5results about How to "Efficient early warning" patented technology

The application discloses a sintering machine large flue oxygen content detection method and system, and belongs to the technical field of sintering machine flue gas detection. The method comprises the following steps: determining the airflow stability of the detection area of the sintering machine large flue; in the detection area of the sintering machine large flue, the oxygen content is detected by using a horizontal river gas detection device to obtain initial oxygen content detection data; the initial oxygen content detection data is subjected to outlier rejection and data calibration to obtain accurate oxygen content data, if the accurate oxygen content data exceeds the preset normal oxygen content range, a warning mechanism is triggered and the next step is executed; the sampling position and detection parameters of the horizontal river gas detection device are adjusted and the oxygen content detection is re-executed until the accurate oxygen content data is stably fed back or the abnormal reason is confirmed and the detection is actively stopped. The performance advantage of the horizontal river gas detection device is fully exerted, accurate and real-time detection of the flue oxygen content is realized, and stable and efficient operation of the sintering production is ensured.

The invention discloses an intelligent acoustic positioning system for sick pigs in a hog house, and relates to the field of sound recognition and positioning. The problems that an existing sick pig acoustic positioning method is poor in voice recognition accuracy and large in positioning error in a complex acoustic environment are solved. According to the invention, a time difference and phase difference complementary positioning principle is adopted, and an adaptive beam forming technology is combined, so that the problem of inaccurate sound source positioning in a pig house environment with strong reflection and high noise is solved, multi-feature extraction and intelligent diagnosis are carried out on enhanced sound data through the sick pig sound recognition unit, and the recognition accuracy is improved. The method is mainly used for positioning sick pigs in a complex breeding environment.

This disclosure relates to a full-process processing method for fiber optic composite ground wires, including: a design stage where a design database is established, the structure and processing technology of the fiber optic composite ground wire are determined based on the database, and auxiliary optimization design is performed; a manufacturing stage where multiple attribute parameters of the fiber optic composite ground wire are monitored to improve quality; an operation stage where the fiber optic composite ground wire is monitored under extreme low-temperature conditions to obtain initial data; the original health indicators are input into a pre-trained health and status recognition model to obtain the status recognition results of the fiber optic composite ground wire; and an early warning is issued based on the status recognition results. The technical solution of this application improves design quality, provides early warning during the operation stage, and is beneficial for extending the lifespan of the fiber optic composite ground wire.

This invention relates to the field of blockchain and supply chain finance risk control technology, specifically to an AI smart contract risk assessment method for supply chain finance. The method includes: collecting multi-source circulation status data and external intervention signals from the supply chain finance network within a preset time window; fusing time-series features to obtain a node behavior feature sequence; inputting this sequence into a dynamic graph neural network model to output network state trust entropy; and calculating the probability distance between the network state trust entropy and the trust entropy distribution that triggers a cascading blocking event in the smart contract as a critical distance; based on the network state trust entropy and critical distance, calculating the system survival probability index and circulation efficiency index using a dynamic risk hedging model to determine the comprehensive risk assessment state and locate the target node; and triggering a smart contract based on the comprehensive risk assessment state to perform rigid resource recovery or flexible resource compensation on the target node, thereby achieving trust reconstruction. This invention achieves efficient early warning and accurate network-level assessment of cascading blocking events in the capital chain.

This invention relates to the field of mine safety monitoring technology, specifically to a fault activation characteristic monitoring method based on multimodal fusion of disaster precursor information, comprising: sample preparation; system construction; synchronous data acquisition; based on the multi-source data acquired in S3, feature vectors are extracted, and a data fusion algorithm is used to identify the initiation, expansion, and penetration stages of fault activation; based on the feature vectors extracted in S4, normalization processing is performed to construct a fault activation index calculation model, and multi-level early warning thresholds are set according to the FAI value range and combined with the characteristic trend slope; a multimodal fault activation early warning mechanism is established, and the reliability of the early warning results in S5 is verified and presented in multiple dimensions; this application constructs a three-in-one multidimensional monitoring system of acoustic emission, digital image, and parallel electrical resistivity, which synchronously acquires multi-source data such as fracture spatial location, surface strain field, resistivity change, and fracture number, making up for the shortcomings of traditional single monitoring methods that are difficult to comprehensively characterize the complex process of fault activation.