7results about How to "Improve discrimination accuracy" patented technology

This invention provides a method and system for early warning of debris flow initiation based on dynamic weighting of multiple influencing parameters, belonging to the field of geological disaster prevention and early warning technology. The method includes: constructing a set of multiple influencing parameters by acquiring source conditions, topographic features, and real-time rainfall parameters; constructing an intrinsic weighting function based on the initiation physical mechanism to calculate the intrinsic weights of the source and topographic parameters; adjusting the intrinsic weights using a dynamic correction function with rainfall intensity or cumulative rainfall as input to obtain dynamic weights; establishing a critical function coupling the dynamic weights and critical conditions to calculate the debris flow initiation critical index, and issuing an early warning when the threshold is exceeded. This solves the problem that existing debris flow early warning methods are unable to simultaneously integrate physical mechanisms and dynamic weight adjustment, and accurately reflect the critical state of debris flow initiation under the coupling effect of multiple factors.

The application discloses a multi-modal satire detection method based on a large language model, and comprises the following steps: constructing a large-scale high-quality text data set; constructing a pre-training language model; adopting a supervised fine-tuning strategy to optimize parameters of the pre-training language model, training the pre-training language model through a cross-entropy loss function of self-recurrence language modeling, and obtaining a multi-modal large language model; and inputting the large-scale high-quality text data set into the multi-modal large language model for processing, and obtaining a detection result.

This invention relates to the field of gravity mineral processing technology, and provides a vision-based method for detecting and controlling the slurry flow rate of a spiral concentrator. The method includes: acquiring slurry images at the inlet of the spiral concentrator; extracting the edge geometric features of each slurry image to construct a slurry edge feature image sample set; constructing and training a slurry edge recognition model based on the sample set; identifying the slurry edge position using the recognition model; constructing a geometric relationship model between the slurry edge and the outer edge of the spiral concentrator using the identified slurry edge position; calculating the geometric distance from the slurry edge to the outer edge of the spiral concentrator using the geometric relationship model; determining the slurry flow rate using the geometric distance, and controlling the slurry flow rate. This invention enables visual determination of the slurry flow rate in a spiral concentrator, efficiently performing automatic control of the slurry flow rate without relying on human experience and subjective judgment, thus significantly improving the accuracy of the determination.

This invention discloses a method and system for identifying waste materials based on multimodal fusion, belonging to the field of intelligent waste material identification technology. The method involves acquiring visible light image data, depth structure data, and near-infrared spectral response data of the waste materials to be identified; obtaining candidate regions from the visible light image data and extracting a set of morphological features based on the depth structure data; extracting material reflection curves from the near-infrared spectral response data; extracting texture distribution features and color stability features from the visible light image data and generating a set of structural consistency features based on the morphological features; constructing a fusion feature vector by performing cross-modal association mapping on multiple features and inputting it into a waste material identification model to obtain candidate identification results; identifying key feature subsets through local sensitivity analysis and reconstructing the weights of the fusion features; and performing classification again to output the final identification result. This invention can effectively improve the accuracy and stability of waste material identification in complex recycling scenarios.

This invention discloses a Fe3O4@SiO2 / polyurethane composite photonic crystal sensor, its preparation method, and its applications, belonging to the field of photonic crystal sensor technology. This invention achieves spatial modulation of the C6 fluorescence signal and enhancement of the local photonic field by spectrally matching the reflection band of the structured photonic crystal with the C6 emission band. This results in significant fluorescence quenching response even under extremely low PA concentrations, overcoming the limitation of low sensitivity in traditional fluorescence detection. Compared with traditional HPLC or conventional fluorescence spectroscopy methods, the photonic crystal enhancement system of this invention can improve PA detection sensitivity by approximately 3-4 orders of magnitude, while exhibiting good reproducibility and environmental stability.

The application discloses a kind of capacitive capacity monitoring method and system of flexible direct current converter station bushing, it is related to the field of flexible direct current transmission equipment on-line monitoring and electrical parameter identification technique, including in the bushing of flexible direct current converter station bushing end screen and ground setting capacitor voltage divider and constructing series capacitor measurement structure, acquisition bushing primary voltage signal and end screen current.Primary voltage signal and end screen current are carried out synchronous signal preprocessing, using wavelet transform and filtering strategy to remove noise and extract frequency domain features.Capacitive capacity coupling relationship of bushing structure parameter is calculated based on frequency domain features, and abnormal point is judged by establishing capacitive capacity change identification model and combining temperature and humidity data.The method described in the application effectively overcomes the problems of static analysis lag, single variable misjudgment and insufficient frequency spectrum resolution, realizes real-time monitoring of flexible direct current bushing capacitive capacity, abnormal dynamic early warning and system improvement of model adaptability, and significantly enhances the safety operation guarantee capability of equipment throughout its life cycle.

This invention discloses a two-dimensional heterojunction array sensing method and device for high-precision monitoring of carbon pollution. The two-dimensional heterojunction array sensing method includes: emitting light signals with dynamically changing wavelength, power, and modulation frequency to a two-dimensional heterojunction array using a tunable light source according to a preset switching sequence; acquiring the electrical signals generated by the two-dimensional heterojunction array under the combined action of the target gas and the light signals, and converting the electrical signals into digital signals in multi-dimensional matrix form; and using a hybrid model integrating convolutional neural network branches and recurrent neural network branches to extract spatial pattern features and dynamic temporal features, ultimately outputting the type and concentration of the gas. This invention effectively solves the cross-sensitivity problem in gas identification by exciting the two-dimensional heterojunction array with optical sequence modulation and combining it with a lightweight neural network model, thus improving the identification accuracy and response speed in complex environments.