4520 results about "Dynamic data" patented technology

The invention relates to a dynamic data pipeline construction method based on artificial intelligence and multi-modal data processing, and belongs to the technical field of data fusion treatment and artificial intelligence. According to the method, a multi-modal deep learning model is adopted, semantic analysis is performed on business documents, user requirements and function specification instructions, and key indexes, data requirements and logic constraints are extracted; constructing a multi-modal knowledge graph, performing unified representation and feature extraction on structured and unstructured data resources, and performing embedded learning by using a graph neural network; based on a deep semantic matching model and a hybrid recommendation system, realizing accurate matching of business requirements and data resources; modeling generation of a data processing flow into a Markov decision process by adopting a reinforcement learning strategy, and automatically generating an optimized data processing workflow; the adaptability of the model to new business requirements is improved by utilizing a meta-learning algorithm; and dynamic scheduling and execution of tasks are realized through a workflow engine. According to the invention, the data processing efficiency can be improved.

The present invention relates to the technical field of mine safety management, and discloses a method and system for dynamic monitoring and early warning of mine environment, including the following steps: multi-source data acquisition, collecting dynamic data of mine environment through remote sensing imaging equipment, ground sensors and meteorological stations; data preprocessing, noise cleaning, time alignment and spatial reference coordinate unification of the data; spatiotemporal data fusion, mapping multi-source data to a unified feature space through feature extraction, cross-modal fusion and attention mechanism; intelligent analysis and disaster prediction, predicting surface deformation, landslide risk and tailings dam stability in mining areas based on multi-task joint learning models; risk assessment and early warning response, dividing risk levels according to comprehensive risk scores, and dynamically generating early warning information, evacuation paths and dangerous area isolation boundaries. The present invention solves the problems of difficulty in multi-source data fusion, low disaster prediction accuracy and delayed early warning response in the prior art.

The invention relates to the technical field of intelligent decision making, in particular to a simulation system intelligent decision making method and system based on a knowledge graph and federated learning, and the method comprises the steps that each simulation node constructs a knowledge graph sub-graph based on local dynamic data, and multi-modal semantic representation is generated through space-time modeling and event chain reasoning; the federal center initializes a simulation decision model architecture and issues the simulation decision model architecture to each node; each node uses a local knowledge graph to train a time sequence diagram network, extracts an equipment degradation path and abnormal propagation characteristics, and uploads gradient parameters in combination with homomorphic encryption; the federation center fuses the multi-node model through a dynamic weight aggregation algorithm to generate a global decision model; and driving knowledge graph evolution based on real-time data, and performing causal reasoning and decision optimization on an event chain through a federal model. According to the method, the problem of insufficient dynamic decision adaptability in a multi-source data island and privacy sensitive scene in a simulation system is solved.

The invention discloses an industrial equipment real-time monitoring system based on edge computing, and the system comprises the following steps: collecting, processing and analyzing the operation data of industrial equipment through distributed edge computing nodes, constructing a node cooperation mechanism, and completing the processing of the data; the dynamic data scheduling module is used for dynamically optimizing a data transmission path and task allocation according to node performance; the intelligent model dynamic migration module is used for adjusting lightweight model deployment in combination with node resource conditions; the multi-modal fault sensing module is used for extracting and analyzing multi-modal features by using a cross-node collaborative algorithm; the edge node self-adaptive management module is used for adjusting node operation parameters in real time and repairing an abnormal state; and the equipment relevance reasoning module is used for constructing an equipment relevance model through a graph calculation method and generating a real-time relevance graph. According to the method, efficient monitoring and anomaly detection of industrial equipment are realized through resource optimization and intelligent analysis of collaborative edge computing nodes.

