80 results about "Adaptive integration" patented technology

The invention relates to the technical field of intelligent fire fighting, in particular to a building electrical fire identification method and system based on multi-source information fusion, and the method comprises the steps: firstly collecting the multi-source monitoring data of an electrical system in real time, then carrying out the preprocessing of the multi-source data, including noise filtering, time alignment and feature extraction, and constructing a dynamic weight fusion model; the weight of each data source is adaptively allocated based on the feature entropy, then fusion features are input to the fire risk grading model, a fire probability index and an early warning level are output, and when the early warning level exceeds a threshold value, a linkage control instruction is triggered and an alarm is given; according to the method, through dynamic fusion of multi-source information, the electrical fire identification precision is remarkably improved, multi-dimensional parameters such as current, temperature, smoke and arc are adaptively integrated by adopting an entropy weight assignment mechanism, the problems of false alarm and missing alarm of traditional single-threshold detection are solved, the missing alarm rate and the false alarm rate are remarkably reduced, and the reliability of fire early warning is ensured.

The invention belongs to the technical field of industrial system integration, and provides a design method for rapid adaptive integration of a cross-domain system. Through the method, developers extract business logic rules of the industry, standardize characteristic mapping and select, develop and integrate functional modules, logic processes and scene components of the cross-domain system, so that the cross-domain system which conforms to industry characteristics and meets business circulation is formed, and meanwhile, the development efficiency of the cross-domain system is improved. The newly added industry logic component can be classified into each industry characteristic model based on the knowledge base, and a multi-dimensional characteristic vector is generated in the platform, so that cross-industry integrated component multiplexing is realized, and the cross-field applicability and flexibility of the platform are further improved. Modularized construction and configuration type development are supported, mapping and matching functions are carried out on various industrial manufacturing industry characteristics such as machining, electronic and electrical appliances and equipment sets, the requirements for subdivision production in the industrial field and rapid adaptation of business processes are met, the system integration cost is reduced, and the application development efficiency is greatly improved.

The invention discloses a drug-target interaction prediction method based on double-flow collaborative attention and sparse feature fusion, and belongs to the technical field of computational biology. The method solves the problem that the existing method cannot capture the sub-structure discrimination features and the key binding region features of the drug-target interaction pair. According to the method, a double-flow collaborative attention strategy combining a multi-scale space attention mechanism and a channel enhanced attention mechanism is adopted to cooperatively capture discriminative features of substructures, the multi-scale space attention mechanism utilizes a multi-branch convolutional layer to adaptively integrate space substructure features, and molecular representation of each substructure is enhanced; the channel-enhanced attention mechanism mitigates the inconsistency of substructure features. The sparse attention mechanism can highlight the key features while suppressing the noise, and the cross attention mechanism improves the extraction capability of the features of the key combination region through feature interaction between the modeling drug and the target. The method can be applied to drug-target interaction prediction.

The invention belongs to the field of power distribution network parameter identification, and discloses a power distribution network line parameter identification method and system based on a graph neural network, and the method comprises the steps: employing a unified projection and attention weighting mechanism, obtaining the point features of a power distribution network line, and carrying out the self-adaption integration of multi-source heterogeneous observation data; initial edge features are explicitly constructed in neural network propagation of each layer of graph, node and edge representation is alternately updated, and the line parameter prediction precision is improved; power grid core laws such as Ohm's law and node power injection constraint are converted into a loss function to constrain a training process, consistency constraint of a parameter prediction result and a power system physical law is realized, and interpretability and credibility of the prediction result are improved; and inputting the power grid data into the graph neural network optimization model, and outputting a power distribution network line parameter identification result, thereby solving the problems that in the prior art, dynamic information interaction between nodes and edges is neglected, so that parameter identification is sensitive to network structure change, and physically incredible parameter output is easy to generate.

