5 results about "Frequency matching" patented technology

A computer-implemented method analyzes periodic information in digital vibration data associated with a machine. The method involves generating a spectral periodic information plot (PIP) based on the digital vibration data and locating amplitude peaks in the PIP at frequencies associated with a fundamental frequency of interest. Peaks appearing at the fundamental fault frequency and related harmonic frequencies are removed from the PIP, while retaining the energy values ​​associated with the removed peaks. The remaining peaks in the PIP are classified into synchronous periodic peaks and asynchronous periodic peaks. The remaining peaks in the PIP, along with the fault frequency and related harmonic frequencies, are graphically plotted on a display device using different colors or line styles to identify different frequency groups of interest. The method implements an algorithm that locates peaks in the PIP at frequencies associated with the fundamental frequency of interest, even if the located peak frequencies do not precisely match the fundamental frequency of interest.

The invention discloses a rainstorm risk probability forecasting method fusing multi-source mode and artificial intelligence model rainfall forecasting, and the method comprises the steps: obtaining multi-source rainfall forecasting data which comprises a global numerical mode, a regional numerical mode and ancient artificial intelligence model forecasting; objective correction is carried out on the multi-source rainfall forecast data through intelligent grid algorithms such as a frequency matching method, a sorting mapping method and a neighborhood method; optimizing a Bayesian model average algorithm to generate a gridding rainstorm probability forecast; and calculating a regional rainstorm risk probability value based on the geographic boundary of the target region. According to the method, intelligent grid algorithms such as a frequency matching method, sorting mapping and a neighborhood method are applied to fuse the advantages of multi-source rainfall forecasting, an improved Bayesian model average algorithm is adopted to generate grid probability forecasting, and the regional rainstorm risk probability is calculated based on geographic boundaries. The method is applied to a Shenzhen land-sea integrated meteorological disaster prevention system, the rainstorm probability forecast deviation is reduced by 32% compared with a traditional algorithm, and the disaster prevention decision accuracy is remarkably improved.

The invention provides an intelligent lamp post environment sensor data abnormity self-correction method, and belongs to the technical field of intelligent lamp posts. A sensor data acquisition system is established to carry out real-time sampling and record sensor state parameters; a frequency matching correction algorithm based on simulated sound wave resonance is constructed to convert sensor data into a sound wave signal identification abnormal mode, and a multi-level abnormal data identification mechanism is designed to start processing flows of different levels according to a deviation rate range. Establishing a temperature drift compensation function, combining a polynomial model and a neural network algorithm to realize self-adaptive nonlinear compensation, constructing a sliding window statistical analysis system to monitor the performance degradation trend of the sensor, and executing an abnormal data self-correction processing differentiation strategy; the technical problem that accurate self-correction is difficult to realize due to abnormal data of the multi-type environment sensor array of the intelligent lamp post in a complex working environment is solved.

This application relates to the field of data analysis technology and discloses a data analysis method and system for formaldehyde detectors. The method includes: acquiring temperature and microstructure data of sensor materials during heat treatment, extracting microstructure features, and obtaining initial distribution parameters; optimizing a heat treatment parameter sequence based on the initial distribution parameters; processing the sensor material according to the heat treatment parameter sequence to obtain adjusted microstructure data; extracting resonant frequency-related variables based on the improved heat treatment parameter sequence, simulating frequency matching effects using finite element analysis to obtain a frequency matching model; generating a virtual test scenario based on the frequency matching model, determining whether the sensitivity index exceeds a preset threshold, and determining the optimal sensor configuration; constructing a verification dataset based on the optimized sensor configuration, and obtaining a detection accuracy index using a classification algorithm. This application improves the sensor's response speed, sensitivity, and accuracy.

The application relates to the technical field of Internet, and discloses an Internet application analysis method based on artificial intelligence, which comprises a data acquisition module, a frequency processing module, a combined data generation module, an AI algorithm matching module and an analysis execution module; through the setting of a data calibration end, when real-time analysis of Internet application is carried out, by formulating static and dynamic data sampling frequency standard parameters and setting frequency consistency threshold values for different application scenarios, the synergy of multi-source data acquisition is ensured, and meanwhile, static data flow and dynamic data flow are subjected to real-time frequency matching detection, so that the data sampling frequency misalignment problem can be identified in real time, the accurate alignment of feature dimensions is ensured, the abnormal detection error is reduced, and through the setting of an algorithm adaptation end, the accurate fitting of analysis conclusions and actual scenes is ensured.