7 results about "Critical index" patented technology

The invention relates to the technical field of industrial data processing, in particular to an intelligent safety accident early warning method and system based on risk assessment, and the method comprises the steps: obtaining a mining operation event, extracting response situations before and after the operation event, checking the response situations with a reference response template, and judging a disturbance response according to the deviation degree; a ventilation network model is constructed, and when it is detected that a monitoring point is abnormal, management traceability is carried out along a traversal upstream path in combination with an air volume distribution proportion; predicting the propagation trajectory of the abnormal gas along the wind flow path and the time of reaching a downstream node based on the ventilation network model, and obtaining spatial correlation analysis; analyzing the disturbance response and the spatial correlation, calculating a coupling risk increment, and outputting a comprehensive risk value; judging the critical degree grade of the comprehensive risk value time series data; and determining a scheduling level according to the combination of the comprehensive risk value level and the critical degree level, when the critical index indicates to evolve to a dangerous state, improving the scheduling level in advance, and generating a differentiated resource scheduling suggestion in combination with the propagation trajectory.

The invention relates to the technical field of environment monitoring and automatic control, in particular to a sewage early warning and treatment analysis system based on deep learning. Comprising a data acquisition module used for acquiring multi-dimensional real-time data of an activated sludge system of an urban sewage treatment plant and constructing a time sequence characteristic data set; the first processing module is used for outputting a dynamic weight coefficient through deep learning model training and reasoning based on the time sequence characteristic data set; the second processing module is used for calculating a critical index representing the system risk in real time by combining the multi-dimensional real-time data, the dynamic weight coefficient and a preset parameter reference value; the early warning judgment module is used for comparing the critical index with a preset early warning threshold value and judging the risk level of the system; and the intervention decision module is used for responding to the risk level judged by the early warning judgment module and generating and executing a hierarchical intervention strategy. According to the system, accurate prediction of the viscous collapse critical state is realized.

This invention provides a method and system for risk point classification in urban rail transit vehicle systems, belonging to the field of urban rail transit operation and maintenance technology. It analyzes and obtains the component composition and connection relationships within the urban rail vehicle system; determines the structural importance index, functional importance index, and critical index based on accident data for each component; uses the K-Means++ algorithm based on a genetic algorithm to determine the number of classifications and clustering results; applies the Gini importance quantification of random forests to quantify the weights of the three types of indicators, calculating the correspondence between the clustering results and the classification levels; and uses set theory as a guide to form the classification results of urban rail vehicle risk point sets. This invention, from the perspective of networked operation and comprehensively considering multiple factors, achieves more accurate risk point classification in urban rail vehicle systems, which has significant theoretical and practical implications for improving rail transit operation safety risk management and ensuring safe and efficient operation.

The invention relates to the field of precise milling and turning, in particular to a dynamic quality planning intelligent management method based on a mechanism model, which comprises the following steps: collecting multi-source heterogeneous data, and respectively constructing an implicit disturbance risk index for representing a system disturbance risk and a wear state parameter for representing a system wear state based on the data; calculating to obtain a system critical index in combination with the implicit disturbance risk index and the wear state parameter; presetting a critical state grade threshold value, and comparing the system critical index with the critical state grade threshold value to obtain a critical state evaluation result; generating a process correction factor based on the critical state evaluation result and the system critical index; according to the process correction factor, outputting a grading correction strategy for the processing process parameters; dynamic balance of production efficiency and quality safety is realized, and quality collapse is effectively inhibited.

The application discloses a kind of city fire hazard early warning analysis method and system based on big data, it is related to disaster warning technical field, including based on feature dimension initialization KDE's Gaussian kernel function and bandwidth parameter, construct quenching function and coupling parameter as constraint in combination with GIS information, calculate simulation defect density, in combination with time series calculation actual critical index and standard deviation, construct heat diffusion equation and solve by two-stage normal adjacent method, generate space-time risk field, calculate risk propagation speed field.The application improves the risk identification accuracy of high-dimensional heterogeneous data through the multi-index clustering method combining entropy weight and kernel density, constructs the odd-symmetry anomaly point identification mechanism, improves the accuracy and stability of anomaly point identification, constructs the heat diffusion equation, generates the space-time risk field by two-stage normal adjacent method, realizes the dynamic evolution display of city fire risk, and improves the early warning response efficiency.

The invention discloses a court case trial simulation method and system based on a complex adaptive system, and belongs to the crossing field of judicial management and computer simulation. According to the method, in order to solve the problems that traditional court management lacks quantitative predictive analysis and influences of various factors on the judgment efficiency are difficult to evaluate, a court case judgment full-process simulation scheme based on Multi-Agent Dynamics is provided. The method specifically comprises the following steps: 1) establishing a three-layer nested model with case characteristics, judge characteristics and judicial program parameters, and realizing full-cycle simulation of a case from case filing, delivery, identification, mediation to case settlement; 2) introducing multi-element random influence factors, such as case complexity, common litigation, lawyer participation rate, crime penalty and the like, and accurately depicting judgment difficulty differences of different types of cases; 3) constructing a judge interaction model, fusing attributes such as qualification and experience, and realizing dynamic balance of working efficiency and case allocation; 4, a Monte Carlo simulation method is adopted, a large amount of simulation data is generated through parameter perturbation, and a sensitivity curve of key indexes is drafted. The system is applied to court trial situation analysis, the case trial efficiency under different parameter changes is accurately predicted, and a scientific basis is provided for court resource allocation and policy making.

The invention discloses a performance evaluation method for an oil chromatography monitoring device and related equipment, which break through the limitation of a traditional single evaluation system by acquiring multi-dimensional operation data such as stability, accuracy, operation and maintenance management, time decay and the like, calculating evaluation factors and performing statistics according to models to generate an exclusive evaluation factor matrix. A critical index calculation method is utilized, the weight is determined based on normalization of a standard deviation of an evaluation factor and a conflict coefficient product, and compared with traditional empirical empowerment, objective weight distribution is achieved, and subjective deviation is avoided. Meanwhile, a dynamic reliability index model is constructed, operation data of a target device are input in real time, and a reliability index is output in combination with a preset threshold value, so that the problem of hysteresis of regular verification is effectively solved, and long-term monitoring requirements are met. In addition, the evaluation factor matrix supports differentiated evaluation, and the limitation of a unified standard is broken through. According to the invention, the problems of single evaluation, insufficient timeliness and lack of differentiation in the prior art are solved, and comprehensive, accurate and real-time evaluation of the performance of the oil chromatography monitoring device is realized.