The invention discloses a cloud data center virtual machine adaptive integration method based on Autoformer and an enhanced double-Q network, and relates to the technical field of cloud computing resource management and optimization, the resource utilization rate of each physical host is monitored in real time, historical load time sequence data is collected, a pre-trained Autoformer model is loaded, and double-Q network parameters and environment state variables are initialized; and predicting a host resource utilization rate based on an Autoformer model, and dividing an overload host, a low-load host and a normal host in combination with the current host resource utilization rate. According to the method, the Autoformer model is adopted for load prediction, trend terms and period terms are decomposed through an autocorrelation mechanism, the multi-scale periodicity in the cloud load is effectively captured, compared with a traditional LSTM model, the Autoformer is higher in modeling capacity for a complex time sequence mode, the misjudgment rate of an overload / low-load host can be remarkably reduced, a more accurate host state detection basis is provided for virtual machine integration, and the method has the advantages of being high in practicability and easy to popularize. Therefore, a resource allocation strategy is optimized.

The invention discloses a dual-path multi-scale feature extraction method with differential information compensation for an LSTF, and relates to the technical field of long-term time series prediction. According to the method, a new network structure DMWCNet is designed, and lightweight and efficient long-term time sequence prediction can be realized by adopting an integral design of reversible normalization, dual-path multi-scale feature extraction and differential perception compensation. Dynamic normalization and anti-normalization of data distribution are realized through the RevIN layer, and the stability and generalization ability of the model under cross-scene and cross-distribution conditions are improved; a core backbone VATMLP module adopts a main and auxiliary double-path structure, a main path models a long-term trend through dimension expansion and progressive compression, an auxiliary path retains local details through a compression and reconstruction mechanism, and two paths of features are adaptively integrated through cascade and fusion projection, so that the expression ability of the model under different time scales is remarkably enhanced.

The invention discloses a modular sensor adaptive integration and data processing method and system in a complex terrain environment, and the method comprises the steps: constructing an extensible sensor sensing layer through a standardized interface module, and achieving the plug and play of various environment monitoring sensors; performing an inspection task in a complex terrain area based on a mobile device, and performing fusion processing on real-time data acquired by multiple sensors through an edge calculation layer; according to the environment data characteristics, the terrain complexity and the running state of the mobile device, the inspection behavior strategy of the mobile device is dynamically adjusted, and follow-up data monitoring and processing are carried out; the processed real-time data is uploaded to a cloud collaboration layer for collaborative analysis in a self-adaptive selection communication mode, and fundamental conversion from a static and fixed monitoring mode to a dynamic and adaptive mode is realized through modular sensor integration, mobile inspection, edge intelligent processing and self-adaptive communication; and the coverage rate, the efficiency and the intelligent level of environment monitoring are remarkably improved.

The invention discloses a virtual-real fusion traffic twinborn simulation test system and method oriented to air-ground coordination, belongs to the field of intelligent traffic design optimization, verification or simulation, and aims to provide a highly flexible multi-scene experiment platform for teaching and scientific research through an entity twinborn-dynamic mapping concept. The system is based on a dynamic display technology, real traffic scenes such as urban roads, emergency dispersion and three-dimensional parking are twinned to a small-scale experiment space, rapid switching of scenes and space layout is supported, and seamless mapping of virtual and real environments is achieved. The central control system synchronizes motion trails and virtual traffic flows of physical equipment such as an unmanned aerial vehicle and an intelligent vehicle in real time through a virtual-real fusion technology to form a small space entity-central control virtual two-way interactive closed loop. A multi-scene adaptive integration idea is taken as a core, the space-ground cooperation capability is combined, dynamic reconstruction and nonlinear migration of tasks such as logistics distribution and emergency response are supported, and a cross-scene experiment can be completed without hardware transformation.

A flood similarity intelligent analysis method based on multi-mode Transform and comparative learning comprises the steps that firstly, static features are processed through a grouping full-connection network, and the characterization capacity is enhanced through feature specific transformation; a CNN-Transform-Attention hybrid network is constructed to extract dynamic process line features, a convolutional layer captures local morphological features, a Transform encoder captures a global time sequence dependency relationship through a self-attention mechanism, key hydrological stages are adaptively focused through an attention weight, multi-modal features are adaptively integrated by adopting a gating fusion mechanism to generate unified embedded representation, and the dynamic process line features are extracted by adopting a convolutional neural network (CNN)-Transform-Attention hybrid network. A comparison learning and difficult sample mining strategy is introduced, discriminative characterization is learned in a low-dimensional embedding space through a comparison loss function training model based on the Euclidean distance, and the similarity is mapped into an interpretable probability of a [0, 1] interval; the technical problem that multi-source heterogeneous features are insufficient in utilization is effectively solved, accurate quantification and interpretable evaluation of flood similarity are achieved, and technical support is provided for flood forecasting, historical flood matching and flood control dispatching decision making.

