126 results about "Web modeling" patented technology

Disclosed in the present invention is a four-network integration architecture for an unmanned swarm system. The four-network integration architecture has the capabilities of heterogeneous platform resource pooling, intelligent dynamic computing power allocation, and timely decision planning, so as to maximize the overall benefit. The present invention focuses on abstracting and integrating independent submodules to form a mesh topology of a swarm. The present invention designs a four-network integration architecture for an unmanned swarm system, which comprises a computing power network, a perception network, a decision network and a communication network as core modules. The structure aims to achieve efficient cooperation of all parts in the swarm, thereby improving the overall performance and adaptability of the system. The system integrates environmental perception, a swarm network modeling component, a knowledge base and a resource pool, providing an intelligent environmental perception strategy and a network modeling strategy for the interior of the swarm. Therefore, the perception of environments, tasks and networks by nodes can be facilitated, thereby completing establishment of intelligent networks, so as to ensure the characteristics of the stability and flexibility of networks.

The invention relates to the technical field of industrial production lines, and discloses an industrial production line multi-device dynamic collaborative scheduling method based on reinforcement learning, comprising the following steps: S1, modeling a three-dimensional state space; s2, hierarchical reinforcement learning architecture; and S3, edge-cloud cooperative execution. According to the industrial production line multi-device dynamic collaborative scheduling method and system based on reinforcement learning, device states, task constraints and resource occupation are integrated into a structured matrix through three-dimensional state space modeling, and a global decision-making layer captures production time sequence dependence by using a bidirectional long-short-term memory network; modeling equipment space association and process constraints through a graph attention network, and generating a global strategy including task allocation, capacity adjustment and resource pre-allocation; and after the edge layer detects the dynamic event, the cloud platform generates a candidate scheme through Monte Carlo tree search, and realizes dynamic event response and multi-target collaborative optimization by combining multiple targets such as global value network evaluation task completion time and equipment load balancing.

The invention discloses a private domain data-based user portrait generation method and a private domain data-based content generation and touch method, and relates to the technical field of big data processing and pushing. Analyzing user behavior characteristics and interest preferences based on the collected data; marking static tags for the corresponding users based on the basic attributes; according to the behavior characteristics and interest preferences, constructing a primary dynamic tag; through user behavior data collected in real time, a time decay weighting algorithm is adopted to improve recent behavior weight and filter abnormal click data, a time sequence cross attention network is adopted to model a behavior feature sequence, interest preference and probability are output, and a dynamic threshold value is set to trigger dynamic label updating; and if the key behavior data of the user is collected, recalculating the dynamic tag based on the key behavior data and performing full-amount refreshing. According to the invention, a data-content-touch real-time linkage closed-loop system is constructed.

The invention relates to the technical field of metallurgical raw material batching control, and discloses a bottom blowing smelting furnace automatic batching intelligent control method and system based on deep learning, and the method comprises the steps: achieving intelligent batching through multi-source data fusion, physical constraint modeling and dynamic optimization control; edge calculation is adopted to realize data space-time alignment and purification, and physical and economic mixed features are constructed; modeling a reaction path based on a graph neural network, and embedding conservation law constraint to synchronously predict key process parameters; and finally, in combination with gradient sensitivity analysis and reinforcement learning, constructing a differentiable optimization framework to realize multi-target dynamic ratio decision and real-time compensation control, and forming a perception-decision-execution closed loop. The system comprises a global sensing and data purification module, an intelligent decision-making and optimization batching module and a high-precision execution and closed-loop control module. According to the invention, the batching strategy is adaptively adjusted, and optimal resource allocation and maximum economic benefit are realized.

The invention discloses a power system net load prediction method based on regular decomposition and double-branch prediction, and the method comprises the steps: obtaining a historical net load sequence of a target power system and corresponding environment parameters, constructing a target function fusing fitting precision and trend smoothness through employing a regularization optimization method, extracting a long-term trend sequence of a net load, and carrying out the calculation of the long-term trend sequence. And a short-term disturbance sequence is separated. Constructing a trend prediction sub-network based on series connection of a Transform encoder and a long-short-term memory network, and learning a trend evolution rule; meanwhile, a regression prediction sub-network based on environmental parameters is constructed, and a nonlinear mapping relation between disturbance and environmental factors is modeled. And utilizing the two types of sub-networks to respectively predict future trend and disturbance components and superpose the future trend and disturbance components to obtain a multi-time-step net load prediction result. According to the method, the problem that a traditional model is insufficient in trend and disturbance modeling capacity is effectively solved, and the accuracy and stability of load prediction are improved.

