4results about How to "Able to learn" patented technology

The invention discloses a dynamic sterilization method based on cough audio detection and analysis, which comprises the following steps: acquiring hospital audio, dividing the hospital audio into audio segments, identifying audio frames containing cough audio, and merging the audio frames containing the cough audio to obtain cough audio segments; overlapping coughs are separated, and samples of all the single-sound coughs are obtained and correspond to the individuals to which the single-sound coughs belong; judging whether the individual coughs accidentally or not on the basis of the quantity and the time interval of the single-sound coughs, and further judging whether a hospital environment sterilization early warning program needs to be started or not; and for each environment index of the hospital, presetting a corresponding disinfection trigger threshold, and if it is judged that the hospital environment disinfection early warning program needs to be started and the hospital environment index reaches the corresponding disinfection trigger threshold, starting a corresponding disinfection program to complete disinfection of the hospital environment. According to the invention, 'on-demand disinfection 'can be realized accurately and dynamically in real time, and interference to normal medical activities is reduced to the greatest extent while the transmission path of aerosol in a hospital is blocked.

According to the taper hole caliber measurement method based on the on-machine clamping error suppression, the confidence label attached to the measurement result serves as a decision basis, a grading tool compensation mechanism is established, the limitation that larger errors are possibly introduced under low-quality data through traditional compensation is broken through, and the problem that the efficiency is low due to full-manual intervention is solved. And due to three-gear grading, the system can dynamically adjust the automation degree according to the data credibility, the machining efficiency is improved, the reliability under the abnormal working condition is ensured, and cooperative control over automation and safety is achieved. By continuously utilizing the real processing result to calibrate the evaluation standard, the confidence coefficient model can be self-evolved along with the change of the working condition, so that the whole system has the closed-loop iteration capability of measurement, compensation, learning and optimization, and the long-term operation precision and stability are continuously improved.

A composite wetland water cycle simulation method and system based on digital twinning belong to the field of water conservancy informatization and digital twinning technology. In view of the problem of insufficient accuracy caused by ignoring the dynamic change of vegetation and lacking adaptive calibration in traditional wetland water cycle simulation, the method comprises the following steps: constructing a three-dimensional digital base of the wetland and generating an unstructured grid; deploying multispectral sensing equipment to obtain vegetation spectral characteristics, and inversing leaf area index and average vegetation height, and the collection frequency is dynamically triggered by the digital twinning model; establishing a segmented dynamic mapping of the submerged state of vegetation and the resistance of water flow, dynamically calculating the Manning roughness coefficient and the transpiration loss term; substituting into the shallow water equation set to perform water cycle simulation; comparing the simulated water level with the measured water level, and triggering adaptive calibration when the deviation shows statistical consistency, and using the sequential data assimilation algorithm to update the key weight coefficients of the mapping model online. The system corresponds to realize the above steps. The application improves the accuracy and adaptability of composite wetland water level and flow velocity simulation.

This invention relates to the field of industrial equipment safety monitoring technology, and discloses a data-driven intelligent early warning method and system for the degradation risk of organic heat transfer fluids. The method includes: acquiring multimodal monitoring data of the organic heat transfer fluid, performing preprocessing and feature extraction to obtain entropy yield and acoustic emission activity index; inputting the entropy yield, acoustic emission activity index, and medium quality parameters into a mechanism classification neural network model to obtain the determination probability of the degradation mechanism; according to the determination probability, inputting the feature vector into the corresponding target branch neural network to obtain an instantaneous risk index; predicting the time required for the medium quality parameters and entropy yield to reach a safety threshold, taking the minimum value as the predicted failure time, and calculating the degradation resilience value in conjunction with the planned maintenance time; and determining the comprehensive risk level based on the instantaneous risk index and the degradation resilience value. This solution can achieve early detection of degradation trends and distinguish different degradation mechanisms, improving assessment accuracy and making maintenance strategies more targeted.