The invention relates to the technical field of data processing, and discloses a multilevel cloud storage cluster construction and data storage method. The method comprises the steps that S1, multiple levels of storage layers are constructed and comprise a cache layer, a performance layer and a capacity layer, and the storage layers are provided with different storage media which are used for storing hot data, warm data and cold data respectively; s2, determining data popularity by monitoring data access frequency, access time interval and data importance; and S3, dynamically adjusting the storage hierarchy of the data according to the data popularity, and triggering hierarchical migration of the data. By constructing a multi-level storage layer and adopting a hierarchical architecture that the cache layer stores high-frequency data, the performance layer stores intermediate-frequency data and the capacity layer stores low-frequency data, high-heat data is preferentially stored in a high-performance medium, access delay is reduced, and data reading and writing efficiency is improved. Meanwhile, in combination with a dynamic data migration strategy, the storage position can be adjusted in real time according to the data access popularity, and the overall performance of the system is further optimized.

The invention relates to the technical field of agricultural traceability, and discloses an agricultural non-point source pollution intelligent traceability method and system based on a knowledge graph, and the method comprises the steps: achieving the system integration of pollution source features through constructing the knowledge graph fusing multi-dimensional data, and building a sky-ground integrated monitoring network to obtain multi-scale dynamic data. An intelligent traceability mechanism is formed based on deep coupling of a knowledge graph and monitoring data, a high-precision pollution identification model is trained in combination with historical data, and real-time response and dynamic traceability of an over-standard pollution area are achieved. And verifying a traceability result through feature matching and semantic reasoning, quantitatively calculating a pollution contribution rate, and finally generating a reliable traceability conclusion containing the position, the type and the contribution rate. According to the scheme, the bottlenecks of data fragmentation, monitoring simplification, extensive analysis and the like of a traditional method are broken through, and the accuracy, timeliness and credibility of pollution traceability are remarkably improved through a series of full-chain technical systems of traceability identification, analysis and positioning.

The embodiment of the invention provides a power distribution network disaster risk assessment method and system based on multi-source big data, and the method comprises the steps: obtaining a multi-source dynamic data set associated with a power distribution network, carrying out the multi-source feature deep coupling of the multi-source dynamic data set, and generating a power distribution network risk coupling feature set; the power distribution network risk coupling feature set comprises an equipment state coupling feature, an environment interference coupling feature and a topological correlation coupling feature; inputting the power distribution network risk coupling feature set into a preset risk situation coupling deduction model to perform multi-dimensional risk situation coupling deduction, and outputting a power distribution network disaster risk situation map; key node risk traceability coupling analysis is carried out based on the power distribution network disaster risk situation map, and a power distribution network weak link set and a risk evolution dynamic parameter set are determined. According to the method, the weak link in the power distribution network can be accurately positioned, the change rule of the risk along with time can be captured, and the improvement from static recognition to dynamic traceability and evolution prediction is realized.

The invention discloses a fusion optimization method based on Kalman filtering and LSTM cascade. The fusion optimization method comprises three steps of dynamic state estimation, time sequence error modeling and closed-loop fusion optimization. IMU (Inertial Measurement Unit) data is used as input, dynamic state estimation is realized through Kalman filtering, modeling system errors are separated, an LSTM (Long Short Term Memory) network is used for carrying out time sequence modeling on a residual error sequence to capture nonlinear errors, and finally, a corrected state quantity is fed back to a Kalman filtering updating link through a closed-loop feedback mechanism. And collaborative optimization of error compensation and state estimation is realized. According to the method, IMU error accumulation is effectively inhibited through a dynamic-data dual-drive mechanism, the navigation precision and robustness in a complex scene are remarkably improved, and the requirements of high-dynamic applications such as intelligent driving and unmanned aerial vehicle navigation can be met. Finally, the optimized IMU data and the GNSS observation value are fused for integrated navigation, high-precision navigation solution is achieved through Kalman filtering, and indoor and outdoor seamless positioning and the all-attitude control requirement of a high-dynamic carrier are met.

The invention discloses a perception intelligent driving complex traffic scene dynamic risk prediction method, and relates to the technical field of risk prediction, and the method comprises the steps: obtaining the multi-source traffic dynamic data of a target region in real time; performing space-time alignment processing on the multi-source traffic dynamic data; inputting the space-time coupling feature matrix into a pre-trained depth space-time prediction model to generate a risk thermodynamic map; calculating a dynamic risk index of each traffic sub-region, and generating a risk level distribution sequence; and triggering a self-adaptive early warning response mechanism according to the risk level distribution sequence, and dynamically adjusting operation parameters of the variable information sign and the traffic signal controller. The technical problems of frequent traffic congestion and high accident risk caused by inaccurate traffic risk prediction and difficulty in dynamic adjustment according to the real-time traffic condition in the prior art are solved, and the technical effects of accurately predicting the traffic risk and improving the safety and traffic efficiency in a complex traffic scene are achieved.