Systems and methods for supply chain network modeling and performance prediction are disclosed. In some embodiments, a disclosed method includes: receiving, from a computing device, a query associated with a supply chain network; representing the supply chain network as at least one graph based on historical transactions in the supply chain network; obtaining at least one machine learning model that is trained based on graph data related to nodes and edges in the at least one graph; generating, using the at least one machine learning model, supply chain prediction data based on the query; and transmitting the supply chain prediction data to the computing device.

The invention discloses an equipment health state monitoring method and system based on the Internet of Things. According to the method, data in a multi-source sensor is obtained and preprocessed, a real-time operation data set of equipment is obtained, and a health quantification deviation value and a health state trend are determined through the real-time operation data set. And when the health state is abnormal, recording a starting point coordinate of an abnormal event, marking an abnormal event triggering timestamp, and analyzing the dependency relationship between the equipment by utilizing graph network modeling. Furthermore, the potential risk probability is analyzed through the long-short-term memory network, real-time monitoring, fault propagation prediction and risk assessment of the equipment health state are achieved, and the operation reliability and the maintenance efficiency of the industrial equipment are improved.

The invention discloses an AI-based digital project performance evaluation data processing method and system, and relates to the technical field of artificial intelligence and digital project management, and the method comprises a multi-source data collection module which is used for obtaining data; the entity alignment module is used for establishing an association relationship among cross-system data entities; the dynamic index generation module is used for dynamically adjusting the evaluation index weight by utilizing a reinforcement learning framework; the efficiency prediction module is used for modeling a task dependency relationship according to the time sequence diagram convolutional network and outputting a delay risk probability; and the visual interface is used for displaying the data. According to the method, cross-system entity alignment, dynamic index weight adjustment, task delay risk prediction and root cause analysis are realized through AI technologies such as BERT semantic matching and a graph neural network, visual data display and interaction functions are provided by virtue of a visual interface, the problems of data dispersion, index static state, risk lag and the like in traditional project evaluation are solved, and the project evaluation efficiency is improved. And the decision support capability of project management is improved.

The invention provides a multimodal transport scheme generation system and method based on multiple agents. Comprising a task analysis module used for generating a task context; the network modeling module is used for acquiring data related to a transportation network and an operation state and constructing environment situation representation; the candidate path generation module is used for generating a candidate path set meeting constraints on the multimodal transport network; the resource checking module is used for carrying out resource availability and time sequence connection checking on the candidate path set; the decision module is used for generating an evaluation vector for the checked candidate path set and generating a structured description; and the output module is used for generating a scheme document and a machine readable instruction and providing the scheme document and the machine readable instruction to an external execution system. According to the method, environment situation representation unification of multi-source dynamic data, constraint-based candidate path set automatic generation and resource time sequence checking, target scheme selection and structured description generation under intelligent agent collaboration, event-triggered increment re-planning and machine readable instruction output can be realized.

The invention belongs to the technical field of flow instruments, and provides a dynamic compensation and error correction system of a high-precision flow instrument, which comprises a multi-dimensional data acquisition and preprocessing module, a module capable of being additionally provided with a sensor, a module for establishing a data stream with a timestamp and processing data, a module for fluid network modeling and priori knowledge construction, and a data processing module. A distributed collaborative optimization and parameter calibration module capable of constructing a directed graph model and generating initial compensation parameters; a network association reasoning and dynamic weight distribution module capable of adding a global penalty term to optimize parameters on the basis of a traditional error function; a working condition adaptation calibration and cross-domain parameter migration module capable of constructing an association graph, detecting abnormity and distributing weights; real-object-free calibration and parameter migration can be realized; according to the system, through multi-module cooperation, the problem that traditional single-table compensation is local and is not global is solved, and high precision and real-time performance of industrial-grade cooperative metering are supported.

