15results about How to "Reduce redundant information" patented technology

This invention discloses a method for multi-source data fusion and power prediction of wind turbine units, belonging to the field of power prediction technology. It addresses the technical problems of poor multi-source data fusion and power prediction analysis in existing solutions. By filtering and retaining key features through mutual information, redundant information can be effectively reduced. Dynamic weighting via an attention mechanism makes the fused features more adaptable to real-time operating conditions. Differential weight adjustments for wind speed, terrain, and equipment status ensure high relevance of the fused features even in complex scenarios. Spatial feature extraction captures local spatial correlations, temporal features capture temporal dependencies, and random forests handle nonlinear mappings, solving the problem of insufficient generalization ability of single models. The attention mechanism automatically assigns weights to different time steps and features, avoiding complex mathematical processes such as matrix operations and noise covariance estimation.

The application provides a model propagation method and device based on semantic ability, comprising: taking a node needing to update a model and an algorithm as a sending end, and broadcasting original semantic information thereof; each receiving end searches for similar information from a local knowledge base, if the similar information can be found, the original semantic information is input into a model of the receiving end for recovery, recovered semantic information is output and fed back to the sending end, otherwise the original semantic information is directly ignored; the sending end selects recovered semantic information closest to the original semantic information according to a preset evaluation standard, and takes a receiving end corresponding to the recovered semantic information as an optimal receiving end, and the sending end sends a model propagation request to the optimal receiving end; and the optimal receiving end broadcasts a model thereof to the network, and updates the model and the algorithm of other nodes in the network. The method provided by the application can update and optimize a node with long-term un-updated semantic ability, low efficiency and unable to meet user demand in time, and balance semantic ability among nodes in the network.

The application provides a train warehouse entering control method and device, electronic equipment and storage medium, and belongs to the technical field of rail transit. The method comprises the following steps: in the case that a train management center (TMC) and an object controller (OC) establish a communication connection, determining a target warehouse into which a train enters based on the train management center (TMC) and the object controller (OC), and controlling the operation of a signal device on a route of the train to the target warehouse; acquiring the state of the signal device; and in the case that the signal device is in a passing state, controlling the train to enter the target warehouse. The application can save redundant information of interlocking system interaction, improve the efficiency of train warehouse entering control, meanwhile, without the intervention of dispatchers, the labor cost is reduced, and the intelligent level of train warehouse entering control is further improved.

The application discloses a hybrid automatic repeat request feedback processing method and device and a terminal, and belongs to the technical field of communication. The hybrid automatic repeat request feedback processing method comprises the following steps: in the case that a first hybrid automatic repeat request feedback (HARQ-ACK) and a second HARQ-ACK are in one time unit, and a codebook used by the first HARQ-ACK is a first codebook, a terminal generates HARQ-ACK information of the first HARQ-ACK and the second HARQ-ACK according to the first codebook, or generates HARQ-ACK information according to the priority of the first HARQ-ACK and the priority of the second HARQ-ACK.

The application belongs to the technical field of aviation strength test, and particularly relates to an acoustic emission data preprocessing method and device for removing waveforms. The method comprises the following steps: step one, obtaining acoustic emission data files, selecting acoustic emission data files to be preprocessed from the acoustic emission data files, and loading configuration parameters in the acoustic emission data files to be preprocessed; step two, opening a memory space for storing processed data; step three, based on the configuration parameters, the acoustic emission data files to be preprocessed are read in a loop, it is judged whether the acoustic emission data files read in each loop contain waveform information, if yes, the next loop is directly continued, if not, the acoustic emission data file read in the current loop is stored in the memory space and the next loop is continued, until the number of the acoustic emission data files to be preprocessed is zero. The application improves the analysis quality and efficiency of acoustic emission data, and promotes the application of acoustic emission monitoring technology in full-machine fatigue test.