The invention discloses an intelligent resource scheduling method and system based on a dynamic data consanguinity atlas, and relates to the technical field of resource scheduling, the method comprises the following steps: collecting execution logs and flow metadata of tasks in a computing platform in real time, and constructing a dynamic directed weighted consanguinity atlas; calculating a blood relationship influence coefficient of each node in the dynamic directed weighted blood relationship map; obtaining a to-be-scheduled task, and calculating a comprehensive priority score based on the dependency weight of the dynamic blood relationship map of the to-be-scheduled task, the real-time load state of the target node and the scheduling execution time delay; performing priority ranking on the to-be-scheduled tasks based on the comprehensive priority score, and allocating cluster resources; and predicting the load trend of the target node, and triggering a migration decision when detecting that the predicted load of the target node exceeds a threshold value and the weight ratio of the key consanguinity tasks borne by the target node exceeds a preset threshold value. Through a dynamic consanguinity map and an intelligent scheduling algorithm, high efficiency and fairness of resource allocation are realized, the cluster utilization rate is improved, and task delay is reduced.

The invention discloses an artificial intelligence-based engineering resource allocation optimization method and system, and relates to the technical field of artificial intelligence and resource management crossing. The problems of resource configuration dynamic change and resource conflict coordination are solved. And processing the multi-modal heterogeneous data in the target engineering scene through space-time alignment and semantic coding to generate a dynamic data stream. A task, resource and environment node relationship is constructed based on a dynamic heterogeneous graph network, and a node dependency weight is updated through an event-driven mechanism. Two-stage collaborative optimization is executed, in the first stage, a baseline scheme is generated through Monte Carlo sampling, and in the second stage, resource conflicts are eliminated through back propagation negotiation. The resource dynamic entropy is monitored in real time, a rebalance algorithm is triggered during local overload, and a distribution scheme is adjusted in combination with security constraints. And finally, outputting the optimization scheme to an engineering management system for execution, dynamically returning updated graph network parameters, forming a data acquisition, optimization decision and feedback closed loop, and improving the intelligence and robustness of resource allocation.

The invention provides an energy-saving temperature control optimization method and system based on a central air-conditioning simulation platform, and relates to the field of central air-conditioning simulation platforms, and the method comprises the steps: collecting the operation parameters of a central air-conditioning system in real time through an Internet of Things sensor network, constructing a multi-dimensional dynamic data set in combination with historical data and outdoor meteorological data, and carrying out the calculation of the multi-dimensional dynamic data set; a physical model and machine learning hybrid driven simulation engine is adopted, a dynamic thermodynamic model of the central air-conditioning system is established, and a multi-objective optimization function of a simulation platform model is defined according to user comfort requirements, energy consumption cost constraints and environmental policy indexes; and adopting a hybrid optimization strategy of fusion of reinforcement learning and a genetic algorithm, iteratively optimizing control parameters in the simulation platform model, deploying a digital twin system according to the energy-saving strategy set, predicting potential faults through a virtual sensor, correcting the parameters of the simulation platform model, and realizing closed-loop control and continuous optimization. The method is used for overcoming the defect that a closed-loop feedback mechanism is lacked in the prior art.

The invention discloses an urban greening system based on big data and an optimization method thereof, relates to the technical field of artificial intelligence, and solves the problems of difficulty in heterogeneous data integration, insufficient real-time analysis capability of dynamic data and low management efficiency in an existing urban greening system. Multi-source heterogeneous data are collected through an API interface and Internet of Things communication, and are cleaned, aligned, converted and fused through a distributed real-time data stream processing platform; image recognition and a machine learning algorithm are used for monitoring diseases and insect pests in real time, and a growth prediction model is trained in combination with an LSTM network, so that the quick response capability of the system to environmental changes is enhanced; according to the method, target weights are dynamically adjusted and optimized through the deep Q network, a multi-target optimization model is constructed in combination with mixed integer linear programming, context awareness and a genetic algorithm, intelligent optimization of irrigation, fertilization, pest control and greening layout is achieved, and the management efficiency and the resource utilization rate of an urban greening system are remarkably improved.