The invention discloses an industrial control safety target range construction method and system based on a digital twinborn technology. The method comprises the following steps: constructing a digital twinborn simulation environment for an actual industrial scene, wherein the digital twinborn simulation environment comprises physical system modeling, communication network modeling and co-simulation modeling; defining an interactive interface between the simulation environment and an external tool, including selecting a communication mode according to a data exchange demand, defining a logic communication channel based on the selected communication mode and standardizing a data exchange format; converting an attack instruction initiated by an external tool into a simulation operation instruction in the simulation environment through the interactive interface, and feeding back a physical state change caused by the simulation operation instruction to the external tool; and an automatic target range resetting mechanism is established, and the state of the simulation environment is backed up and recovered through a snapshot mechanism. According to the invention, the management efficiency of the industrial control safety target range is improved, and the test reliability is improved.

The invention relates to the field of post capability modeling, in particular to an intelligent post capability dynamic modeling optimization system and method based on data analysis. The method comprises the following steps: carrying out hierarchical deep analysis on an enterprise organization structure, carrying out enterprise topology network modeling, and constructing an enterprise structure topology network; the method comprises the following steps: collecting enterprise employee multi-dimensional behavior logs based on an internal heterogeneous system of an enterprise, carrying out one-by-one employee behavior analysis and dynamic behavior interaction perception, and constructing a multi-post behavior interaction graph; performing staff performance time sequence calculation based on the multi-post behavior interaction diagram, performing multi-dimensional capability comprehensive evaluation, and constructing a post multi-dimensional capability evaluation model; spatial position calculation is carried out on the enterprise architecture topology network one by one, and a real-time adjustment post topology model is constructed. According to the method, dynamic personnel demand configuration analysis and adjustment are realized, the maximum utilization efficiency of talent resources is kept, and the enterprise personnel configuration flexibility is improved.

The invention discloses a defense strategy self-generation method and system for an intelligent device cluster, and belongs to the technical field of intelligent device cluster network security. The system comprises a network modeling module, an attack strategy integration module and a defense strategy dynamic generation module. The method comprises the following steps: constructing a network attack and defense game model to represent a network attack and defense scene of a current intelligent equipment cluster; acquiring a network topology of the intelligent device cluster, and selecting an attack strategy and an action path according to the network topology; and executing an attack behavior on the intelligent equipment cluster according to the attack strategy, obtaining a defense matrix of the current intelligent equipment cluster and the quantized detection capability of each network node cluster by a defense strategy dynamic generation module, and generating a defense strategy by adopting a deep reinforcement learning model. According to the invention, the problems of poor adaptability, insufficient collaboration, unreasonable resource scheduling and the like in the traditional defense technology of the intelligent equipment cluster network are solved, and the overall efficiency of network security defense is greatly improved.

The invention discloses an energy internet topology toughness evaluation method and system based on business criticality and energy-information coupling, and the method comprises the steps: 1) energy internet business-driven multi-dimensional heterogeneous network modeling and criticality quantification: constructing a business semantic ontology library, quantifying a business comprehensive criticality weight, mapping a business flow to a physical topology, and establishing a business semantic ontology library; endowing nodes and links with multi-dimensional service bearing attributes to form a multi-dimensional attribute enhanced network topology; 2) effective redundant path evaluation and vulnerability quantification based on the business QoS constraint: defining an effective path judgment criterion meeting the business QoS, calculating the number of weighted effective business disjoint paths, and quantifying the business key link / node vulnerability; and 3) performing energy-information coupling dependence modeling and cascade failure risk propagation assessment. According to the method, the accuracy and pertinence of evaluation can be remarkably improved, the coupling risk is comprehensively revealed, and refined decision support is provided for planning, operation and maintenance and toughness enhancement of the energy internet.

The invention discloses a cross-domain AI knowledge aggregation method based on collaborative filtering. The method comprises the steps that S1, multi-source heterogeneous AI knowledge data and user behavior data are collected and preprocessed; s2, constructing a double-tower cross-domain embedded network, and outputting a cross-domain semantic fusion sequence; s3, modeling and analyzing user preferences through the improved Bi-GRU network, and generating user behavior preference vectors; s4, performing semantic diffusion and neighborhood reasoning on the cold start user, and complementing interest features; s5, adopting a double-tower recall structure and an XGBoost model to sort and generate a cross-domain recommendation list; s6, constructing a context rule base to execute context adaptability judgment, and generating a matched knowledge aggregation recommendation list; and S7, performing incremental learning according to user feedback information, and dynamically updating the double-tower cross-domain embedded network and the improved Bi-GRU network. According to the method, the knowledge matching precision, the cold start adaptability and the scene adaptation capability of cross-domain recommendation content are improved.