The invention provides a visualization method and system for self-adaptive matching of a display object resolution and a fusion processing mode. The method provided by the invention comprises the following steps: before to-be-displayed content reaches a video physical port of a visualization system, determining a display template according to a plurality of to-be-controlled visualization system display sizes; selecting a target visualization system according to the content density of the to-be-displayed content; determining a display object type in the to-be-displayed content, determining a fusion processing mode of the display object according to the display object type, determining a fused display position of each display object according to the display scene, supplementing a display template corresponding to the target visualization system, and obtaining a target display template; different types of display objects are obtained by utilizing a video physical port, each display object is processed by utilizing a video processor according to a fusion processing mode of each display object, and a plurality of processed to-be-fused display objects are displayed in the same canvas according to a fused display position in a target display template to obtain a fused canvas; and displaying in the target visualization system.

This invention discloses a method for rapid prediction of data center temperature fields based on convolutional neural networks, comprising: acquiring the geometric information of the data center structure; performing geometric modeling and mesh generation on the data center structure to obtain the mesh structure of the data center; acquiring the operating parameters of the equipment within the data center; acquiring the temperature field data of the data center and dividing the temperature field data according to a height plane to generate the output dataset for training the convolutional neural network model; converting the mesh structure of the data center at different heights into a spatial feature map structure and recording the corresponding height information and operating parameter information to generate the input dataset for training the convolutional neural network model; and using the trained convolutional neural network model for temperature field prediction. This method can achieve accurate and reliable rapid prediction of the temperature field of data center rooms.

The application relates to a ciphertext retrieval method for a localization database, which is based on hierarchical security targets of covering mode leakage and actual operation conditions, and is designed under the guidance of strong backward security to support an index structure with an immediate deletion function and a searchable encryption scheme meeting the strong backward security, and a trusted execution environment is applied to improve the scheme performance. The application effectively supports the immediate deletion of index information and document information, reduces the redundant information on the server, in the scheme design aspect, the trusted execution environment is adopted to optimize the searchable encryption scheme performance, reduce the client storage, and greatly improve the ciphertext retrieval efficiency.

The embodiment of the invention provides a data verification method and device, electronic equipment, a storage medium and a program product. Comprising the steps that state message information currently sent by a sending end is received and stored, a first verification data packet currently sent by the sending end is received, and the first verification data packet comprises transmission data, first verification data and target data; the target data comprises first low-order data of a first preset digit in the first counting data and second low-order data of a second preset digit in the second counting data. And determining target step data according to the first low-order data and stored first counting data, and determining second verification data according to the target step data, the first counting data, the second counting data and the first verification data packet. And if the second verification data is the same as the first verification data, the verification of the state message information is passed. According to the invention, the data processing amount in the verification calculation process is reduced, so that the communication resources occupied in the verification process are reduced.

The invention discloses a group behavior recognition method based on a Markov decision process and extension graph convolution, and relates to the technical field of computers. The method comprises the following steps: extracting a key frame of a video to be identified based on a Markov decision process, and sequentially extracting a convolution feature map of the key frame, bounding boxes of a plurality of target individuals in the convolution feature map and behavior features of each target individual; in each layer of graph convolutional network, calculating a cosine distance between behavior characteristics of a node and an adjacent node in the human body target relation graph so as to determine a connection weight and update the behavior characteristics of the node; calculating a relation feature vector between any two nodes based on the behavior features obtained by the last layer of graph convolutional network, and dividing a plurality of sub-target interaction graphs; classifying each sub-target interaction diagram to obtain a predicted behavior label; and determining the predicted behavior tag of the subgroup with the highest occurrence probability in all the key frames as a group behavior tag. According to the method, the group behavior identification precision is improved.

