57results about How to "Increase contribution" patented technology

The invention belongs to the technical field of signal processing, and particularly relates to a multi-source data fusion fault diagnosis method and system based on the DS evidence theory, and the method comprises the steps: firstly collecting the multi-source information of mechanical equipment, including vibration, image and sound data, respectively calculating the corresponding characteristic indexes, carrying out the dimension reduction processing of the indexes through employing a PCA algorithm, and carrying out the fault diagnosis of the multi-source information. And carrying out fault diagnosis by adopting a Bayesian fault diagnosis model, and carrying out fault fusion diagnosis by adopting a DS evidence theory after a diagnosis result of each sensor is obtained. The method can comprehensively consider the characteristics of various data, extract more effective fault indexes, realize more comprehensive fault identification, fuse the diagnosis results of a plurality of sensors, ensure the final diagnosis accuracy, and is suitable for fault diagnosis of various complex mechanical systems.

The invention relates to the technical field of intelligent fire early warning, in particular to an urban fire intelligent early warning system which comprises an urban structure intelligent analysis module, a fire dynamic early warning simulation module, a fire prevention strategy optimization module and an environment influence intelligent analysis module. According to the method, the urban graph model is generated by using the geographic positions, materials, fire data and building age data of the urban buildings, the relevance and characteristics between the buildings are deeply analyzed, and the fire diffusion influence is simulated through the graph convolutional network, so that the prediction precision of the fire propagation path and speed is remarkably improved; the evacuation difficulty and risk level of a building under multiple fire source points are calculated in an auxiliary manner, a basis is provided for scientific planning of evacuation routes and fireproof isolation areas, emergency response and resource allocation are optimized, meteorological, population dynamics and holiday activity data analysis are integrated, the effect of environmental factors in fire early warning is enhanced, the accuracy and timeliness of early warning are ensured, and the early warning efficiency is improved. And the overall efficiency of an urban fire prevention system is improved.

The invention belongs to the technical field of seismic signal monitoring, and discloses a micro-seismic signal spectrum analysis system and method based on DVEC, and the system comprises a data collection device, a data preprocessing module, and a depth variation code-classifier. Acquiring a plurality of original vibration signals through a data acquisition device; performing fast Fourier transform (FFT) processing on the plurality of original vibration signals by using a data preprocessing module to obtain corresponding frequency domain sequence signals; splicing the original micro-seismic signals on a plurality of time domains and the corresponding frequency domain sequence signals in a dual-channel mode to form dual-channel micro-seismic signal input data; and inputting the dual-channel micro-seismic signal input data into the depth variational coding-classifier to obtain the classification category of each original micro-seismic signal. According to the method, different types of seismic signal features can be clearly distinguished, unknown types of seismic signals can be identified, and the method is expected to be an important technical means in the field of seismic signal identification.

The invention belongs to the technical field of data, and particularly relates to a dynamic access behavior analysis method based on a dynamic interaction graph. The method comprises the following steps: constructing a dynamic interaction diagram to accurately characterize a dynamic access behavior; constructing a dynamic access behavior feature representation learning model for learning noise-oriented dynamic access behaviors; through model training, model parameter optimization and noise reduction processing on noise in dynamic access behavior data, the influence of noise data on the model effect is weakened, the contribution of normal data is enhanced, the ability of the model to distinguish abnormal behavior characteristics is enhanced, and the model robustness is improved; according to the method, the analysis accuracy and efficiency of dynamic access behavior data can be improved, behaviors of illegal access data are analyzed and mined, and illegal access operation is prevented.

The invention discloses a battery capacity online estimation method based on model parameters, and belongs to the technical field of battery capacity estimation. Comprising the following steps: acquiring voltage and current data of a battery, and performing parameter identification and feature processing on the voltage and current data; performing correlation analysis on the battery model parameter and the maximum available capacity thereof, and extracting a battery health factor; building a bidirectional gating cycle unit model based on a double attention mechanism, and dividing battery data into a training data set and a test data set; and inputting the training data set into the bidirectional gating cycle unit model for training until the loss function converges and then carries out battery capacity online estimation together with the test data set. According to the method, a double attention mechanism is introduced, the correlation degree of battery model parameters, historical information and capacity is analyzed from the two aspects of features and time sequences, the features and time sequences with high contribution degree are highlighted through dynamic weight distribution, and the model precision is improved.