The invention relates to the technical field of remote sensing monitoring, in particular to a remote sensing image ocean green tide monitoring method and system fusing physical prior and spatio-temporal evolution. The method comprises the following steps: acquiring a multi-modal remote sensing monitoring image; performing multi-modal feature extraction on the acquired image, wherein the multi-modal feature extraction comprises spectral reflectivity feature extraction, ocean dynamics feature extraction and feature alignment and unified representation; establishing a physical prior of a green tide characteristic wave band by using an ocean optical radiation transmission model; constructing a dynamic space-time diagram based on the extracted multi-modal features to obtain a node global feature vector and a dynamic adjacency matrix; carrying out adaptive graph convolution feature coding based on physical prior and a dynamic space-time diagram; through fusion of multi-spectral images of multiple platforms such as satellites and unmanned aerial vehicles and ocean dynamic data and combination of atmospheric correction and wave band resampling, consistency processing and high-precision extraction of multi-source features are realized, and comprehensiveness and reliability of green tide feature recognition are remarkably improved.

The invention provides a supply chain collaborative material management system and method, and relates to the technical field of material management, and the method comprises the steps: receiving a multi-enterprise heterogeneous material data flow, and constructing an industry knowledge graph based on the field-level semantic mapping of a dynamic ontology library; a standardized data table is generated; standardized data is injected into a distributed message queue for parallel processing, dynamic cache partitions are created according to material object labels, timestamp indexes are built in the dynamic cache partitions, a sliding window mechanism is adopted to scan data, and multi-strategy resolution is performed on conflict data in a window; performing conjoint analysis on the cleaned data to generate an enhanced analysis report, and activating a case transfer learning mechanism to update a model when the similarity between new data and historical cases exceeds a threshold value; constructing a material state transfer matrix for the abnormal data, simulating a correction operation in a digital twin environment, and triggering automatic correction; the problems of multi-enterprise heterogeneous data integration, dynamic data processing, intelligent analysis and the like are solved, and the collaboration efficiency and the management level of a supply chain are improved.

The invention relates to the field of shale oil and gas, and discloses a method for identifying a shale oil and gas reservoir dessert, which comprises the following steps: acquiring a core CT image, three-dimensional seismic data and production dynamic data, and carrying out cross-scale preprocessing; constructing a fractional-order non-local seepage field model to represent nano-to-kilometer-level flow characteristics; predicting seepage field parameters through a Lie group symmetry constrained neural network; establishing a cross-scale coupling model of the quantum adsorption effect and the macroscopic seepage law; dynamically updating model parameters based on real-time monitoring data; and executing multi-target collaborative optimization to generate a sweet spot three-dimensional distribution and development scheme. According to the method, a fractal dimension dynamic constraint cross-scale data fusion technology is adopted, the effect of accurate mapping of nanopore and macroscopic fracture network parameters is achieved, and the problem of misalignment of CT scanning and seismic inversion data space registration is solved through pore communication fractal analysis.

The invention discloses a low-altitude aircraft take-off and landing platform site selection optimization method. The method comprises the steps that real-time dynamic data and static GIS data including urban traffic flow data, meteorological data, landform data, environment data and POI data are acquired; constructing an urban three-dimensional digital model according to the static GIS data, and constructing a digital twin model according to the three-dimensional digital model and the real-time dynamic data; constructing environment constraint conditions and safety constraint conditions of candidate take-off and landing platform positions in the digital twin model; performing multi-objective optimization on the digital twin model through a particle swarm optimization algorithm, and generating an optimal candidate take-off and landing platform site selection scheme set meeting environment constraint conditions and safety constraint conditions; and dynamically updating the digital twin model according to real-time data feedback, and dynamically adjusting the site selection scheme of the take-off and landing platform. According to the method, the virtual city model is constructed through the digital twin, an accurate simulation environment and real-time feedback are provided for site selection optimization, and the accuracy and feasibility of a site selection scheme are improved.