The invention discloses a lightweight cross-domain recommendation method and system based on user alignment Agent driving. The method comprises the following steps: firstly, acquiring historical behavior data of a user in multiple fields, fusing multi-modal contents such as texts and images, generating a fine-grained interest prototype through a cross-domain semantic encoder, and constructing a personalized Agent to simulate the intention of the user; then, in a multi-field collaborative environment, an Agent behavior strategy is optimized by utilizing reinforcement learning and a mixed reward mechanism, general preference and field specific preference are modeled through a hierarchical strategy network, and knowledge fusion is realized through a gating mechanism; and then, in combination with a preference distillation technology, extracting transferable characterization from Agent behaviors, and constructing a lightweight cross-domain knowledge graph. Finally, behavior track compression and cross-domain preference mapping are adopted, and efficient and low-consumption personalized recommendation is achieved. According to the method, the problems of cross-domain data sparsity and model complexity are effectively relieved, recommendation accuracy and system response efficiency are improved, and the method is suitable for real-time recommendation service of multiple scenes.

A method for training a machine learning model using enhanced multilayer direct disassembly networks (MDDNs). The method includes converting MDDNs into knowledge graphs and using them to train a generative and a discriminator component. The generative component produces synthetic disassembly structures, which are evaluated by the discriminator. Feedback from the discriminator is used to refine the generative model, improving the accuracy of disassembly network modeling.

The invention relates to the technical field of mine safety production, in particular to a mine ventilation parameter dynamic optimization method based on industrial internet of things, which comprises the following steps: S1, data fusion perception and edge calculation; s2, modeling a digital twin ventilation network; s3, performing multi-objective optimization solution; s4, instruction issuing and feedback; and S5, model self-correction and knowledge base updating. According to the scheme, the multi-dimensional sensor cluster and the dual-mode communication network are constructed through the industrial Internet of Things, real-time sensing and differential transmission of mine ventilation parameters are achieved, the data cleaning and feature extraction technology of edge calculation is combined, the authenticity and effectiveness of input data are ensured, and true and effective data are provided for dynamic optimization of the ventilation parameters.

The invention relates to an enterprise risk early warning method and device based on big data, equipment and a medium. The method comprises the following steps: respectively extracting time dynamic characteristics and space topological characteristics of nodes through a dynamic heterogeneous risk map, and generating unified space-time embedding representation through cross-modal fusion; an event logic enhanced nonlinear propagation effect of a dynamic heterogeneous graph attention network modeling risk event is adopted, and enhanced node representation and global representation are output; calculating the deviation between the node and the community mean value through topological residual detection to obtain a topological residual score, and calculating the minimum similarity between the node and the known risk prototype through similarity analysis to obtain a representation similarity score; the two scores are linearly combined and normalized to generate an unknown risk score, and a known risk probability is calculated based on a pre-training model; the comprehensive risk index is obtained through maximum value operation, comprehensive monitoring and early warning of known and unknown risks of an enterprise are achieved, and the coverage range, accuracy and timeliness of risk identification are remarkably improved.

The invention discloses a cross-version vulnerability recognition system based on a depth map neural network, and the system comprises a multi-version code metadata collection module which is used for collecting software source codes of multiple versions and analyzing the software source codes to generate a multi-version code metadata set; the multi-version code graph generation module is used for constructing a multi-version code graph set; the feature representation matrix generation module is used for generating a feature representation matrix of the multi-version code graph; the depth map neural network modeling module is used for obtaining an embedded matrix of each node by adopting a GLEM model; the potential vulnerability node identification module is used for identifying a cross-version potential vulnerability node set; and the result output module is used for outputting a cross-version vulnerability identification result and generating a detection report. According to the method, the depth map neural network and the cross-version modeling method are adopted, multi-version vulnerability automatic recognition is achieved, and the method has the advantages of being high in intelligence, high in adaptability and accurate in detection.

