13 results about "Relative risk" patented technology

This application discloses a method, apparatus, equipment, and medium for predicting risks in chemical processes, relating to the field of deep learning technology. The method includes acquiring multi-source process data of a chemical process; preprocessing the multi-source process data to obtain preprocessed data; constructing a network model based on the preprocessed data; calculating the structural entropy of the network model to obtain network structural entropy; solving the network structural entropy to obtain a relative risk value sequence; and using a preset attention mechanism-bidirectional long short-term memory network model to predict the risk of the relative risk value sequence to obtain a risk prediction sequence for the chemical process. Through the above technical solution of this application, the accuracy of chemical process risk prediction can be increased, achieving intelligent risk early warning.

The application provides a severe risk prediction method based on a PINN-Cox combined model and related equipment, and relates to the field of intelligent medical information technology. The technology obtains multi-source time series clinical data, and constructs individual survival analysis data sets. On this basis, a PINN-Cox combined prediction model is constructed. The non-linear mapping capability of the deep neural network is used to output a time-dependent relative risk function, which can accurately represent the instantaneous hazard rate in a complex severe scenario. At the same time, the method innovatively defines a mechanism-data combined loss function. In the training, a negative log partial likelihood loss term based on a non-proportional Cox model and a physical residual loss term based on a differential equation of disease evolution mechanism are introduced at the same time, thereby improving the robustness and anti-overfitting capability of the model under the condition of incomplete clinical data such as small samples and irregular follow-up. Finally, the dynamic prediction of the survival function, cumulative risk function and expected remaining life median of the to-be-predicted patient within a preset time window in the future is realized.

The invention relates to the technical field of vehicle safety, and provides an air bag preparation method and system combining feature fusion and a hierarchical model, and a terminal. The risk occurrence probability is obtained by evaluating the probability that the vehicle terminal has the risk event, the first risk probability is obtained by performing feature fusion according to the relative risk probability between the risk occurrence probabilities, and the risk probability weight is allocated to the risk dimension and the risk evaluation factor; and carrying out weighted calculation on the risk occurrence probability according to the risk probability weight to obtain a second risk probability, and judging whether the airbag controller enters a pre-preparation state or not by combining the first risk probability and the second risk probability. Therefore, the risk probability is determined by combining different risk assessment algorithms, the robustness of risk assessment is improved, and compared with independently distributing the weight for each risk assessment factor and distributing the risk probability weight for the risk dimension, the risk assessment accuracy is improved, and the reliability of making a decision when the airbag controller enters the pre-preparation state is improved.

The present application relates to the technical field of security risk classification, in particular to a kind of city security risk classification and evaluation early warning system, comprising: risk sampling module, region division module, risk load module, risk progression module and risk update module;Through the risk data and risk type in the risk sampling area are listed;Using the risk type and the risk load capacity of each sampling area is classified, determine the regional risk distribution map of each sampling area;From the regional risk distribution map, extract risk point, with the data proportion of each risk point and the risk load capacity of region, generate regional risk load probability;Set the risk point load capacity of each risk point, and extract the progression probability of risk point load capacity, obtain the relative risk state of each sampling area;According to the relative risk state of each sampling area, identify the risk entity and the update range of risk entity of each sampling area, and iteratively update the risk level of each sampling area;The efficiency of risk level division is realized.

The invention relates to the technical field of vehicle safety, and provides a feature fusion and relative weight combined airbag preparation method and system and a terminal. The risk occurrence probability of each risk assessment factor is obtained by assessing the probability that the vehicle terminal has the risk event, feature fusion is performed according to the relative risk probability between the risk occurrence probabilities to obtain the first risk probability, and the risk relative weight is determined according to the relative importance between the risk assessment factors. The risk occurrence probability is subjected to weighted calculation according to the risk relative weight to obtain the second risk probability, so that whether the airbag controller enters the preparation state or not is judged in combination with the first risk probability and the second risk probability, the risk assessment robustness is improved, the weight is determined through the relative importance among the risk assessment factors, and the risk assessment accuracy is improved. Therefore, the relative weight can truly and accurately reflect the actual influence of each factor in a specific scene, so that the reliability of decision making when the airbag controller enters the pre-preparation state is improved.

This invention discloses a method, apparatus, equipment, and storage medium for risk early warning of power contractors, belonging to the technical field of power contractor risk early warning. The invention includes four steps: Step 1: generating a risk profile of the power contractor; Step 2: quantitatively assessing the power contractor's risk resistance capability; Step 3: calculating the relative risk value of the power contractor enterprise; Step 4: based on pre-set thresholds as early warning conditions, issuing risk warnings to power contractors reaching high-risk and medium-risk levels, and outputting the relative risk value and indicator information for the warning; no warning is issued for low-risk contractors. This invention automates the entire process from data collection and classification of power contractor risk activities, generation of profile indicators, quantitative calculation and assessment, relative risk value calculation, and risk early warning. The entire process is based entirely on data-driven decision-making to achieve risk quantification and early warning, fully considering the relative numerical assessment of risk indicators and risk resistance capabilities, thus improving the accuracy of risk early warning.

