5results about How to "Sensitive capture" patented technology

This invention relates to the field of fault early warning technology, and provides a method and system for early warning of faults in large oil-filled equipment, comprising: Step 1, collecting acoustic fingerprint data throughout the entire lifecycle of the large oil-filled equipment and constructing an acoustic fingerprint sample set; Step 2, preprocessing the acoustic fingerprint sample set, extracting acoustic fingerprint feature parameters, and establishing a standardized acoustic fingerprint database; Step 3, constructing a multi-channel deep learning acoustic fingerprint recognition model and a discharge severity assessment model based on deep learning; Step 4, based on the output of the trained acoustic fingerprint recognition model and combined with a preset multi-level early warning threshold system, performing real-time assessment of the operating status of the large oil-filled equipment, and generating corresponding early warning information when the assessment result meets the early warning triggering conditions; Step 5, pushing the early warning information to the operation and maintenance terminal, and automatically generating operation and maintenance suggestions based on the defect type and severity. This invention can effectively provide early warning of faults in large oil-filled equipment.

This invention provides a method and system for determining emergency transport routes. The method includes: acquiring multi-source heterogeneous data of patients to be transported during emergency transport, including vital sign time-series data, transport node location data, and structured medical record data; inputting the multi-source heterogeneous data into a shared latent space projection network to obtain multi-source aligned data with data distribution alignment; inputting the vital sign aligned data from the multi-source aligned data into a reinforcement learning controller to obtain the dynamic weights of each vital sign component in the vital sign aligned data, and determining the disease risk level of the patient to be transported based on the dynamic weights; acquiring a global path planning model trained based on a federated learning framework, and inputting the disease risk level and the multi-source aligned data into the global path planning model to obtain the target transport route. This invention improves the safety and personalization of emergency transport.

This application discloses a PCB board testing mechanism, relating to the field of PCB board testing technology. It includes a main body, a slider, and a through-hole. The main body has a placement groove on its upper side. Two sets of uprights arranged symmetrically around the center are fixedly connected to the upper diagonal of the main body. A lifting groove is formed on the inner side of each upright. The slider is slidably connected to the inner side of the lifting groove. A pressing mechanism is installed on the outside of the uprights, and a detection mechanism is installed on the outside of the slider. The through-hole is located on the upper side of the placement groove, and a heating mechanism is installed inside the main body. In a single operation, this device accurately measures the PCB thickness by pressing down with a pressure block, while simultaneously using hot air heating and real-time monitoring of pressure changes to directly determine its thermal expansion trend, achieving efficient and simultaneous detection of thickness and thermal expansion performance.

The application provides a water conservancy structure leakage monitoring system and method, and the system comprises: an active heating control module, which applies a controllable thermal excitation to a grating array temperature sensing module; the grating array temperature sensing module generates a temperature distribution of a region to be measured, and the temperature distribution comprises a reference temperature distribution before the controllable thermal excitation is applied, a heating temperature distribution during the controllable thermal excitation is applied, and a cooling temperature distribution after the controllable thermal excitation is stopped; a leakage identification module, which determines a temperature-time response curve based on the reference temperature distribution, the heating temperature distribution and the cooling temperature distribution, extracts a temperature rise rate, a maximum temperature rise amplitude, a peak time, a cooling rate and a thermal time constant in the temperature-time response curve, determines a comprehensive discrimination index based on the above parameters, and determines whether the water conservancy structure has leakage based on the comprehensive discrimination index. The application improves the sensitivity and accuracy of leakage monitoring.

This invention discloses a method for predicting LVAD (Large-Invasive Ventricular Assist System) faults using Data Envelopment Analysis (DEA). The method constructs independent decision-making units (DIUs) based on the weekly operational status of the LVAD, acquiring raw multi-dimensional data covering equipment, physiological, and clinical dimensions. After imputation of missing values, correction of outliers, and standardization preprocessing of the raw data, the data is input into a combined model consisting of a Base-Cut-Controlled Decision-Making (BCC) model and a Super-Efficiency DEA model for efficiency calculation. Abnormal decision-making units are initially screened using the BCC model, and the optimal super-efficiency value is calculated using the super-efficiency DEA model. Based on this super-efficiency value and its trend over the monitoring period, envelope analysis is performed using preset two-level warning thresholds to generate graded fault warning signals. Projection analysis is performed on the units that trigger warnings, and the redundancy rate of input indicators is calculated to rank and locate the causes of faults. This invention achieves early and accurate prediction and quantified analysis of LVAD faults.