6results about How to "Strong representative" patented technology

This invention provides an intelligent evaluation method for rockburst hazard at working faces based on principal component analysis-probabilistic neural networks, belonging to the field of rockburst hazard evaluation technology. An evaluation index system is established and data is collected by combining rockburst hazard influencing factors, drill cuttings monitoring, critical stress index monitoring, and actual field conditions. Principal component analysis (PCA) is used to simplify the original evaluation index data, resulting in comprehensive evaluation index data containing information from the original evaluation indicators. The comprehensive evaluation index data is divided into a training set and a test set. A probabilistic neural network (PNN) is used to train the evaluation model on the data in the training set, and then the performance of the evaluation model is tested using data from the test set, and its accuracy is calculated. If the accuracy is greater than or equal to 90%, the evaluation model is considered acceptable; if the accuracy is less than 90%, it is considered unacceptable, and the PNN's smoothing factor needs to be modified and retrained until the accuracy of the evaluation result meets the set value.

This utility model relates to an automatic sampling device for flotation cell slurry, solving the problems of traditional sampling methods that require repeated sampling, are cumbersome to operate, and have low sampling efficiency. It includes a sampling box, a sampling handle, and a suction pipe. The sampling box has a movable cover on top; the upper half of the sampling box is equipped with grids at certain intervals and angles to allow the slurry to enter the sampling spoon from the side along the gaps between the blades; the lower half of the sampling box has intact side walls without openings; the bottom of the sampling box is inclined at a certain angle to the horizontal plane, and a tubular discharge hole is provided at the lowest point of the sampling box. The lower end of the sampling handle is connected to the sampling box, and the sampling handle is engraved with scales to determine the sampling depth. The suction pipe is connected at one end to the discharge hole at the bottom of the sampling box, and the other end is equipped with a pipe switch. This utility model provides accurate and representative sampling; it has a high degree of automation and high sampling efficiency; the device has a simple structure and is easy to operate.

This invention discloses an improved near-infrared spectral wavelength selection and modeling method for artificial bee colony algorithms, comprising the following steps: data preparation, chaotic initialization, fitness evaluation, establishing a PLS model based on wavelength subsets, using the regularized fitness function of RMSECV as the fitness Fitnessxi of nectar source xi; searching for foraging bees; searching for observation bees; searching for scout bees; outputting the optimal wavelength subset Sbest and establishing the final PLS model. The beneficial effects of this invention are: the method effectively improves convergence speed and accuracy; chaotic initialization improves the quality of the initial population, avoiding the algorithm from prematurely falling into local extrema; the adaptive perturbation factor balances the algorithm's global search and local exploitation, improving the ability to find the optimal wavelength combination in high-dimensional discrete space, and the selected wavelength subset has stronger representativeness, resulting in a final PLS model with lower RMSEP (root mean square error of prediction) and better robustness.

This invention discloses a satellite fire detection method based on dynamic exponential features and deep learning. The method includes: acquiring multi-band spatiotemporal observation data from geostationary meteorological satellites; filtering candidate pixels from the full-disk satellite data using multiple threshold conditions; extracting spatiotemporal input features and channel input features of the candidate pixels based on the multi-band spatiotemporal observation data from the geostationary meteorological satellites; and inputting the spatiotemporal input features and channel input features of the candidate pixels into a pre-trained fire spatiotemporal network model to obtain the fire detection result. This invention effectively solves the problem of high false alarms and false negatives caused by cloud cover, high-temperature ground surfaces, and vegetation interference in traditional methods, overcomes the difficulties of weak fire signal extraction and spatial positioning in mixed pixel backgrounds, and significantly improves detection accuracy, stability, and computational efficiency. It can provide key technical support for forest fire monitoring and emergency decision-making.

The application relates to the technical field of optical detection, and discloses a pigskin collagen content detection method. Before contacting a detection medium, the initial optical profile of a pigskin sample is established. According to the profile, a special detection path for the sample is generated, and the moving track and the stay node of the probe sampling are specified. The medium is injected at the starting point of the path, and a synchronization mechanism is started to synchronize the probe movement with the medium diffusion front. When the probe reaches the terminal node, the spectral sequence of each node on the path is recorded completely, and the sequence contains the dynamic characteristic absorption information of collagen. The spectral sequence is subjected to cross-node correlation fusion to generate the fusion spectral feature map of the sample. The map is compared with a pre-stored feature-content mapping model, and the collagen content value is output. The application realizes adaptive sampling and dynamic process tracking, and improves the accuracy and reliability of detection.

This invention discloses a dynamic temperature measurement and compensation method for microwave heating based on multi-source data fusion, belonging to the field of dynamic temperature field monitoring and compensation technology. The method includes the following steps: 1. Using different types of temperature sensors, measuring the dynamic temperature data of materials during the process of multiple parameter changes under microwave heating, and then calculating the first-order difference between adjacent time points; 2. Calculating the proportion of volatility and negative change; 3. Calculating the stability score of temperature measurement data from different sensors, and then calculating the normalized weight of each sensor to ensure that the sum of the weights of multiple sensors is 1; 4. Calculating the weighted fusion temperature as the final temperature; 5. Regression analysis to construct a temperature measurement compensation model corresponding to each temperature sensor. This invention proposes an adaptive weighted fusion algorithm for multi-source temperature sensors based on dynamic stability assessment, solving the problem of limited accuracy of single sensors in existing temperature measurement scenarios, and improving the accuracy and stability of temperature measurement.