The application discloses a kind of urban road network traffic accident risk research and visual method, comprising: obtaining road network accident data, pedestrian data, vehicle data and environmental data;The data obtained are preprocessed;Obtain the connection relationship of city road network, establish road network adjacency matrix, obtain the spatial correlation of each accident;Establish the random parameter space joint prediction model of road network accident severity considering road network spatial correlation and data heterogeneity;The coefficients corresponding to each variable in the prediction model are analyzed, and the risk factors affecting the severity of the accident are identified;According to the spatial residual term of each road entity, the relative risk value is calculated, and the relative risk of road network heavy accident is identified;According to the relative risk value of each road entity, a road network risk map is drawn, and the relative risk profile visualization is realized.The application can identify the influencing factors of traffic accident severity from the micro level, explore the potential relationship between risk factors and accident severity, and identify accident black spots.

The application provides a breast cancer absolute risk prediction method based on generative representation and relative risk decomposition, which receives a mammography image, clinical factors, region identification and age, firstly determines an annual baseline incidence rate sequence according to the age and the region; uses a generative representation model to perform imaging style generation and continuous constraint on the image, aligns the breast structure, and extracts a feature vector reflecting long-term tissue state; then inputs the feature vector and the clinical factors into a staging risk calculation framework to obtain a relative risk coefficient, and uses an imaging style variant set to perform consistency test and parameter correction; on this basis, the absolute risk sequence is recursively synthesized according to the baseline incidence rate by year, the risk is determined according to a threshold, and a screening interval is obtained; the application can output individual absolute risk and screening interval on a unified annual time axis, weaken the influence of imaging style difference on the evaluation result, and improve the stability of risk prediction.

Relative risk in a network system can be determined according to some examples. For example, a computing system can determine a hierarchy of groupings for risk factors within a network based on risk data associated with the risk factors. The computing system can determine associations between the risk data and the risk controls. Each risk control can represent an amount of control for reducing riskiness of a risk factor. The computing system can determine a risk assessment for each grouping of the hierarchy of groupings based on the risk data, the risk controls, and the hierarchy of groupings. Determining the risk assessment can include determining a control coverage for each grouping based on the associations and the risk data. The computing system can output the risk assessment for each grouping of the hierarchy of groupings for display on a graphical user interface.

The application discloses a hydrogen leakage situation prediction model modeling method and device, and the scheme is characterized in that: first, the measurement data of historical hydrogen leakage events, preset leakage risk index information and hierarchical system information between different leakage risk indexes are acquired; the first weight and the second weight of each leakage risk index information are calculated by using the entropy weight method and the analytic hierarchy process respectively; then, based on the first weight and the second weight, the relative distance degree between each leakage risk index and the worst solution of the index is determined by using the superior-inferior solution distance method, and a relative risk identification result is obtained; the highest risk event of hydrogen leakage in the relative risk identification result is subjected to fault tree decomposition, a plurality of basic component failure events of the highest risk event are obtained, and model training is performed by taking the measurement data as a training set, so that a hydrogen leakage situation prediction model is obtained, and the technical problem that the existing hydrogen leakage prevention and control cannot perform situation awareness on the space-time evolution of hydrogen leakage events is solved.

A gas pipeline leak detection technology and method, belonging to the interdisciplinary field of risk assessment and information technology, includes: collecting various parameters of the gas pipeline; based on the collected parameters, using an improved Kent evaluation model to adjust and score the first set of indicators for urban environmental adaptability, determining the weights of the adjusted indicators using the analytic hierarchy process (AHP), and calculating the relative risk value of each pipe segment unit in combination with a second index; simulating an accident using a leak scenario model based on leak scenario parameters, and outputting the accident simulation results; spatially overlaying the relative risk value and the accident simulation results on the same electronic map, and outputting emergency response measures. This invention achieves accurate risk assessment of urban gas pipeline networks by combining an improved Kent evaluation model with the AHP, and provides intuitive and scientific technical support for risk management and emergency decision-making in gas pipeline networks by combining multi-model fusion leak accident simulation and visualization technology.

The application discloses a kind of urban intersection space accident severity relative risk discrimination and visualization method, comprising: obtaining intersection accident data, intersection design data, meteorological data, traffic flow data;The data obtained are preprocessed;Extract city road network topology, construct intersection adjacency matrix, obtain the spatial correlation of each accident;Establish the spatial prediction model of road intersection accident severity considering intersection space correlation;Extract the spatial residual term of each intersection, and discriminate the relative risk of intersection heavy accident;According to the relative risk value of intersection, draw the accident severity spatial risk hotspot identification diagram, realize risk profile visualization.The application can effectively determine whether a specific intersection in urban road has higher probability of serious injury accident than intersection with similar characteristics, and visualize in the form of heat map, provide theoretical support for accident black spot diagnosis.