The invention discloses a fast convergence reliability algorithm based on an improved AK-MCS model, mainly relates to the technical field of reliability analysis, and comprises a weight clustering algorithm, a multi-trend selection method and a sample number control method adopted for constructing the AK-MCS model, and the three algorithms can be combined for use or can be used independently; according to the method, the number of times of experimental point calculation can be greatly reduced while the model precision is ensured, and the failure rate calculation efficiency is improved.

The invention provides a zero-carbon building multi-objective optimization method. The zero-carbon building multi-objective optimization method comprises the steps of obtaining a researched building and a plurality of design parameters corresponding to the researched building; the design parameters, matched with the physical characteristics of the researched building, in the multiple design parameters are defined as physical design parameters, and the design parameters except the physical design parameters are defined as technical design parameters; according to the shape design parameters, obtaining a shape design optimal solution of the researched building; and setting a plurality of technical design stage optimization objectives, and obtaining a zero-carbon building multi-objective optimization optimal solution of the researched building according to the shape design optimal solution and the technical design parameters. According to the method, a zero-carbon building design stage is divided into a body design stage and a technical design stage, and an optimal zero-carbon building body design and technical design scheme is obtained based on a multi-objective optimization module.

The invention relates to an electrical deepening design platform and design method based on Revit secondary development, and the method comprises the steps: arranging an electrical system data table according to an electrical system diagram, and importing the electrical system diagram and the electrical system data table into the platform; generating a power distribution equipment model according to the imported file, and automatically checking electrical key parameters and cable type selection in the power distribution equipment model; unconnected electrical systems in the model are connected to perfect the bridge model; taking each electrical component in the model as a starting end and a terminal end of each cable loop, and finding all bridges passing through from the starting end to the terminal end; a single bridge serves as a main body, all corresponding cable loops are checked circularly, and the filling rate of the bridge is calculated according to all the cable loops passing through the bridge; and finally, optimizing and determining a cable loop laying scheme according to the filling rate of each bridge. According to the invention, optimization and verification of cable type selection, bridge size and cable routing are realized, and the efficiency and accuracy of deepening the process are improved.

The present invention provides a sample selection method and device, and a classifier training method and device, which are applied to a single-stage target detector, wherein the sample selection method includes: constructing a cost function that simultaneously considers classification loss and regression loss; using the cost function to calculate the cost of all candidate samples according to the prediction information output by the detection network and the target true value; using prior information to screen candidate samples; sorting the screened candidate samples according to the cost, and selecting the candidate samples with the highest priority. N The samples with the lowest cost are positive samples, and the rest are negative samples. The classifier training method and device distinguish all positive samples according to the IoU value between the regression box and the target box, improve the contribution of high-quality candidate samples to the classification loss, and suppress the influence of low-quality samples.

The present disclosure discloses a method and device for identifying a radiation source, which specifically relates to the field of data processing. The main technical scheme includes: when performing the identification of the radiation source, inputting a noise vector into a generation network in a preset radiation source identification model; outputting a signal sample after the convolution operation of the preset radiation source identification model to blind the discriminant network in the preset radiation source identification model; performing binary classification on the output samples of the generation network and the unlabeled samples based on the discriminant network; and evaluating the binary classification results based on the skewed distribution loss function and the labeled samples. The radiation signal is trained based on the preset radiation element identification model to identify its radiation source, and the gradient imbalance caused by the uneven sample category is offset according to the skewed distribution loss function, thereby increasing the contribution of the minority class samples to the total loss, and realizing the identification of the radiation source of the skewed category distribution data.

