30results about How to "Reduce noise interference" patented technology

The invention discloses an ontology recognition method and device oriented to a decision knowledge graph, and the method comprises the steps: carrying out the ontology knowledge extraction of text data of a decision-related field, and obtaining an ontology text set; the ontology text set comprises an attribute text set and a rule text set of each ontology; the attribute text set comprises various attribute texts; the rule text set comprises various rule texts; preprocessing the ontology text set to obtain a preprocessed text data set; performing ontology type identification processing on the text data set of each ontology in the preprocessed text data set to obtain a type identification result of each ontology; the type of the ontology comprises a fact type ontology, a rule type ontology and a decision type ontology.

This invention provides a method for predicting the health trend of hydropower units based on the TCN-BiLSTM-Attention model, relating to the field of hydropower generator sets. The method involves: S1: acquiring the original vibration signal of the hydropower generator set and denoising the original vibration signal using a TTAO-VMD-EWT joint denoising model; S2: obtaining multiple operating state parameters of the hydropower generator set and calculating the Pearson correlation coefficient, Spearman correlation coefficient, maximum information coefficient, and distance correlation coefficient between these parameters and the vibration signal; S3: calculating the fusion evaluation index of each operating state parameter and the vibration signal, selecting key features, and constructing a multi-dimensional feature vector together with the reconstructed vibration signal; S4: constructing a TCN-BiLSTM-Attention prediction model to predict and output the predicted trend of the hydropower generator set's vibration signal. This method improves prediction accuracy, increases prediction efficiency, and enhances the accuracy, stability, and timeliness of hydropower generator set vibration trend prediction.

This invention discloses a drug target affinity prediction method that integrates PPI quality and uncertainty. The method constructs a drug molecule map and a multimodal protein structure representation, and extracts multi-source features by combining the local PPI sub-map of the target protein. By calculating the protein's low-frequency level, prediction uncertainty, and PPI quality, a PPI quality-aware gating factor is generated to adaptively adjust the PPI information injection intensity, and a residual enhancement strategy is used to preserve the original protein features. Subsequently, the drug representation and the enhanced protein representation are fused using adaptive gating, and the result is input into a prediction network to output the drug-target affinity. This method effectively integrates protein function and interaction information, improves the prediction stability of low-frequency proteins and the model's generalization ability, and provides an accurate and reliable computational tool for drug screening and candidate molecule selection.

The application discloses a kind of image recognition method and system under complex illumination environment, the method includes: obtaining the image to be identified needing to carry out image recognition, the illumination influence area is obtained by analyzing the image to be identified, and the illumination influence area is divided into a plurality of grid units according to pre-set rule by grid division;Respectively, randomly select pixel points from illumination influence area and non-illumination influence area, and obtain corresponding pixel block based on pre-set window size;The similarity weight between the pixel block of illumination influence area and non-illumination influence area is calculated respectively, to denoise the image to be identified, and the denoising identification image is input into pre-set image recognition model to obtain corresponding image recognition result.The application solves the problem of low image recognition efficiency in the prior art under complex illumination environment.

This invention discloses an entity relationship recognition method and apparatus for decision knowledge graphs. The method includes: retrieving a set of decision text information from a decision knowledge text database; preprocessing the set of decision text information to obtain a preprocessed dataset; and performing entity relationship recognition processing on the preprocessed dataset to obtain an entity relationship information set.

This invention provides a magnetic detection device based on diamond NV centers and the Kerr effect. It achieves the sharing of light sources and optical paths between magnetic detection technologies based on the magneto-optical Kerr effect and those based on NV centers, combining the two technologies and greatly simplifying the device structure. This increases the intensity of the optical signal received by the detection module, reduces noise interference in the signal, and thus improves the accuracy of magnetic detection. It can simultaneously meet the requirements of rapid and high-resolution detection, and improve the overall detection speed while ensuring detection accuracy.

This invention provides a lithium carbonate price prediction method based on multimodal data collaborative driving, comprising the following steps: multimodal data source identification and data acquisition; multimodal data classification and standardization; personalized noise filtering and outlier correction of multimodal data; extraction of trend and correlation features from structured data; extraction of semantic and visual / speech features from unstructured data; extraction of key information and feature quantification from semi-structured data; weighted collaborative fusion and feature optimization of multimodal features; construction and training of a two-branch collaborative prediction model; model prediction and preliminary verification of prediction results; optimization and dynamic correction of prediction results; interpretability analysis and visualization of prediction results; and method effectiveness verification and continuous improvement. This method, based on multimodal data collaborative driving, addresses the pain points of existing lithium carbonate price prediction technologies, forming a comprehensive, high-precision, and highly practical prediction solution.