The invention provides an underwater image enhancement method based on structure-color collaborative modeling, and relates to the technical field of image enhancement, and adopts a parallel double-branch architecture: on one hand, efficient long-range dependence modeling is performed on horizontal and vertical directions through ''directional frequency Mamba'' so as to maintain edge and structure topology; on the other hand, a differentiable histogram is introduced to guide self-attention, and global color distribution is explicitly coded to correct color cast and dynamic range compression. On each scale, context and fine-grained details are adaptively integrated by using lightweight'gated multi-scale fusion ', and a result with natural color, improved contrast and clear structure is obtained through decoding and reconstruction. The method is low in calculation overhead, high in interpretability and suitable for real-time deployment of the low-power-consumption terminal.

The invention discloses a smart city big data acquisition and summarization method, and particularly relates to the technical field of big data processing, and the method comprises the steps: S1, data perception and pre-acquisition, predicting future data demands through a business demand prediction engine, and dynamically adjusting an acquisition strategy, S2, dynamic routing and storage, and according to a demand prediction result and a real-time portrait of a storage node, carrying out data acquisition and summarization. The method comprises the steps of S1, intelligently distributing appropriate storage nodes for data, S2, carrying out self-adaptive integration, predicting and generating a minimum conversion set according to requirements, and only converting and fusing necessary data, and S4, carrying out data service, and pushing fused data in a subscription-release mode through a uniform interface. According to the method, demand prediction is preposed and runs through the whole data processing flow, so that the conversion from passive full-quantity acquisition to active precise service is realized, the technical problems of data redundancy, high transmission load, contradiction between storage and calling and the like in a traditional method are effectively solved, and the efficiency and the intelligent level of data acquisition and summarization are remarkably improved.

The invention discloses an intelligent water affair management system based on deep learning, and the system comprises a data collection and preprocessing module which is used for collecting the data of a water supply network and constructing a standardized water affair input tensor; the topology modeling module is used for generating a water supply pipe network diagram structure and diagram structure representation thereof; the state initialization module is used for fusing the input tensor and the graph structure to generate an initial state vector; the state evolution calculation module is used for establishing an augmented state evolution equation and carrying out adaptive integration to generate a prediction state sequence and source sink estimation; the multi-objective decision module is used for constructing a comprehensive cost function and generating a pump station and valve control instruction; the physical constraint projection module is used for executing water pressure, flow velocity and water age constraint projection and outputting an executable scheduling instruction; and the dynamic simulation feedback module is used for performing digital twin simulation and updating the model and sparse estimation weight. According to the invention, fine control and dynamic feedback optimization of the water affair system are realized, and the operation efficiency and the scheduling intelligence level are improved.

The present application relates to the technical field of Xinchuang storage and database integration, and discloses a database adaptive integration method, system, device and medium based on Xinchuang storage, wherein the database adaptive integration method based on Xinchuang storage comprises the following steps: extracting a protocol element definition set and a medium performance characteristic parameter matrix of a source system and a target system; generating a protocol difference vector by using an edit distance algorithm; generating a three-dimensional affinity tensor of a data block-medium-encoding format by comprehensively storing overhead, access efficiency score and medium performance parameters; generating a version-aware data encoding conversion rule set and a medium allocation mapping table by solving and generating through a genetic algorithm optimization; performing streaming data reading and online format conversion; and directly writing the converted data stream into a corresponding target storage medium. The method realizes integrated processing of protocol adaptation and medium optimization, effectively shortens the migration window, and avoids temporary storage of an intermediate format.