The invention discloses a large container crane intelligent early warning system based on personnel behavior analysis, and the system comprises the following steps: collecting operator behaviors, equipment operation states and environment parameters in real time through a multi-mode sensor network, constructing a dynamic data set, extracting behavior characteristics through a time difference embedding algorithm, and carrying out the early warning of the operator behaviors, the equipment operation states and the environment parameters; a multi-level spatial-temporal characteristic matrix is generated in combination with dynamic scene influence factors, regional risk factors are dynamically calculated by using a Bayesian inference model, comprehensive risk values are generated through an adaptive multi-objective optimization algorithm, risk levels are divided into low, medium and high classes, and a system triggers graded early warning responses according to the risk levels. And an optimized behavior adjustment suggestion is generated in combination with a quantum probability behavior prediction algorithm, and a feature extraction rule and risk assessment logic are dynamically optimized through a progressive learning algorithm to form a closed-loop optimization process. According to the invention, intelligent analysis and real-time early warning of personnel behaviors are realized, and the safety and adaptability of the crane in an operation environment are improved.

The invention relates to the technical field of municipal road construction, and discloses a municipal road construction section scheduling optimization method and system based on artificial intelligence, and the method comprises the steps: collecting the multi-source dynamic data of a construction region through Internet of Things equipment and a satellite remote sensing technology, and constructing a time sequence database; a multi-objective optimization model is established, and an initial scheduling scheme is generated by adopting a non-dominated sorting genetic algorithm with the goal of minimizing construction period, resource waste and traffic jam influence. Dynamically adjusting the priority of the construction section by using a reinforcement learning algorithm, and updating a construction task sequence; a graph neural network is utilized to detect space-time conflicts of construction sections, and conflict-free scheduling constraint conditions are generated; and optimizing resource allocation based on mixed integer linear programming, simulating a construction process in combination with a digital twinborn technology, correcting a construction progress deviation by using a Kalman filtering algorithm, and outputting a final scheduling instruction. According to the invention, optimization of municipal road construction section scheduling is realized, the construction period is effectively shortened, resource waste is reduced, and traffic jam is relieved.

The invention relates to the technical field of intelligent fault diagnosis, and discloses a fault diagnosis method for new energy power generation equipment, and the method comprises the steps: obtaining a multi-physical-quantity time dynamic data set; outlier elimination is carried out on the data set, and a deviation degree sequence of each physical quantity is extracted; based on the deviation degree sequence, normalization processing is completed, and a correlation matrix among multiple physical quantities is constructed; generating a preliminary network structure through correlation screening and symmetry completion; calculating a path weight based on the initial network structure and performing topology reconstruction to obtain a final network topology structure; performing deviation propagation analysis according to the final network topology structure, and determining potential fault position distribution; based on the fault position distribution, performing fault grade classification by adopting a support vector machine algorithm, and outputting a fault risk grade label; and in combination with the fault level label and the network topology structure, node risk scoring and area identification are executed, and finally fault positioning is completed. According to the method, accurate positioning of the fault position in a complex system can be realized.

The invention provides a tunnel fan group cooperative control dynamic optimization method and system. According to the method, dynamic data of vehicles in a tunnel are tracked through the Leiyu fusion technology, and a multi-dimensional game parameter set is constructed in combination with edge computing node analysis data and environment monitoring indexes. Based on a dynamic game theory model, fan cooperative response intensity is dynamically adjusted, smoke exhaust efficiency and evacuation channel wind speed constraint are balanced, fan control priority is calculated, differential rotating speed and deflection angle control is driven, and game strategy weight is iteratively optimized after environment feedback data is generated. And realizing rolling time domain control until the vehicle movement and the environment index reach a stable interval. According to the technical scheme provided by the invention, dynamic collaborative optimization of tunnel smoke exhaust and evacuation safety is realized, and the ventilation response efficiency and the evacuation channel safety in an emergency scene are improved.