This invention relates to big data processing technology, and particularly to a method for detecting software that maliciously acquires computing resources based on big data. The method includes: acquiring the executable file of the target software and constructing graph data from this data; filtering instruction segments from the graph data and extracting context features from the filtered instruction segments using a Transformer encoder; using the context features as initial features for nodes and a graph attention network to aggregate node features to obtain static features; acquiring the protocol fingerprint and traffic statistics features of the target software and extracting dynamic features from the acquired features using a bidirectional LSTM network; fusing the static and dynamic features through an attention mechanism, and inputting the fused features into a random forest classifier to detect whether the target software is maliciously acquiring computing resources. This invention comprehensively captures both static and dynamic behavioral features, overcoming the limitations of traditional single-feature detection.

This invention relates to the field of cable defect identification technology, solving the technical problems of low accuracy in cable defect identification caused by gradient diffusion and feature degradation in existing deep networks, which lead to loss of edge details and difficulty in effectively extracting true weak thermal deformation features. Specifically, it relates to a method for identifying surface thermal deformation defects in extruded insulated low-voltage fire-resistant cables. The method acquires a visual image of the cable surface in the extrusion cooling state and a one-dimensional temporal physical signal, preprocesses the surface visual image to obtain a standard image, and uses a weighted gradient edge detection algorithm to construct templates in multiple directions to perform edge detection on the standard image. This invention reduces the probability of network parameter combinations getting trapped in local optima during model training, lowers false positive and false negative rates, improves the classification accuracy of cable surface thermal deformation defects, and meets the high-precision and high-robust defect detection requirements of extruded insulated low-voltage fire-resistant cables.

This invention relates to the field of medical technology, specifically to a method for monitoring anesthesia depth based on a neural network model. It includes the following steps: collecting physiological indicator data of patients under different degrees of anesthesia; preprocessing the physiological indicator data to obtain interference-free physiological indicator data; and transmitting the processed physiological indicator data to a data management system; constructing a neural network model; inputting the physiological indicator data into the neural network model through the data management system; and training the neural network model. This invention, by collecting physiological indicator data of patients under different degrees of anesthesia to establish a training dataset, and training the neural network model, enables the neural network to accurately predict the patient's anesthesia depth. This method of collecting and processing the training dataset provides sufficient data samples, allowing the neural network model to be trained on diverse data, thus improving the model's generalization ability.

The present application belongs to the technical field of soil compaction degree prediction, and particularly relates to a soil compaction quality prediction method, medium and terminal based on image recognition, comprising the following steps: S10, collecting data of soil compaction degree and soil surface compaction pictures of sand under different conditions; S20, preprocessing the collected pictures, including picture normalization, grayscale and binarization processing, and constructing a data set; S30, constructing an improved CNN neural network model based on the preprocessed data set; S40, importing the data set into the improved CNN neural network model for pre-training to obtain a pre-training model; S50, importing the collected pictures into the pre-training model, and performing freezing and fine-tuning on the pre-training model to obtain a sand compaction quality prediction model; and S60, importing the preprocessed surface compaction pictures of the sand to be detected into the prediction model to output a compaction degree prediction value. The present application has simple process and convenient operation, the obtained prediction model has strong robustness and generalization ability, and the prediction efficiency and accuracy of sand compaction quality are high.

This invention relates to a dynamic retrieval enhancement generation method based on critical path entropy increase trends and document selection. Addressing the issues of retrieval response lag and false triggering caused by single-point threshold triggering in existing technologies, as well as noise interference and resource waste caused by direct splicing of Top-K retrieval documents, this invention creatively proposes a critical path entropy increase trend triggering mechanism. It optimizes single-point entropy monitoring of all tokens to critical path token entropy increase trend monitoring, achieving on-demand retrieval triggering by dynamically determining uncertainty increments, thereby suppressing false triggering caused by noise fluctuations in non-critical locations and alleviating retrieval response lag. Furthermore, this invention proposes a document selection model based on direct preference optimization of DPO, optimizing the coarse-grained injection of Top-K documents into a fine-grained filtering and injection mechanism. By filtering out irrelevant and redundant documents, it reduces the invalid occupation of the context window, thereby solving the problems of reduced generation quality and wasted computational resources caused by noise interference.

