7 results about "Early warning score" patented technology

The application discloses a short-term and impending roof fall early warning method for underground mines based on multi-source monitoring and partition early warning values, and comprises the following steps: S1, multi-source monitoring is conducted on roof fall to obtain multi-source monitoring data; S2, based on existing several stope falling rock mass influencing factors, specific influencing factors are screened, and partition early warning is conducted based on the specific influencing factors, and an early warning value early warning scoring system of rock mass internal fracture and surface deformation is established; and S3, the early warning value early warning scoring system of rock mass internal fracture and surface deformation is utilized to conduct actual partition early warning. The method is based on multi-source monitoring means, and meanwhile, the specific engineering geological condition partition of the monitoring object is considered, different early warning values are set for different partitions, and the short-term and impending early warning accuracy is remarkably improved.

This invention relates to the field of integrated defense and early warning technology for clustered targets, and discloses a clustered target threat early warning method based on intuitionistic fuzzy game theory and consensus decision-making. This method collects raw data from multiple sensors and quantifies it into an intuitionistic fuzzy set to generate a clustered integrated decision matrix; calculates the degree of consensus and hesitation potential energy; when consensus is insufficient, it generates resource focusing instructions to drive sensors to adjust physical parameters and perform feedback correction to form a consensus closed loop; it constructs a strategic conflict model to perform game-theoretic linkage analysis to solve for optimal attribute weights and calculates a dynamic early warning threshold; finally, it calculates the final early warning score based on the MABAC algorithm, compares it with the dynamic threshold to trigger an alarm, and outputs a dynamic interpretation vector. This invention solves the problems of decision oscillation and poor adaptability to fixed thresholds in complex electromagnetic environments by using physical information synergy enhancement and strategic game threshold drift mechanisms, thereby improving the accuracy and interpretability of early warnings.

This application discloses a mental health early warning method and system based on virtual avatar dialogue. First, a semi-structured dialogue list is compiled. Then, standard dialogue recordings based on the semi-structured dialogue list are acquired. Next, extended dialogue lists and manually compiled dialogue lists are generated based on the standard dialogue recordings. Then, extended dialogue recordings based on the extended dialogue lists and manually compiled dialogue lists are acquired. Next, a set of subjective indicator parameters is acquired, and an objective indicator parameter set is generated based on the standard dialogue recordings and the extended dialogue recordings. Finally, a comprehensive early warning score is calculated based on the set of subjective and objective indicator parameters. Compared with existing technologies, this application ensures the comprehensiveness and flexibility of subjective evaluation through semi-structured dialogue lists, extended dialogue lists, and manually compiled dialogue lists, while achieving objective evaluation through factors such as voice. Finally, an early warning assessment result is given based on both subjective and objective evaluations, effectively ensuring the accuracy of the assessment.

The present application relates to the technical field of coal mine safety, a gas disaster cloud early warning method and system based on multidimensional data driving, comprising: confirming discrete data based on multiple historical coal mine data, confirming an influence coefficient based on the discrete data and a pre-constructed correlation analysis algorithm, confirming a gas state monitoring mechanism, obtaining a coal mining working area, confirming gas pressure based on the coal mining working area, a pressure sensor and a hole sealing air bag, confirming a firmness coefficient based on the coal mining working area and a crushing testing machine, confirming a gas emission risk index based on the coal mining working area, a gas flow meter and a gas concentration sensor, calculating a disaster early warning score according to the pressure influence index, the hardness influence index, the emission speed influence coefficient, the gas pressure, the firmness coefficient and the gas emission risk index, confirming a risk level according to the disaster early warning score, and completing gas disaster early warning. The present application can improve the accuracy of gas disaster early warning.

The present application relates to the technical field of power equipment detection, and discloses a micro offset early warning method and system for an electric energy meter detection device, a medium and a product. The early warning method comprises the following steps: randomly decomposing an error value of a detection unit into three errors subject to a normal distribution; obtaining prior and posterior distributions of the three errors according to a Bayesian method, and calculating a system offset mean value and a 95% confidence interval of the offset; calculating a fusion early warning score of the detection unit based on the system offset mean value; and triggering a micro offset early warning according to a judgment result of the system offset mean value, the confidence interval and the fusion early warning score. The present application can effectively identify a micro systematic offset far below a maximum allowable error, scientifically distinguish between equipment failure and individual dispersion of an electric meter, solve the problem that a traditional method cannot find a gradual drift, realize a leap from "threshold management" to "trend management", and provide reliable technical support for predictive maintenance of an electric energy meter calibration device and high-precision metering guarantee.

This invention discloses a supercritical CO2 equipment early warning and self-healing system based on multimodal trend fusion, comprising a sensing and computing layer, a hybrid model fusion diagnostic layer, a multi-level hierarchical self-healing control layer, and a shadow-following evaluation module. The sensing and computing layer collects operational data and constructs a private knowledge base. The hybrid model fusion diagnostic layer constructs a multi-dimensional residual feature space, generates early warning scores, and outputs early warning information. The multi-level hierarchical self-healing control layer executes a three-level strategy, optimizing control parameters through a cost function. The three-level strategy includes performance degradation self-healing, equipment fault self-healing, and emergency protection self-healing. This invention constructs a sensing and computing layer, a hybrid model fusion diagnostic layer, and a multi-level hierarchical self-healing control layer. It generates early warning scores through multi-dimensional residual trend analysis and matching with the private knowledge base, executes a three-level self-healing strategy, and introduces a shadow-following model to quantify the net self-healing benefit and dynamically optimize decision thresholds, thereby achieving early warning prediction for supercritical CO2 equipment.

This application provides a method for early warning and control of microbial contamination in eye drop production, applied in the fields of pharmaceutical engineering and automation control technology. The method includes: collecting droplet sequences from filling needles, extracting necking time deviation, abnormal interface oscillation amplitude, and detachment volume deviation, and encapsulating these as abnormal physical indicators; collecting differential impedance of the high-purity water pipeline wall, the ratio of low-frequency response of adherent particles, and fluid pulsation attenuation to construct a biofilm formation risk index; merging these two data points in time to generate a current contamination risk score; combining wind speed disturbances, pressure differential jumps, and personnel trajectories, locating the contamination source and predicting the microbial diffusion path through reverse flow field tracing and forward simulation; and generating a comprehensive early warning score through spatiotemporal fusion, triggering tiered dynamic control measures such as maintaining production, issuing early warnings, suspending filling, flushing pipelines, regional lockdown, or adjusting airflow. This invention achieves real-time perception, precise source tracing, and proactive prevention and control of microbial contamination.