The invention discloses a Co3O4-Cu2-xS composite photo-thermal catalyst as well as a preparation method and application thereof. The composite photo-thermal catalyst is prepared by taking a surfactant as a bridge and compounding Co3O4 and Cu2-xS. The composite photo-thermal catalyst has a reduced band gap width, can effectively promote migration and separation of photon-generated carriers, enhances the absorption efficiency of visible light-near infrared light, and shows excellent photo-thermal conversion performance. The composite photo-thermal catalyst can be used for constructing a Co3O4-Cu2-xS + PMS + NIR catalytic system, and peroxymonosulfate is activated under the irradiation of near-infrared light so as to degrade antibiotics in water. Experiments prove that the Co3O4-Cu2-xS + PMS + NIR system disclosed by the invention can achieve the degradation rate of 99% on levofloxacin within 20 minutes, and the generation efficiency of active species is remarkably improved through the synergistic effect of a photothermal effect and interface activation. The catalyst has broad-spectrum degradation activity on various antibiotics, keeps efficient and stable catalytic performance in a wide pH range, and shows excellent ion interference resistance and adaptability to various water bodies.

The invention discloses a credit card recommendation method and device, electronic equipment and a computer readable storage medium. The method comprises the following steps: acquiring multi-dimensional customer data of debit card stock customers through a first business channel of a financial institution; credit card customer group information is obtained through a second business channel of the financial institution, the credit card customer group information comprises credit card customer group classification information and a credit card recommendation strategy corresponding to each credit card customer group, and the first business channel and the second business channel are different business channels of the financial institution; determining a credit card customer group to which the debit card stock customer belongs according to the credit card customer group classification information and the multi-dimensional customer data; and obtaining a credit card recommendation strategy corresponding to the credit card customer group, and recommending a credit card to the corresponding debit card stock customer according to the credit card recommendation strategy corresponding to the credit card customer group.

The invention relates to a training method and device of a single-agent reinforcement learning model based on visual representation, electronic equipment, a storage medium and a computer program product, and the single-agent reinforcement learning model comprises an online state encoder, an action encoder, a reinforcement learning network and an auxiliary task network. The auxiliary task network comprises a state prediction model, and according to state information and action information obtained by a target agent based on an observation image of the target agent in the current time period and reward information of the current time period, the state prediction model is used for predicting the state information and action information of the target agent from the perspective of visual representation through the auxiliary task network. The state characterization and the action characterization of the target agent are learned, the optimal decision action is selected for the target agent through the reinforcement learning network, and the time sequence information of the time period in reinforcement learning is fully utilized. Performance and sample efficiency improvement of a single agent in a challenged complex continuous control task with an image as state input can be realized.

The present invention discloses a cross-device ECG federal privacy classification framework method, system and storage medium, and relates to the field of federated learning technology. The specific steps include the following: the server sends initial model parameters to the client; the client trains the initial model using a training data set to obtain basic type recognition model parameters, and sends them to the server in a differential manner; the server receives the basic type recognition model parameters for clustering to obtain clustering results; the clustering results are weighted averaged to obtain the final update parameters of the server model, and the training of the initial model is completed; the ECG data is recognized by the trained initial model, and the respective ECG data type recognition results are output on each client. The present invention can improve data recognition accuracy while protecting user data privacy during ECG health monitoring.

The invention discloses a magnetic resonance image channel selection method based on an adaptive threshold and PCA, and relates to the technical field of nuclear magnetic resonance medical imaging. Comprising the steps of image preprocessing, adaptive threshold separation, contour fitting, channel pre-screening, contour fitting, channel pre-screening, image data matrix construction, principal component analysis, channel sorting and screening and the like. By dynamically calculating the threshold and segmenting the local characteristics of the image, the accuracy and adaptability of segmentation are improved, the foreground and the background can be more accurately separated under the condition that the contrast ratio is large, and meanwhile, PCA analysis is adopted to calculate the principal component of channel data, fully consider the correlation between channels and reserve the main information of the data. The PCA can effectively reduce dimensions, select a channel with the maximum contribution degree, and ensure that important information is not lost. The combination of the two has better robustness and systematicness when processing complex scenes and multichannel data, redundancy and noise are effectively reduced, and the signal-to-noise ratio of magnetic resonance images is improved.