This invention belongs to the interdisciplinary field of bioinformatics and artificial intelligence, specifically involving a multi-perspective hybrid expert protein function prediction method based on cross-attention. The method includes: multi-source data preprocessing and feature construction, multi-perspective expert feature extraction, cross-attention routing fusion, and functional classification prediction. First, this method integrates pre-trained sequence features, secondary structure physicochemical features, and protein topology features to construct a multi-source input. Second, it designs a parallel hybrid expert network to extract deep features from three perspectives: sequence semantic evolution, local amino acid physicochemical environment, and global protein topology. Subsequently, a cross-attention router with sequence features as the query vector is introduced to dynamically calculate the contribution weights of each expert network to different samples, achieving adaptive fusion and denoising of multi-perspective information. Finally, the outputs of each expert are weighted and summed based on dynamic weights to achieve high-precision and robust protein function prediction.

This invention provides a multi-channel receiving method, apparatus, storage medium, and electronic device for ultrasound imaging. The method includes: S1, controlling the multi-channel transmission signals on the channel device; S2, controlling each element on the probe to receive signals transmitted by at least two channels on the channel device; S3, filtering noise from the signals transmitted by at least two channels on the channel device received by each element to obtain effective signals; and S4, performing ultrasound imaging based on the effective signals. The multi-channel receiving method, apparatus, storage medium, and electronic device for ultrasound imaging of this invention utilize multi-channel transmission and single-channel reception, and perform noise filtering to effectively eliminate the influence of noise and improve the signal-to-noise ratio.

The application discloses a data flow transfer path extraction method and device, equipment and a storage medium, and relates to the field of databases, and comprises the following steps: obtaining target log data, and determining corresponding target asset data based on the target log data; the target log data comprises first log data and second log data; the target asset data comprises database asset data and API asset data; the target asset data is processed to obtain corresponding target access features, and a target clustering algorithm is used to cluster the target access features to obtain corresponding target clustering results; target correlation degrees corresponding to the first log data and the second log data are determined based on a target matching rule and the target clustering results, and a target data flow transfer path is determined based on the target correlation degrees and a correlation degree threshold. The application realizes the construction of a data flow transfer path around a database and an API.

This application provides a method and system for compensating for clock drift in electricity meters based on model predictive control. The method involves setting specific fields and assigning initial values ​​to time data packets transmitted bidirectionally between the electricity meter and a time server. As the time data packets pass through each router, the effective synchronization data packet and the final value of the specific field are determined based on the current value of the specific field and the router's waiting queue length. The asymmetry coefficient of the bidirectional transmission delay between the electricity meter and the time server is determined using the effective synchronization data packet, the initial value, and the final value. The clock drift amount of the electricity meter is predicted based on the meter's clock drift prediction model, the effective synchronization data packet, and the asymmetry coefficient. The local clock of the electricity meter is then compensated and corrected in real time based on the clock drift amount. Using the scheme of this application, clock drift in the electricity meter can be compensated based on the bidirectional delay difference.

This invention discloses a pre-trained online Gaussian process device and method for predicting hydrogen fuel cell voltage, applicable to marine propulsion systems. Addressing the problems of traditional models relying on real-ship data, weak generalization ability, and inability to adapt to battery aging, the device includes a sensor module, a memory, and a processor. The sensor module collects pressure, current, temperature, and voltage data; the memory stores the model and program; and the processor performs data processing and prediction calculations. The method pre-trains a Gaussian process regression model using experimental data, updates it online according to preset rules using real-time ship data, and outputs the prediction results after data preprocessing, model construction, updating, and prediction. This invention achieves high-precision prediction in zero-sample / few-sample stages, dynamically adapts to complex operating conditions and battery aging, operates efficiently without redundancy, and ensures reliable operation of marine hydrogen fuel cell propulsion systems.

The utility model discloses a deep foundation pit dewatering water level monitoring device based on self -calibration feedback formula deep foundation pit water level sensor belongs to deep foundation pit construction monitoring technical field, and it is high -precision, anti -interference's water level real -time monitoring in the application of deep foundation pit dewatering engineering.