The invention relates to the technical field of thermal energy engineering and industrial artificial intelligence crossing, in particular to a boiler combustion multi-target cooperative control method based on PINN and reinforcement learning, which comprises the following steps: collecting boiler combustion related data through a multi-source sensor network, and fusing edge data; based on physical information neural network modeling training, predicting a key physical field in the boiler; constructing and training a reinforcement learning model based on PINN state embedding; and a multi-target cost function is constructed based on the reinforcement learning model, the multi-target cost function is continuously evaluated through the reinforcement learning model, the weight is automatically adjusted, and multi-target cooperative control over boiler combustion is achieved. The method is obviously superior to a traditional scheme in the aspects of physical consistency, adaptive capacity, real-time performance and multi-target cooperation, and a reproducible theory-engineering integrated new normal form is provided for efficient, clean and flexible operation of a coal-fired power plant boiler combustion system under the double-carbon background.

The present application provides an intelligent production scheduling and exception management method based on large language models, which relates to the field of artificial intelligence technology and includes: converting user requests into structured data through a semantic parser and complementing fuzzy information in combination with the context; modeling production data using an attention-based relational graph neural network and retrieving relevant context information in combination with a retrieval-augmented generation framework; adjusting the scheduling strategy in real time according to user feedback and changes in the production environment; and generating scheduling instructions using a large language model in combination with the structured data and the scheduling strategy. Through dynamic knowledge base construction, hierarchical memory mechanism design, attention-based relational graph neural network modeling, and the semantic understanding ability of large language models, the intelligence level, logic, and adaptability of the production scheduling system are significantly improved.

In order to solve the key problems of difficulty in multi-source information fusion, insufficient spatial topology modeling, attenuation of long-term prediction precision and the like in an existing aircraft ground taxiing trajectory prediction method, a parallel processing architecture of a historical trajectory encoder and a pavement path encoder is designed, and trajectory time sequence features are extracted by using a long-short-term memory network; modeling a spatial topological relation of a control path by adopting a graph attention network, and realizing effective integration of heterogeneous information through a feature fusion layer; a multi-component loss function fusing the position, the speed and the acceleration is provided, and the continuity and the smoothness of a prediction track are constrained; a lightweight data enhancement strategy and an adaptive residual connection mechanism are designed, the model generalization ability is improved, and long-term prediction error accumulation is relieved. According to the method, multi-source trajectory information can be effectively fused, and the accuracy and stability of aircraft ground taxiing trajectory prediction are remarkably improved.

The invention discloses a knowledge base accurate calculation-oriented adaptive AI agent generation method, and relates to the technical field of artificial intelligence, and the method comprises the following steps: establishing a multi-source heterogeneous data collection interface matrix, constructing a semantic network modeling engine containing an RDF triple parser, designing a dynamic structure adjustment algorithm based on an LSTM-GRU hybrid neural network, and generating a semantic network model based on the LSTM-GRU hybrid neural network. And developing a task hierarchical computing framework. According to the self-adaptive AI agent generation method provided by the invention, by constructing the multi-source heterogeneous data acquisition interface matrix, the problems of large data format difference and uneven quality are effectively solved, the efficiency and accuracy of data acquisition and preprocessing are improved, efficient semantic alignment of heterogeneous ontologies is realized by utilizing a semantic network modeling engine, and the generation efficiency of the heterogeneous ontologies is improved. And the precision of entity disambiguation and relation reasoning is improved, powerful support is provided for dynamic processing and reasoning of knowledge, and the frequency can be updated according to the task complexity and data.

The invention belongs to the technical field of system modeling, and relates to a multi-innovation OIF Elman network modeling method based on measurement data, which comprises the following steps of: constructing a mathematical model of an OIF Elman network, and obtaining a to-be-estimated parameter matrix of the network by defining a parameter matrix; a sliding window mechanism is introduced, a to-be-estimated parameter matrix is utilized to define a multi-information criterion function, and a parameter estimation sub-algorithm for estimating the OIF Elman network weight is constructed; establishing a dynamic factor adjustment strategy by adopting an exponential decay and minimum value limitation mode; in the parameter estimation sub-algorithm, a dynamic factor adjustment strategy is introduced, and a gradient parameter estimation algorithm is constructed; and estimating a parameter matrix of the OIF Elman network model through a gradient parameter estimation algorithm based on the acquired input and output measurement data. The method is suitable for online modeling, and is superior to the existing method in the aspect of modeling precision.