According to the metabolism-oriented clustering-based tumor-associated macrophage metabolism typing method provided by the invention, the single-cell RNA sequencing data set for analysis is collected and arranged, and then the tumor-associated macrophage population is identified and extracted from the single-cell RNA sequencing data set, so that the technical deviation is eliminated; a core metabolism gene set is constructed based on a tumor-associated macrophage population, then weight distribution and optimization of core metabolism genes are carried out, contribution of the metabolism genes in clustering analysis is enhanced, metabolism-oriented clustering is carried out based on the optimized weight, and a metabolism typing result is generated. Therefore, enhancement of metabolic gene expression characteristics and more accurate distinguishing of TAMs subgroups are achieved, typing results are analyzed and verified through GSEA enrichment analysis or GO enrichment analysis, typing categories are output, challenges confronted when high-dimensional single-cell data are processed through a traditional method are effectively overcome, the method is not only suitable for TAMs, but also can be expanded to other tumor-related cell types, and the method has a good application prospect. And the blank in the prior art is filled.

The invention relates to the technical field of micro-grids, and discloses a multi-virtual power plant carbon sharing optimization method. The method comprises the following steps: establishing a single virtual power plant optimization model, and considering carbon sharing between virtual power plants (VPP); establishing a multi-virtual power plant asymmetric Nash bargaining model based on the carbon sharing contribution degree: quantifying bargaining capability of each virtual power plant based on the carbon sharing contribution degree, and establishing a multi-virtual power plant (MVPP) asymmetric Nash bargaining model based on the carbon sharing contribution degree; and solving the MVPP asymmetric Nash bargaining model, and decomposing into two steps of solving: solving the cooperative alliance cost minimization model to obtain the carbon quota shared by the VPP, and solving the MVPP asymmetric Nash bargaining model to obtain the shared carbon price of the VPP. Compared with the prior art, the MVPP carbon sharing optimization method is established, and the carbon sharing strategy of the VPP is solved by adopting the asymmetric Nash bargaining game based on the carbon sharing contribution degree, so that the carbon cost of each VPP is reduced, the economic benefit of each VPP is improved, the benefit distributed by the VPP with the high carbon sharing contribution degree is larger, and the fairness of benefit distribution is ensured.

The invention discloses an efficiency improvement evaluation method and device for a code assistant, equipment and a medium. The efficiency improvement evaluation method comprises the steps that a problem sequence input by a developer is received through the code assistant; counting performance index data corresponding to different service functions in the code assistant according to a feedback result of the code assistant for the problem sequence; and determining an efficiency improvement evaluation result corresponding to the code assistant according to the performance index data corresponding to different service functions in the code assistant. According to the technical scheme, the overall efficiency improvement contribution of the code assistant for the development activity can be accurately evaluated, and enterprises are guided to deploy research and development cost.

The invention discloses an intelligent detection method and system for abnormal SQL statement judgment, and the method comprises the steps: designing a CNN-LSTM dual-channel architecture and a cross-modal attention fusion layer through the introduction of a combined design of an abstract syntax tree, byte pair coding and a recurrent neural network, enabling the two networks to capture local and global features respectively, and carrying out the recognition of abnormal SQL statements. The feature weight is dynamically adjusted through an attention mechanism, deep interactive fusion is realized, a heterogeneous base learner cluster is constructed, a meta learner is constructed by extracting multi-dimensional scene features and introducing a multi-layer perceptron, the base learner weight adaptive to the scene is dynamically generated, and meanwhile, a judgment threshold value is adjusted in combination with actual requirements, so that scene self-adaptive accurate decision is realized; and finally, abnormal SQL detection is achieved. According to the method, the feature fusion defect is solved through a cross-modal dynamic attention mechanism, scene adaptive optimization is realized through a meta-learning-driven dynamic decision framework, and the diversity of the model is enhanced through cooperation of multi-scale embedding and differentiated training strategies.