The application discloses an analysis method, system and device based on cancer cross-modal data and a medium, and comprises the following steps: in response to receiving an analysis request input by a user, inputting the analysis request to a first large model, determining an analysis task and an analysis tool for executing the analysis task; in the case where the analysis task comprises a target analysis task, extracting information from clinical data input by the user by using an information extraction engine to obtain object basic data; obtaining a gene expression matrix formed by cancer transcriptome data after dimension reduction based on the object basic data by using a wisdom algorithm contraction engine; performing survival analysis on the gene expression matrix after dimension reduction by using a risk mapping engine to obtain a survival analysis result and a target gene set affecting survival; and checking the target gene set based on reference data by using an arbitration evaluation engine in the analysis tool to obtain a checking result.

The application provides a question and answer method and device based on knowledge graph retrieval and a large language model. The question and answer method comprises: in response to receiving natural language question information, using a knowledge graph retrieval system to obtain a corresponding candidate path set; using a large language model to coarsely filter and finely filter the candidate path set to obtain a coarsely filtered path set and a target path set and construct a structure prompt instruction, to obtain a first answer result output by the knowledge graph retrieval system and a second answer result output by the large language model, respectively, and to calculate the corresponding confidence values, respectively, to screen and correspondingly fuse the first answer result and the second answer result according to the confidence values, respectively, to generate a target answer result corresponding to the natural language question information. Through the above method, the accuracy of answer generation of the large language model in the question and answer reasoning task is improved, and the stability and performance of the large language model based on knowledge graph retrieval are improved.

This invention relates to the field of mold price prediction technology, and in particular to a mold price prediction method based on multi-dimensional data fusion. A hybrid neural network model combining Transformer and LSTM is constructed. The global attention mechanism of the Transformer module is used to deeply mine the complex relationships between high-dimensional static features, while the LSTM module captures the temporal evolution patterns of historical prices. The two types of features are effectively fused, significantly improving the accuracy and stability of mold price prediction. Through a pre-processing layer and attention heads that process features of specific categories, the model is guided to perform structured modeling based on feature categories in the early stages of learning. This effectively distinguishes and focuses on the dependencies within different categories of features, such as static attributes, process attributes, and temporal costs, reducing noise interference between irrelevant features and improving the interpretability, efficiency, and generalization ability of the model's feature association learning.

The application discloses a semantic deviation evolution prediction method and system, and belongs to the technical field of natural language processing and artificial intelligence. The method maps input data to a unified semantic vector space, constructs a literal semantic vector, determines a standard context based on historical corpus and context conditions, and generates a corresponding context intention semantic vector. The semantic deviation between the literal semantic vector and the context intention semantic vector is calculated, and an initial judgment of semantic deviation triggering state is made by using a judgment model trained by contrast learning. For samples judged as semantic deviation, a predictive generation reasoning is performed under the semantic consistency constraint, a potential semantic evolution path is simulated and generated, and a semantic evolution distribution stability is checked. Finally, the semantic deviation, evolution confidence ranking result and context construction basis are output, realizing quantitative judgment, evolution prediction and explainable analysis of semantic deviation.

The application discloses a kind of based on difficulty perception's collaborative knowledge tracking method, belong to knowledge tracking technical field.The application includes: obtaining the difficulty parameter of each question in student historical answer data, and the difficulty parameter is standardized processing;Based on the historical answer of student and difficulty information, construct difficulty perception behavior characteristics;Calculate the similarity between target student and other students, and filter out the student similar to target student in difficulty perception behavior characteristics;According to the answer data of the similar student and the difficulty information of the target question, collaborative information screening is carried out;Based on the screened collaborative information, the learning state of target student is predicted.The method flow of the application is clear, easy to implement, can be directly deployed in existing online learning system, improves the overall performance of knowledge state prediction under the premise of not increasing additional hardware cost.