The application provides a radar scattering cross section (RCS) acquisition method based on CBFM-AIM-EDM, and the implementation steps are as follows: initializing parameters; calculating main characteristic basis functions of each sub-region based on CBFM-AIM; calculating secondary characteristic basis functions of each sub-region based on CBFM-AIM-EDM; constructing a reduction matrix and acquiring the radar scattering cross section (RCS). The main characteristic basis functions and the secondary characteristic basis functions of each sub-region are calculated by using the characteristic basis function method (CBFM), then the RWG basis functions and their divergence on each sub-region can be projected on the matrix grid respectively by using the adaptive integration method (AIM) to obtain the Topelitz matrix, so that the calculation speed is accelerated, the RCS acquisition efficiency is improved under the condition of ensuring the accuracy, and the coupling between two sub-regions of the target is equivalent to the coupling between equivalent dipoles by using the equivalent dipole moment method (EDM), so that the RCS acquisition efficiency is further improved.

The invention relates to a text classification method fusing semantic information and structural information, and aims to solve the problems of text graph noise interference, incomplete semantic capture, rigid information fusion and the like in existing graph neural network text classification. The method comprises the steps that firstly, a target text is preprocessed, and phrase blocks with complete semantics are extracted through BERT sequence labeling and B-I-O labeling; constructing an enhanced text graph by taking the phrase blocks as nodes and combining various relationships such as self-loop edges and syntactic dependency edges and cross-sentence anaphora connection; afterwards, redundant edges in the picture are cut through attribute-enhanced personalized PPR, and noise interference is weakened; and finally, inputting the optimized text graph into gradient gating fusion GNN, adaptively integrating BERT context features and global dependency information, outputting node features and completing classification. The method effectively breaks through the limitation of a traditional method, realizes deep fusion of semantic and structural information, has higher classification accuracy, stronger generalization ability and good stability, and is suitable for various short text, long text and professional field text classification scenes.

The invention discloses a hypergraph propagation source positioning method based on interaction enhancement. A traditional traceability method mainly depends on a pairwise interaction hypothesis of a simple graph structure, and the positioning precision under a complex topology is limited. The method comprises the following steps: firstly, constructing a hypergraph propagation model and obtaining a complete observation snapshot of a network node; then, extracting the dynamic state and global spectrum features of the nodes, and constructing a basic feature vector; an improved double-flow interaction hypergraph neural network is utilized, hyperedge internal key information is aggregated through local attention flow, and multi-scale topology diffusion flow is utilized in parallel to capture overall long-range dependence; and finally, introducing a gating residual fusion mechanism to adaptively integrate the double-flow features, and accurately outputting the propagation source probability based on a weighted cross entropy loss function. According to the method, the path reconstruction deviation of a traditional method can be effectively overcome and the robustness can be improved in the aspect of processing high-order interaction and long-distance dependence. According to the method, the performance bottleneck based on simple graph traceability is broken through, and powerful technical support is provided for propagation source positioning.

The application discloses a runoff prediction method based on STL-SVMD decomposition and adaptive integration, and comprises the following steps: collecting runoff and multi-source meteorological data; constructing a trend item and a seasonal item responding to long-term climate driving by using a generalized STL method; constructing a multivariate SVMD target function containing a driving alignment item and a physical penalty item, and decomposing runoff residuals into mode subsequences with clear physical correlation on the premise of meeting causality constraints; performing closed-loop screening on the modes based on information retention rate and physical consistency double indexes; constructing a component-level integrated prediction model containing monotonicity, annual integral and extreme value sensitivity constraints; and finally, reconstructing runoff prediction results by using a multi-objective loss function and a time-varying adaptive Bayesian optimization strategy to dynamically adjust model parameters. The application improves the physical consistency and precision of runoff prediction.

The application discloses a power grid malicious traffic detection method and system based on adaptive integration, relates to the technical field of power grid network security, and aims to solve the problem that the prior art cannot effectively detect encrypted malicious traffic in a resource-limited power grid edge environment. The application comprises the following steps: a client collects traffic data, monitors its own resource state and performance index, encapsulates the user demand into a request, and sends the request to a server; the server constructs a query feature vector, performs similarity query in a historical strategy library to quickly reuse or fine-tune the strategy, and if no strategy is found, an adaptive integrated decision algorithm is started to generate an optimal integrated learning scheme; the scheme is sent to the client, the client loads and runs the scheme and detects traffic features; and when malicious traffic is detected, the client triggers a security response action. According to the technical scheme, the detection strategy can be dynamically adjusted according to actual resources and demands without decrypting the traffic, and the optimal balance between detection effect and resource consumption can be achieved under the condition of resource limitation.