The present invention relates to an umbilical cable bending stiffness testing device and method. The device comprises a workbench; a measuring groove is formed in the top of the workbench; a fixing assembly is arranged in the measuring groove, and the fixing assembly is used for fixing two ends of an umbilical cable; a mounting plate is fixedly connected to the bottom of the workbench; a mounting frame is fixedly connected to the upper surface of the mounting plate; a threaded rod is rotatably connected between the lower surface of the mounting frame and the upper surface of the mounting plate; obtained data is transmitted to a dynamic data acquisition system by means of a displacement monitor body; the dynamic data acquisition system calculates obtained parameters to obtain bending stiffness curves of the umbilical cable under different curvatures. Moreover, because the device is in rigid connection with the umbilical cable by means of two sets of hoops, the problem of stress concentration caused by flexible connection of a steel strand rope is avoided, and the rigid connection ensures that tensile loads can be applied to two points of the umbilical cable in a balanced manner during an experiment, thereby improving the accuracy of bending stiffness testing results.

The invention discloses a data-mechanism dual-drive-based rock burst early warning method and system, and the method comprises the following steps: deploying a multi-modal sensor network to collect coal and rock stratum data, building a rock burst disaster precursor information sample database, providing a rock burst disaster multi-modal data precursor feature recognition algorithm, and carrying out the recognition of rock burst disaster multi-modal data precursor features. Mining the relevance between the multi-modal data and disaster-causing key risk indexes, and establishing a rock burst disaster multi-modal data prediction model; establishing a three-dimensional geological geometric model, fusing a multi-field coupling dynamics constitutive model and a catastrophe criterion, constructing a PINN physical information neural network prediction model of the rock burst disaster, and obtaining a time-space evolution rule of an energy field of a target area; providing a loss function coupling calculation method of a multi-modal data driving sample error and a physical driving control equation residual error, dynamic data and mechanism prediction result weight, comprehensively calculating a risk score, and accurately judging a top disaster danger level.

The invention discloses a data security dynamic evaluation system and a protection method, belongs to the technical field of information security, and is used for solving the problem of terminal equipment access control and behavior risk dynamic collaborative protection. The method comprises the following steps: generating fingerprints through terminal equipment features, verifying authorization, inputting access feature slices into a time sequence model by authorized terminal equipment, generating a trust score based on cosine similarity of a behavior sequence and a prediction sequence, and constructing a Markov model to dynamically select an authentication mode according to the trust score; calculating a risk index by combining network and geographic information, and distributing the risk index to a real or virtual environment; and the unauthorized terminal equipment distributes the virtual environment after registration. Differentiated monitoring is carried out, a time sequence library is established in the virtual environment, and doubt scores are generated through sequence matching; the state deviation of the real environment is calculated through a hidden Markov model, and the risk score is generated by fusing the multi-dimensional features. And based on the result management authority, the suspicion terminal equipment isolates and shrinks the authority, the compliance terminal equipment authenticates and migrates, and the access is regulated and controlled according to the minimum privilege principle and the time window mechanism.

The invention relates to the technical field of image processing, and discloses an AR technology-based precise positioning method and system for minimally invasive surgery of hepatobiliary surgery. The method comprises the steps that blood vessel network data are extracted through a medical image sequence, a three-dimensional structure diagram is generated, operation target points are marked, and a coordinate system is established; collecting dynamic data for mapping matching, and establishing a real-time tracking model; constructing an augmented reality scene and implanting a navigation path; acquiring an instrument position calculation track, and comparing with a navigation map to generate an instruction; constructing a projection interface to display the key area; recording the whole process and evaluating the operation quality. By establishing the real-time tissue deformation tracking model, a multi-source data fusion accurate positioning mechanism and an intelligent operation quality evaluation system, the accuracy and reliability of operation navigation are effectively improved.