The application discloses a social media-based interpretable dynamic graph network benefit prediction model training method, relates to the technical field of text analysis and market prediction, and obtains market data and related social media data of a financial asset to divide the data by weeks; for the social media data of each week, a topic is taken as a node, and similarity between topics is taken as an edge weight, a topic correlation graph of each week is constructed to obtain weekly graph data; a prediction model performs time sequence updating and market prediction according to the weekly graph data; a text time sequence memory unit obtains a current week topic memory vector according to a current week topic text embedding vector and in combination with a last week topic memory vector; a multi-layer graph attention network captures the correlation features between nodes through an attention mechanism and adopts attention weights for weighted summation to obtain a current week global graph embedding; and a classifier outputs a next week price change prediction result. The application solves the problem that the prior art cannot effectively capture the time sequence evolution of topic sentiment and the correlation between topics.

The invention relates to the technical field of image target detection, and discloses a target detection method for a non-motor vehicle scene, which specifically comprises the following steps of: based on sampling of repetition factors, calculating the repetition factors according to the occurrence frequency of a training image containing a specific category of objects; adjusting the sampling rate of sampling the training image containing the specific category of objects from the training data set; based on mosaic balance enhancement, according to the repetition factor of each category, repeating the object of the non-motor vehicle category at the random position of the training image; and sequentially performing feature extraction on the training images by using the target detection model, generating candidate frames of possible objects, classifying the objects in the candidate frames, and finally obtaining a detection result. The non-motor vehicle instance proportion in the original data set is increased by using the sampling method based on the repetition factor and the balanced sampling method based on mosaic, and the non-motor vehicle lane scene can be well adapted under the condition that the non-motor vehicle lane data set does not need to be additionally collected.

The application claims a kind of dual-channel and attention mechanism aspect-level multi-modal sentiment analysis method, which is based on neural network, the sentiment information contained in image feature is extracted by aspect word feature and sentence feature joint attention mechanism multi-scale, and GCN network is introduced into aspect-level multi-modal sentiment analysis task, greatly improve the feature extraction and interactive fusion ability of model.In the present application, the pre-training encoder is used to extract aspect word, sentence feature and image feature in the feature extraction layer.In the attention mechanism layer, after the bidirectional fusion of aspect word and sentence feature, the final aspect word feature and sentence feature representation are obtained.Image feature establishes image feature extraction network through channel attention mechanism and spatial attention mechanism.Finally, the interactive fusion features of each modality are dynamically extracted by GCN module.In the experiment, the performance indicators of aspect-level multi-modal sentiment analysis based on attention mechanism are improved on the data set.

The invention provides a loss prediction method and device based on local performance estimation, equipment and a medium, and relates to the field of artificial intelligence, and the method comprises the steps: obtaining feature data of a to-be-predicted sample, inputting the feature data into each base model, and obtaining the original prediction output of each base model; obtaining a neighbor sample set of the to-be-predicted sample in the historical verification set, and calculating a local performance index of the base model in the neighbor sample set for each base model; and calculating a dynamic adjustment coefficient of each base model according to the local performance index of each base model and the global performance index of each base model, generating a fusion weight for the to-be-predicted sample in combination with the global weight and the dynamic adjustment coefficient of the base model, and performing weighted fusion on the original prediction output by using the dynamic fusion weight to obtain a loss prediction result. The method can adapt to telecommunication user groups with high data heterogeneity, and the loss prediction precision of individual users is improved.

This invention discloses a single-target tracking method and system based on channel attention and spatiotemporal awareness. The method includes the following steps: inputting a template image and a search region image; extracting hierarchical features from both using two weight-shared RepVGG backbone networks; performing self-attention and cross-attention feature fusion on the two feature maps using a fusion model based on channel attention mechanism; completing target tracking prediction through classification branches, bounding box regression branches, and centrality regression branches; updating the template in real time through threshold judgment; adding the temporal information of subsequent frames to the fusion network to achieve complete target tracking; and demonstrating tracking performance. The system includes: a feature extraction module, a feature fusion module, a tracking prediction module, and a performance display module. This invention allows the template features to adaptively enhance their completeness with spatiotemporal changes, thereby improving the robustness of tracking.