The application provides a bearing health state online evaluation method and system based on morphological profile analysis and federal evolution hypergraph, aiming at solving the problems of poor model self-adaptability, difficult cross-device knowledge migration and easy to be submerged early weak fault characteristics of the prior art under dynamic working conditions. The method captures the geometric profile evolution of bearing micro-damage by constructing a morphological multi-scale profile feature extraction engine, topologically preserving morphological decomposition of the vibration signal; adopts a Bayesian Poisson online learning algorithm to realize dynamic threshold adaptive updating and early warning of the health index; introduces an evolutionary hypergraph neural network to model the high-order multi-element fault propagation relationship between the bearing and the adjacent components; finally, through a federal edge collaborative framework, the incremental aggregation and knowledge migration of the cross-device model are realized under the premise of protecting data privacy. The application significantly improves the robustness of bearing fault diagnosis under variable working conditions and the sensitivity of early warning, and provides a lightweight and evolving solution for intelligent operation and maintenance in distributed industrial scenarios.

The application discloses a kind of based on the transport hub maintenance scheduling efficiency optimization method of deconstruction of variable topology super network, comprising the following steps: step one, the maintenance element involved in the current real-time maintenance scheduling scene of transport hub and its interrelated relationship mapping modeling is modeled into supergraph network model;Step two, the basic uncertainty characteristics of supergraph network CNN topological structure are characterized;Step three, according to the supergraph network model, the evaluation value of measurement index is calculated;Step four, the three indexes calculated are compared with the reasonable value interval range of historical scheduling result, and the rationality of current scheduling state is judged;Step five, based on the measurement index, the maintenance scheduling efficiency of transport hub is improved.The beneficial effects of the present application are that the execution efficiency, resource collaboration degree and dynamic robustness of maintenance scheduling can be effectively improved through super network modeling and quantitative analysis, and bottleneck positioning and optimization are realized.

The invention discloses an SBSIR model construction method and an IMGPE algorithm for identifying network node influence in the field of Internet social network modeling analysis. The method is improved on the basis of a traditional SIR. Different from a traditional infectious disease model, the SBSIR model not only considers a direct interaction relationship between users, but also introduces interest similarity as a spreading mechanism of information spreading, so that the SBSIR model is more in line with an actual spreading rule of an interest social network compared with the traditional infectious disease model. The IMGPE algorithm reconstructs the network based on interaction and interest features among users in the social network, and identifies nodes with influence in the interested social network by measuring local features and global features of the network and position features among the users.

The invention provides a multi-path routing method suitable for a non-order-preserving routing standard, and relates to the technical field of network routing, and the method comprises the steps: carrying out the network modeling of a common routing problem into a directed graph, and abstracting the directed graph into a routing algebra; aiming at the routing algebra of which the routing standard meets the monotonicity but does not meet the order-preserving property, obtaining the order-preserving property through a maximum order-preserving reduction technology; and calculating first K optimal paths from a source node to a destination node in the directed graph through a multi-path algorithm based on the routing algebra after order-preserving reduction, and setting an algorithm efficiency and path balance mechanism of the multi-path algorithm. According to the scheme, the method can be applied in a non-order-preserving environment, the application range of a routing algorithm is remarkably expanded, and the method can be widely applied to a routing decision scene in a complex network environment and has the advantages of being high in universality, stable in performance and high in path quality.

The application discloses a kind of cross-version vulnerability identification systems based on depth map neural network, comprising: multi-version code metadata acquisition module, for collecting multiple versions of software source code and parsing generation multi-version code metadata set;Multi-version code graph generation module, for constructing multi-version code graph set;Characteristic representation matrix generation module, for generating the characteristic representation matrix of multi-version code graph;Depth map neural network modeling module, for using GLEM model, obtains the embedding matrix of each node;Potential vulnerability node identification module, for identifying the potential vulnerability node set across version;Result output module, for outputting cross-version vulnerability identification result and generating detection report.The application adopts depth map neural network and cross-version modeling method, realizes multi-version vulnerability automatic identification, with the advantages of strong intelligence, high adaptability, detection precision.