The invention relates to a network real-time synchronization communication system, and relates to the technical field of computer network communication. The system comprises four core modules: a hardware optical IO synchronization module which integrates a multi-wavelength optical transceiver array and an anti-jitter circuit and provides a nanosecond global clock signal, and the synchronization precision is less than or equal to 15ns; the time sequence arrangement module is used for dividing a fixed time window and a dynamic buffer window based on a time sensitive network, supporting a priority preemption mechanism of the SRIO bus and realizing deterministic data transmission; the distributed RTDATA nodes replace a traditional single-board computer, a heterogeneous computing unit and an intelligent storage controller are integrated, and protocol stack processing delay is reduced through the zero copy technology; according to the dynamic multicast routing system, a safely isolated multicast tree is constructed as required, and link load distribution is optimized in combination with machine learning. Through collaborative design of hardware optical synchronization and dynamic data arrangement, the problem of real-time performance reduction caused by multi-node communication concurrence is solved.

The invention relates to a multi-source sea area data intelligent supervision method, system and device and a storage medium, and the method comprises the steps: obtaining sea area geographic space data and historical sea area supervision data of a target sea area, and carrying out the monitoring construction, and obtaining an island monitoring grid; acquiring multi-source monitoring data of the target sea area according to the island monitoring grid, and performing grid data identification to obtain a global pattern spot dynamic data set; extracting a suspected specific target area coordinate of the global pattern spot dynamic data set and calculating a coordinate specific target confidence coefficient; and carrying out range comparison on the suspected specific target area coordinates, and carrying out behavior evaluation in combination with the coordinate specific target confidence to obtain a sea area specific target early warning report.

The invention discloses a dynamic weight correction and path deviation probability prediction method for a vehicle track, which comprises the following steps of: acquiring vehicle data and multi-source dynamic data of the vehicle track in real time through an optical sensor, a radio wave sensor and an inertial navigation sensor, carrying out space-time calibration, extracting obstacle characteristics and road structure characteristics, and predicting the path deviation probability of the vehicle track. The obstacle movement trend is quickly captured through a space-time diagram sequence and a diagram convolutional neural network, real-time obstacle avoidance is realized in combination with dynamic weight adjustment, a driving intention is predicted by using a Bayesian neural network, a trajectory planning strategy is adjusted through weight correction, and uncertainty is reduced by using multi-source data fusion and probabilistic prediction. A closed-loop feedback mechanism continuously optimizes the model, and efficient operation is kept in complex scenes such as intersections and roundabout through real-time weight adjustment and closed-loop feedback, so that the purpose of quickly responding to dynamic obstacles or driving behavior changes can be achieved, and the precision of the path deviation prediction probability is improved.

The invention relates to the field of distributed machine learning, provides a big data-oriented distributed model training optimization method, and solves the problems of load imbalance, low resource utilization rate, large communication overhead, insufficient fault-tolerant efficiency and the like caused by data fragmentation staticization in the prior art. Load balancing is realized through intelligent clustering and overlapping control; the multi-dimensional heterogeneous resource evaluation model monitors calculation / storage / network indexes in real time, and realizes adaptive scheduling in combination with a task prediction and optimization algorithm; the hierarchical gradient synchronization mechanism adopts a tree-shaped parameter server and a dynamic compression technology, so that the communication traffic is reduced by 50%, and the precision loss is less than 0.8%; the incremental checkpoint system uses erasure code coding and parallel recovery to shorten the fault recovery time from 15 minutes to within 2 minutes, the resource utilization rate reaches 85% or above, the convergence speed is improved by 30%-40% in ResNet, BERT and other model training, and the large-scale training efficiency and the system stability are remarkably optimized.

The invention relates to the technical field of data storage, in particular to a dynamic data distribution storage method and system for an RAID (redundant array of independent disks), and the method comprises the following steps: based on the access frequency of data blocks in a disk, recording the access frequency and timestamp of each data block, carrying out heat rating on the data blocks, and determining thresholds of hot data and cold data. According to the method, the dynamic distribution of data storage is effectively optimized by analyzing the access frequency of the data blocks, and the performance and the economic efficiency of a storage system are remarkably improved by migrating the frequently accessed data to the cache disk and migrating the less accessed data to the low-cost storage; according to the method, unnecessary occupation of high-performance storage resources is reduced, the operation cost is also reduced, heat rating is performed on the data blocks, and the storage positions of the data blocks are correspondingly adjusted, so that the data processing speed is higher, meanwhile, the data security is improved, when a hard disk fails, data recovery is faster, and the service continuity is ensured.