The present application discloses a method and device for allocating the contribution of a virtual power plant to demand response, which relates to the field of contribution allocation, including: obtaining the internal resources of a virtual power plant when virtual power plant control is not adopted and when virtual power plant control is adopted, and calculating the sum of the load values ​​of the internal resources of the virtual power plant when virtual power plant control is not adopted and when virtual power plant control is adopted; calculating the contribution value of the adoption of virtual power plant control to participate in the demand response market to alleviate the power supply and demand gap of the power grid; calculating the contribution difference before and after different virtual power plant internal resource combinations participate in demand response; calculating the weighting factor of each internal resource of the virtual power plant; calculating the dependence of each virtual power plant internal resource on the overall adoption of virtual power plant control; calculating the contribution allocation value of each virtual power plant internal resource after the virtual power plant control strategy is adopted. The present application can solve the problem of contribution allocation of various types of internal resources of virtual power plants participating in the demand response market in a more scientific, efficient and convenient way.

The present application discloses a method, device and electronic device for generating a query processing model, wherein the method comprises: performing session segmentation on historical query data to determine session segmentation data; screening based on the session segmentation data to extract target queries; determining the similarity relationship between target queries, constructing a training sample set based on the similarity relationship; and training a preset learning model based on the constructed training sample set to obtain a target model. This method can efficiently and automatically construct a training sample set, train a more accurate query processing model, and apply the target model to improve the accuracy of similar queries obtained by query rewriting.

The invention provides a preparation method of mulberry and Weishi wine and the obtained mulberry and Weishi wine, the preparation method of the mulberry and Weishi wine comprises the following steps: S1, adding hops into mulberry juice, and sterilizing to obtain a to-be-fermented liquid; s2, main fermentation: inoculating the activated and cultured saccharomyces cerevisiae BYBC 2.21108 into the to-be-fermented liquid for liquid fermentation; the preservation number of the saccharomyces cerevisiae BYBC 2.21108 is CGMCC (China General Microbiological Culture Collection Center) And S3, ageing to obtain the mulberry-Wei Tai wine. The aroma of the mulberry-Wei Di wine is dominated by sulfides, the mulberry-Wei Di wine is more unique, and the fermentation degree and the quality are remarkably improved. In the aspect of contribution of aroma main components, the fruit-flavor beer has the due beer flavor of beer and the aroma of fruits, and also has the characteristics of low alcoholic strength, rich organic acid and oxidation resistance, so that the fruit-flavor beer has great development and utilization values.

The invention relates to the technical field of target detection, in particular to a small target detection method and device and a medium thereof. The method comprises the following steps: S1, acquiring an original image, and preprocessing the original image to obtain a data set; step S2, constructing an original YOLOv8 model, improving the original YOLOv8 model, and replacing an SPPF structure of the original YOLOv8 model with an SPPF-MAA structure to obtain an improved YOLOv8 model; s3, inputting the data set into the improved YOLOv8 model, and training the improved YOLOv8 model by using a self-adaptive weight loss function; and S4, target detection is carried out by using the trained YOLOv8 model, so that the precision of small target detection can be remarkably improved.

The application provides a distance measurement method and an electronic device, and relates to the field of visual perception. The method comprises the following steps: acquiring distance prediction values and uncertainties of a target object in a to-be-measured image determined by at least two distance measurement algorithms; wherein the uncertainty is set according to a prediction confidence determined by the distance measurement algorithm or is set according to at least two distance prediction values determined by the distance measurement algorithm; determining a fusion weight corresponding to each distance prediction value according to the uncertainty; and fusing each distance prediction value according to each fusion weight to obtain a distance measurement value. The distance prediction values of multiple distance measurement algorithms can be fused according to the uncertainty, so that the advantages of different ranging algorithms are fused for distance measurement, and the stability and reliability of monocular visual ranging can be improved.