment deployment based on the Gaussian process regression algorithm; the present invention realizes the full-chain closed-loop management of "perception-prediction-transmission-maintenance-optimization", significantly improves data accuracy, communication efficiency and equipment reliability, and provides a low-cost and adaptive solution for the air quality monitoring platform.

The invention provides a repairable system lightweight on-condition maintenance integration method and system under variable working conditions, and the method comprises the steps: employing a comprehensive reliability evaluation model as a reliability calculation tool, employing a lightweight comprehensive maintenance model, and calculating an optimal economical maintenance interval in a maintenance stage; combining the calculated optimal economical maintenance interval, the set lowest reliability and the parameters of the comprehensive reliability evaluation model, and calculating to obtain an optimal economical maintenance threshold value and a lowest operation reliability threshold value; and comparing the equipment comprehensive risk index value with a maintenance threshold value to obtain a condition-based maintenance decision result, and realizing reliability calculation of the repairable system in a running-maintenance-running cycle process by utilizing a comprehensive reliability evaluation model formed by combining a proportional risk model and a mixed failure rate model. And through interaction with the lightweight comprehensive maintenance model, a condition-based maintenance decision of the repairable system is realized.

The invention belongs to the technical field of data processing, and particularly relates to an intelligent kitchen electrical equipment data analysis method and system based on an Internet of Things cloud platform, and the method comprises the steps: obtaining a historical state feature sequence of kitchen electrical equipment at a current evaluation time point; performing double-path parallel time sequence feature analysis on the historical state feature sequence, and respectively calculating a time sequence context vector, a time fluctuation index and an accumulated severity score; constructing a dynamic risk signature based on the time fluctuation index and the accumulated severity score, and combining the current state and the historical context to form an enhanced feature vector; and inputting the enhanced feature vector into a Cox proportional risk model, and calculating a real-time fault risk. According to the method, the enhanced feature set containing the multi-dimensional time sequence information is constructed by deeply excavating the time sequence evolution mode and the dynamic dependency relationship of the operation state of the equipment, and the accuracy of fault risk prediction of the kitchen electric equipment is remarkably improved.

A system and method for forecasting failure in a plurality of assets wherein the system comprises one or more computing devices connected to a server through a network, wherein the server comprises a memory that stores reusable group attribute values, and one or more processors coupled to the memory. The one or more processors comprise program instructions that when executed, cause the one or more processors to forecast one or more failure rates for said assets based on a hazard model. The method comprises identifying a subgroup of assets and attributes of a specific plurality of assets and attributes corresponding to a grouped attribute combination, retrieving reusable grouped attribute values from the memory, and replacing a process of evaluating the subgroup of assets and attributes according to the hazard model, with the retrieved reusable grouped attribute values.

A calculation method for an annual average loss of an external substructure reinforcement system based on interfacial shear stresses is provided. The calculation method includes: step 1: establishing a deterministic model for an external substructure reinforcement system, and generating seismic ground motion samples and structural samples by random sampling; establishing a probabilistic hazard model based on the seismic ground motion samples; generating a probabilistic vulnerability model based on interfacial shear stresses; establishing a total probabilistic loss expectation model by different seismic ground motion intensity levels; and using the total probabilistic loss expectation model to obtain an annual average probabilistic seismic loss of the external substructure reinforcement system based on the interfacial shear stresses. The calculation method can realize classified calculations of the seismic loss of the external substructure reinforcement system. Compared with the traditional seismic loss calculation method, the calculation method is more efficient in calculation.

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.

The invention provides a maintenance decision method and system for predicting reliability of rotating equipment based on dynamic monitoring, and the method comprises the steps: taking maintenance operation interval time as operation time, analyzing a plurality of monitoring indexes of the rotating equipment based on the operation time, and forming corresponding index time sequence change data; determining an initial failure rate by adopting a probability statistical distribution algorithm according to the operation time of the rotating equipment; determining a fault rate function of the to-be-calculated object based on the proportional risk model, and calculating fault rate thresholds corresponding to different state information of different to-be-calculated objects; obtaining an index prediction value based on a long-short-term memory neural network algorithm, and further obtaining a state function prediction value; and judging the relationship between the state function predicted value and the fault threshold curve to formulate a corresponding operation and maintenance scheme. By the adoption of the scheme, the defects that in the prior art, analysis elements are single, and calculation result accuracy and dynamic characterization characteristics are insufficient can be overcome, and the reliability change condition of the rotating equipment can be monitored and predicted efficiently and flexibly.

A parameter estimation device estimates parameters of a Cox proportional hazard model through secure calculations, the parameter estimation device comprising: a data storage unit that stores a database, the database having records for each observed object, the records including the time point at which an event is observed, the characteristic quantity of the observed object at that time point, and the state of the observed object at that time point; a calculation unit that reads a vector composed of time points from the database, generates a permutation table and a mark indicating the boundary of the time points by sorting the vector, and by using the permutation table and the mark, performs aggregation of the characteristic quantity at each time point while hiding the value of the time point, and performs the parameter estimation based on the aggregation result; and an output unit that outputs the parameters estimated by the calculation unit.

The present invention belongs to the technical field related to equipment maintenance, and specifically discloses a corrective sequential monitoring and maintenance method for equipment facing the impact of random failures, as well as a storage medium and an electronic device. The method includes the following steps: 1) Using historical state monitoring data, establish a proportional hazards model (PHM), and predict the remaining useful life (RUL) of the equipment based on the model; 2) Construct a correction factor model based on the error between the predicted value and the true value of the RUL to obtain a corrected RUL predicted value; 3) Taking the corrected RUL under the random failure mode as the control standard, formulate a maintenance strategy with sequential monitoring intervals and decision control limits; 4) Construct an optimization algorithm based on semi-Markov decision process (SMDP), and iteratively obtain the optimal corrected RUL decision control limits and sequential monitoring intervals; (5) Online obtain the state monitoring data of the equipment, and perform high-low frequency monitoring according to the corrected RUL level. The method proposed by the present invention is beneficial to reasonably monitor the operating state of the equipment, can save the equipment operation and maintenance costs, effectively improve the economy of equipment operation, and has strong operability in actual engineering.

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.

A system and method for forecasting failure in a plurality of assets wherein the system comprises one or more computing devices connected to a server through a network, wherein the server comprises a memory that stores reusable group attribute values, and one or more processors coupled to the memory. The one or more processors comprise program instructions that when executed, cause the one or more processors to forecast one or more failure rates for said assets based on a hazard model. The method comprises identifying a subgroup of assets and attributes of a specific plurality of assets and attributes corresponding to a grouped attribute combination, retrieving reusable grouped attribute values from the memory, and replacing a process of evaluating the subgroup of assets and attributes according to the hazard model, with the retrieved reusable grouped attribute values.

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.