9results about How to "Accurate Classification and Identification" patented technology

The invention provides a ginseng age limit identification and detection method and system based on laser-induced breakdown spectroscopy. The ginseng age limit identification and detection method comprises the following steps: collecting ginseng of different age limits as samples; the method comprises the following steps: establishing original spectral data of a sample by utilizing LIBS (Laser-induced Breakdown Spectroscopy), and performing data set division and normalization preprocessing; carrying out feature selection on the preprocessed spectral data by adopting a genetic algorithm GA; processing the feature selection data by adopting different classification algorithms and establishing classification algorithm models; and aiming at the age limit prediction of each classification algorithm model, establishing an integrated classification model based on a voting method, and obtaining an age limit category with the highest probability as a final ginseng age limit prediction result. The method has the advantages of rapidness, no damage, high precision and the like, and can be widely applied to the fields of ginseng quality identification, market supervision and the like.

The application discloses an environmental monitoring data processing method and system based on pollution discharge permission execution report, and belongs to the field of environmental monitoring data processing.The method comprises the following steps: obtaining multi-source environmental data flow for preprocessing and space-time alignment to generate a first data set; extracting a constraint condition field, and instantiating a dynamic quality control rule library based on the constraint condition field and working condition state information; adopting double-layer discrimination logic to process the first data set to generate a second data set after quality control, so as to distinguish between false abnormalities and real emission over-standard caused by equipment drift; taking effective data in the second data set after quality control as input, performing emission accounting operation, and outputting a third data set containing a compliance rate and a compliance margin rate; performing memory mapping corresponding to the third data set and an execution report template, and executing a three-level verification control flow to generate a correction suggestion table based on the verification result.The application reduces the misjudgment rate of abnormal data and improves the accuracy of quality control.

The application relates to a 5G environment intelligent pathological auxiliary diagnosis system and relates to the technical field of pathological section diagnosis, in particular to a 5G environment intelligent pathological auxiliary diagnosis system. The application aims to solve the problem that the generalization performance of an existing AI auxiliary pathological diagnosis model is limited and the system cannot maintain the recognition accuracy of pathological digital images. The 5G environment intelligent pathological auxiliary diagnosis system comprises a CT image acquisition module, a CT image processing module, a deep learning module and a recognition module. The CT image acquisition module is used for acquiring CT images of patients by a CT scanner. The CT image processing module is used for processing the acquired CT images of patients to obtain processed CT images of patients, and the processed CT images of patients are used as a training set. The deep learning module is used for constructing a deep learning model and obtaining a trained deep learning model based on the training set. The recognition module is used for recognizing the processed CT images of patients to be measured based on the trained deep learning model.

The present invention discloses a low signal-to-noise ratio radar radiation source signal pulse identification device and a training and identification method thereof. The radar radiation source signal pulse identification device comprises a data segmentation module; a deep neural network module is arranged at the back end of the data segmentation module; a forward reasoning result storage module is arranged at the back end of the deep neural network module; a fusion reasoning module is arranged at the back end of the forward reasoning result storage module; a feedback training access module is arranged at the back end of the fusion reasoning module; the feedback training access module is connected to the deep neural network module at the back end; and a classification model parameter storage module is also arranged at the back end of the deep neural network module. The device and the training and identification method can not only obtain a good classification model, but also obtain an accurate classification and identification result, and can effectively distinguish the types of radar radiation source signals with low signal-to-noise ratio.

The invention discloses a work site safety detection method, a safety detection system and a safety fence. The method comprises the following steps: using expanded data to train a feature extraction model, using the output of the feature extraction model to train a feature dimension reduction model, using the output of the feature dimension reduction model to train a classifier model, and applying each trained model to construction site detection, wherein the feature extraction model adopts a neural network based on a circular dynamic optimization algorithm, the feature dimension reduction model adopts an autoencoder neural network based on dual loss, and the classifier model adopts an extreme learning machine algorithm based on curvature-aware weight update; the feature extraction model of the invention can adjust the optimization strategy according to different training stages, simulate biological cycles to cope with different data characteristics, and improve the efficiency and accuracy of feature extraction; feature dimension reduction is performed by an autoencoder, and a method based on dual loss is adopted, so that the intrinsic structure of the data can be effectively mined and the representation ability of the reduced-dimensional data can be improved.

The embodiment of the application discloses a control method and device of a fuel cell turbo-compressor. The method comprises the following steps: identifying a current working condition according to an operating parameter; in a slow load reduction working condition, slowly opening a bypass valve, slowly closing a variable nozzle ring, reducing a high-speed drive motor speed and a throttle opening degree, maintaining a compressor outlet flow rate not lower than a minimum safe flow rate, recovering turbine residual pressure energy and motor rotor kinetic energy; in a rapid load reduction working condition, quickly opening the bypass valve, quickly closing the variable nozzle ring, reducing the high-speed drive motor speed and the throttle opening degree, maintaining the compressor outlet flow rate not lower than the minimum safe flow rate, recovering the turbine residual pressure energy and the motor rotor kinetic energy; in an emergency shutdown working condition, fully closing the variable nozzle ring, fully opening the bypass valve, idling the high-speed drive motor, keeping the throttle at a purge opening degree and stopping the system after the purge is completed, so that on-line surge suppression, pressure overshoot control and energy recovery are coordinated, and the system safety and energy utilization rate are improved.

The invention discloses a power distribution network power supply quality analysis method, apparatus and device, and a storage medium. The method comprises the steps of obtaining operation data of a power distribution network; determining the type of a power supply event of the power distribution network according to the operation data; wherein the types of the power supply events of the power distribution network comprise transient state events and steady state events; extracting corresponding features in the operation data, and selecting a plurality of target features for power supply quality analysis of the power distribution network from the extracted features according to the type of the power supply event of the power distribution network; performing empirical mode decomposition on the target features to obtain corresponding optimized feature components; and inputting the optimized characteristic component into a preset power distribution network power supply quality analysis model, so that the power distribution network power supply quality analysis model analyzes the power distribution network power supply quality problem according to the optimized characteristic component, and outputs the corresponding power distribution network power supply quality problem. According to the invention, the accuracy of power distribution network power supply quality problem analysis can be improved.

The present invention belongs to the technical field of audio scene recognition, and provides a method and system for generating a student behavior log based on audio scene recognition. The method includes obtaining a recorded audio document, dividing the audio document into a plurality of audio segments; using a trained educational scene recognition model based on the audio segments to obtain educational scene prediction labels for each audio segment; marking the start time point and the end time point for each audio segment, and generating a time distribution behavior log based on the marked start time point and the end time point of each audio segment combined with the educational scene prediction labels of each audio segment; wherein the process of prediction by the educational scene recognition model includes extracting the spectral features of the audio document and the probability distribution of the theme on the audio words, generating the theme features based on the probability distribution of the theme on the audio words and the spectral features of the audio document, updating the theme features, and classifying based on the updated theme features to obtain the educational scene prediction labels.

The invention discloses a western region typical rock mass recognition and classification method and system based on remote sensing images, and belongs to the technical field of rock mass recognition and classification. The method comprises the following steps: S1, collecting historical remote sensing data of a target area, and preprocessing the historical remote sensing data to obtain identification data; s2, constructing a rock mass recognition and classification model, and training the rock mass recognition and classification model by adopting the recognition data to obtain a final model; and S3, based on the final model, performing identification and classification on the remote sensing data collected in the target area to obtain an identification and classification result. According to the method, rapid and accurate identification and classification of complex geological structures and rock masses in the western region can be realized, and powerful support is provided for geological survey and mineral resource exploration.