This application provides a method, electronic device, and storage medium for predicting the peak discharge of a dam breach, relating to the field of water conservancy engineering technology. The method includes: acquiring event data from multiple historical dam breach events; selecting at least one dam breach discrimination factor from multiple candidate influencing factors and constructing an initial prediction model based on the dam breach discrimination factor; obtaining multiple sets of experimental data based on the historical dam breach event data, and solving the initial prediction model based on the multiple sets of experimental data to obtain a target prediction model; acquiring each dam breach discrimination factor of the target dam to be predicted, and predicting the peak discharge of the target dam breach based on the target prediction model and the dam breach discrimination factors, thus improving the accuracy and reliability of peak discharge prediction; and using the target prediction model and the measured values ​​of the dam breach discrimination factors to predict the peak discharge of the target dam breach, achieving automated and intelligent risk prediction.

The application discloses an adaptive drug repositioning method based on feature-driven isomorphic graph learning, relates to the technical fields of drug repositioning and graph neural networks, and comprises the following steps: acquiring drug-disease association data and corresponding graph network data information; initializing feature embedding of disease nodes and drug nodes according to the drug-disease association data and the corresponding graph network data information, and constructing a heterogeneous graph and an isomorphic graph; performing a heterogeneous graph convolution operation through disease-to-drug direction and drug-to-disease direction propagation, and calculating the importance weight between the disease nodes and the drug nodes by using an attention mechanism to generate heterogeneous information; adaptively weighting the edges in the isomorphic graph through a learnable parameter mask, controlling the binarization degree of the weight through L1 regularization and entropy regularization, performing an isomorphic graph convolution operation, and generating isomorphic information. The application effectively reduces the noise interference in the pre-defined graph by adaptively weighting the edges in the isomorphic graph through the learnable parameter mask.

This invention relates to a method for rapidly identifying the heat tolerance of rice parental materials based on the high-temperature tolerance characteristics of humidity-sensitive male-sterile hybrid combinations. The method involves using a humidity-sensitive male-sterile line as the female parent and a candidate restorer line as the male parent to create a hybrid combination. The resulting F1 generation seeds are sown and cultured to the reproductive growth stage, then subjected to high-temperature stress treatment. A relative heat tolerance coefficient is calculated based on the seed setting rate data of the F1 generation under high temperature. Based on the correlation between the relative heat tolerance coefficient and the male parent material, the heat tolerance level of the male parent material is determined. This invention utilizes hybrid rice materials bred from humidity-sensitive male-sterile lines and their corresponding male parent materials to identify heat-tolerant rice varieties through staggered sowing in the field. The key indicator of seed setting rate under high-temperature stress shows a significant positive correlation with the seed setting rate and high-temperature tolerance characteristics of the male parent material, indicating that hybrids with this special genetic background can act as "sensors" to accurately reflect the heat tolerance characteristics of the male parent.

The application relates to a target identification method and device in a complex electromagnetic environment, which comprises the following steps: carrying out denoising processing on a target signal to be identified by using a wavelet packet threshold value to obtain a reconstructed signal; identifying the reconstructed signal according to a frequency domain self-adaptive amplitude modulation to obtain a signal type of the target signal; obtaining a corresponding radiation source individual of the target signal by using a pre-trained support vector machine; and obtaining an identification result of the target signal according to the signal type of the target signal and the corresponding radiation source individual. Through the above technical scheme, the wavelet packet threshold value is used for denoising, the noise interference is significantly reduced under a low signal-to-noise ratio, the effective part of the target signal is improved, the reconstructed signal is identified according to the frequency domain self-adaptive amplitude modulation, so that the signal type is obtained, the calculation time can be reduced, the target signal has a high identification rate, and the pre-trained support vector machine is used for obtaining the radiation source individual, so that the identification of the corresponding radiation source individual of the target signal can be efficiently completed.

This application discloses a method for determining developmental trajectories based on single-cell multi-omics clustering, belonging to the field of biomedical data mining technology. The method includes: acquiring multi-omics data of single cells from the same tissue; determining the potential representation of each single cell in different omics based on feature encoding technology; constructing a K-nearest neighbor graph for each omics based on the distance between single cells; and determining the corresponding multi-order similarity matrix; using the multi-order similarity matrix to complete the missing potential representations of single cells, obtaining the complete potential representation of each omics; performing cluster analysis on each omics to obtain single-cell clustering results; weighted fusion of the potential representations of each single cell in different omics to obtain a comprehensive potential representation; and analyzing the developmental trajectory of single cells based on the comprehensive potential representation. This application can stably and accurately cluster single cells under conditions of missing single-cell omics or significant differences in omics quality, thereby accurately determining the developmental trajectory of single cells.