The application discloses a multi-modal emotion recognition method based on intra-modal perception and inter-modal cross fusion of a Transformer mode, and steps include the following: firstly, encoding and extracting deep features of speech and text; then, based on the intra-modal perception module of the Transformer proposed by the application, long-distance dependency relationships in each mode are captured, local perception learning of emotion features is realized, and redundant information in the deep features is reduced; secondly, in order to fuse unaligned multi-modal sequence information and fully utilize the complementarity of different modal information, the inter-modal interactive fusion module based on the Transformer is proposed to capture information dependency relationships between different modes and obtain fused multi-modal global information; finally, an ablation experiment is conducted to verify the effectiveness of the method. The application realizes effective parallel computing for multi-modal emotion recognition, and further improves the recognition performance and generalization ability of a multi-modal emotion recognition system.

The invention provides a test method for predicting the Bug number of a software version based on a neural network, and belongs to the technical field of software engineering and software test management. Indexes are extracted from historical data such as demand scale and complexity, code change frequency, development cycle, test resource investment, test personnel experience and coverage, and a training sample is generated in combination with multi-platform data association and field aperture unification; the method comprises the following steps of: performing normalization and PCA dimension reduction on a sample, dividing a training set and a test set, constructing a neural network containing ReLU and Dropout, and performing parameter adjustment training with EarlyStopping and ReduceLROnPlatau through K-fold cross validation; features are generated for new iteration according to the same preprocessing process, the number of predicted defects is output, and a basis is provided for testing resource configuration and use case priority in cooperation with a Web interface.

The application relates to the technical field of battery monitoring, in particular to a deep learning method and device for predicting cell failure in a BMS. The method comprises the following steps: collecting multi-modal operation data of the cell and dividing the data into time sequence samples; extracting time domain features, frequency domain features and differential features of the time sequence samples, selecting the features by using a Pearson correlation coefficient and mutual information, reducing the dimensions by principal component analysis, and constructing a data set; constructing a cell failure prediction model based on a gated recurrent unit model, optimizing the performance of the model by combining an attention mechanism; on the basis of predicting the cell failure, predicting the battery health state and failure time, introducing an adaptive failure threshold mechanism, and dynamically adjusting the failure judgment standard; constructing an incremental learning framework based on sample selection, and dynamically adjusting the prediction model by using new working condition samples. The method can significantly improve the accuracy, robustness and adaptability to complex working conditions of the cell failure prediction, and provides more reliable decision support for the battery management system.

The invention relates to a question and answer information determination method and device, computer equipment and a storage medium. The method comprises the steps of obtaining current question information of a user, and determining whether the current question information has unique answer information or not; if the current question information does not have unique answer information, determining a target answer range corresponding to the current question information based on an initial answer range corresponding to the current question information and an answer range corresponding to the preposed question; determining alternative answers corresponding to the current question information based on the target answer range; performing fragment division on the alternative answers to obtain a plurality of alternative answer fragments; based on whether each alternative answer fragment appears in multiple rounds of question answering processes before the current question information, screening the alternative answer fragments to obtain screened answer fragments; and determining answer information corresponding to the current question information based on the screened answer segments, and outputting the answer information. The method can improve the accuracy of question and answer results.

The application discloses a kind of lunar basalt bright temperature feature optimization and geological unit classification method, comprising: obtaining the characteristic image of lunar surface different frequency noon bright temperature and midnight bright temperature, and extracting bright temperature difference feature;Combining artificial interpretation and existing geological map calibration research sample;Propose a kind of bright temperature feature optimization method;Optimized bright temperature feature is processed by principal component analysis dimension reduction, and the redundancy of bright temperature feature is reduced;Supervised classification is carried out to the geological structure in the dimension-reduced bright temperature data using random forest model, and the geological structure diagram is obtained;Bright temperature feature is recombined according to frequency and acquisition time and bright temperature difference respectively, and supervised classification is carried out respectively, to further clarify the specific role of each bright temperature feature in the classification process.The method effectively fills the blank of the existing lunar bright temperature feature on basalt geological unit classification and the contribution of the research, and provides a unified guidance research scheme for the optimization method of lunar bright temperature feature.