The invention discloses a drainage facility detection method and system based on a regional attention hybrid architecture. The method comprises the following steps: constructing a detection model and carrying out end-to-end training on the model by utilizing a marked training image set; the regional attention branch is responsible for capturing local detail features of the image, and the linear time sequence branch is used for modeling the global context and structural relationship of the image. Then, a feature fusion module carries out self-adaptive integration on the output of the two branches to form final feature representation; and finally, the bidirectional feature pyramid network performs multi-scale target detection and positioning to obtain a detection result. The system comprises a training data acquisition unit, a model training unit and a model application unit. According to the invention, the problem of insufficient generalization of a traditional detection algorithm in multiple scenes is solved. The method can be widely applied to the field of image processing.

The invention provides a false news detection method based on a graph U-Net and adaptive integration. The false news detection method comprises the following steps: S1, constructing a news and user interaction process into a node graph; s2, forming a graph self-encoder and a decoder; and S3, realizing false news detection by utilizing a self-adaptive integration strategy of local feature fusion and global information optimization. According to the false news detection method capable of being constructed, classification optimization is achieved through the supervision signals provided by the false labels under the condition of no manual labeling, and the detection precision and generalization ability are improved.

The present invention provides system executing dynamic mixed reality experiences on user device including processors and non-transitory memory storing instruction receiving trigger signals from user interaction with trigger mechanisms through user devices. Trigger mechanisms comprise universal access links invoking instant applications without installation. System activates modular mixed reality engine dynamically loading mixed reality modules and digital assets, executing instant applications within sandboxed runtime environments using secure execution frameworks. System enables context-aware, dynamic, adaptive integration of virtual elements into physical environment displayed through user interfaces using real-time physical environment data and spatial analysis. Mixed reality experiences render on adaptive interactive user interfaces enabling dynamic digital placement, manipulation, transformation of virtual elements adjusted dynamically before and during rendering based on real-time physical environment data, spatial analysis, hardware-software configurations. System improves device functioning minimizing computational, overhead sandboxed execution, reducing launch latency via instant application invocation, enabling secure, adaptive, spatially consistent digital object interaction.

The invention discloses a graph classification method based on multi-view adaptive attention and feature integration enhancement, and aims to improve the representation capability and classification performance of graph structure data. An existing image classification method generally has the problems of single feature extraction view angle and inflexible fusion mechanism, and multi-level structure information in an image is difficult to fully mine. Therefore, the method has the following characteristics and contributions: firstly, a feature integration module fusing multi-graph convolution and one-dimensional convolution is constructed, graph node features are extracted from different view angles through multiple graph convolution, and self-adaptive integration of cross-view-angle features is realized by means of the one-dimensional convolution; secondly, designing a multi-angle adaptive channel attention mechanism, dynamically calculating weight distribution of each channel, and emphatically strengthening information expression of key semantic channels; finally, experimental verification on eight public graph classification data sets shows that the average classification accuracy of the method is improved by about 10% compared with that of a current advanced method, and the superiority and practicability of the method in a graph classification task are effectively proved.

The invention provides an adaptive integration method and device for heterogeneous credit data, and belongs to the technical field of credit data processing, and the method comprises the steps: obtaining heterogeneous credit features of all enterprises in a designated region; the heterogeneous credit features comprise structured credit features corresponding to the structured credit data of each enterprise and unstructured credit features in the unstructured credit data of each enterprise; extracting behavior information, subject information and spatio-temporal information in the heterogeneous credit features of each enterprise; and taking people, organizations and events as core entities, and obtaining credit knowledge maps corresponding to the enterprises based on the association relationship integration of the behavior information, the subject information and the spatio-temporal information corresponding to the enterprises, so as to take the credit knowledge maps corresponding to the enterprises as multi-dimensional association credit archives of the enterprises. According to the adaptation integration method and device for the heterogeneous credit data provided by the invention, the reliability and accuracy of enterprise credit evaluation can be improved.

The invention relates to the technical field of power system overvoltage protection, in particular to a tower lightning current measurement method based on adaptive integration and path planning. According to the technical scheme, the method comprises the following steps that a rectangular plane coordinate system is established with the center of the cross section of a platform at the middle-section height of a target measurement tower as the origin of coordinates, and the actual geometric dimension and the structure type of the platform are measured; and adaptively determining the size and the length-width ratio of the rectangular measurement path according to the geometric size and the structure type of the platform. According to the method, high-precision measurement of lightning currents of various tower structures is achieved through full-process self-adaptive optimization from path planning to node distribution, the optimal measurement path can be determined in a self-adaptive mode to improve the universality of the method, the precision bottleneck of a square path is overcome through a double-path fusion strategy, and the method is suitable for large-scale popularization and application. And meanwhile, a dynamic node encryption mechanism is introduced, so that the sensor resource configuration is optimized on the premise of ensuring the precision, and the adaptability, precision and efficiency of measurement are improved.

The application provides a SysML model driven automatic simulation script processing system and method, relates to the technical field of SysML model driven simulation, and comprises a user interactive interface / script editor, a script analysis and execution engine, a SysML model data interaction encapsulation library, a core operation and self-defined function library, a SysML modeling tool interface, an external simulation and communication interface and model data storage. The application establishes a tool chain with model-simulation bidirectional mapping, complex logic automatic processing and heterogeneous environment adaptive integration capabilities.

According to the combustion flame temperature prediction method and system provided by the invention, based on RGB and temperature data of flame, an advanced Bagging integration algorithm is adopted, three base models are effectively fused, and a high-precision nonlinear regression prediction model is constructed. According to the method, ten-fold cross validation and an adaptive integration strategy based on error reverse weighting are introduced, a prediction variance is introduced as a penalty factor, and an automatic hyper-parameter optimization framework based on Bayesian inference and a reinforcement learning controller is adopted, so that efficient collaborative tuning of multi-model hyper-parameters is realized; the generalization ability and prediction performance of the model under different working conditions are ensured, the manual parameter adjustment burden is reduced, and the automation level of the system is improved. The method overcomes the defects of the prior art in a high-dimensional, multi-noise and nonlinear environment, and has high practical value and popularization and application potential.

The invention discloses a multi-level visual feature dynamic fusion method and device, equipment and a medium, and the method comprises the steps: introducing a task-perceived and content-driven multi-level feature fusion mechanism into a visual language large model, and carrying out the multi-level feature fusion according to different contents of an input image and a text instruction. Fusion weights are adaptively distributed to all layers of features of a visual encoder, the comprehensive understanding ability of the model for image details and global semantics is remarkably improved, and the application range of the visual language large model in fine-grained perception and diversified scenes is expanded, that is, by introducing a dynamic weight distribution mechanism, the visual language large model can be more accurately identified. Adaptive integration of different levels of visual features is realized, and the comprehensive understanding ability of a visual language large model for image details and global semantics is effectively improved.

This invention provides a method and system for super-resolution reconstruction of low-quality images of mine tunneling faces. The method includes incorporating RSAB and SASSB modules into the HG structure. In SASSB, a semantic neighborhood scanning strategy is used to rearrange the feature sequences, and SASSM is used to recover the spatial structure at the hidden state level. In the MSMF module, local memories at each stage are aggregated using LMF, and GMF is used to adaptively integrate hierarchical memories of different depths. The system includes a shallow feature extraction module, a deep feature extraction module, and a reconstruction module. This invention simplifies computation and enables efficient combination of global and local information, while improving the ability to recover spatial structure and utilize hierarchical memory information.

The invention discloses a wire harness self-adaptive integrated structure which comprises a multi-core signal connector plug shell used for fixing a wire harness and a substrate used for being attached to a base in an electric connector, and the multi-core signal connector plug shell is connected to the substrate through a floating assembly. The floating assembly comprises a shell used for being connected with the substrate, a connecting rod used for being connected with a shell of the multi-core signal connector plug, a ball head arranged at the end of the connecting rod and a pressing plate elastically connected into the shell, and the radial section, close to one end of the shell of the multi-core signal connector plug, of the shell is arc-shaped or V-shaped. Various wire harnesses at the position of the battery pack are integrated on the electric connector in a mode of simple structure, small size and low cost, so that wiring near the battery pack of a new energy automobile is neater, and the problems of wire harness breakage, deformation, winding and wiring falling caused by vibration during driving can be prevented.