1997 results about "Topological graph" patented technology

The invention relates to the technical field of model analysis, in particular to a time-space fusion neural network line topology analysis method for a power distribution network. The method comprises the following steps: obtaining original line topology data corresponding to a power distribution network, and carrying out structured disassembly and preprocessing to construct a space-time double graph structure; constructing a bidirectional dynamic feature interaction mechanism based on the space-time double graph structure, performing multi-scale topological feature extraction, and generating a space-time separated feature vector set; performing deep coupling fusion on the feature vector set subjected to time-space separation to generate corresponding unified topological feature representation containing abnormal topology; and constructing a dynamic topology state prediction model based on the unified topology feature representation to optimize a space-time joint loss function and output a corresponding real-time topology connection relationship and an equipment state change trend, and meanwhile, performing dynamic topology reconstruction to generate a current-moment reliable topological graph corresponding to potential branch disconnection and temporary tripping. The topology analysis accuracy of the power distribution network can be improved.

The invention provides an intelligent inspection robot path optimization method and system based on an edge inference model, and the method comprises the steps: obtaining an environment feature topological graph of a target inspection region, and determining an initial edge inference model; incremental training is carried out on the initial edge reasoning model through the real-time environment perception data flow, and a dynamic reasoning model adaptive to the current environment characteristics is generated; performing priority scoring on each path node in the environment characteristic topological graph based on a dynamic reasoning model, and generating an initial optimization path sequence; triggering a feedback calibration mechanism of the dynamic reasoning model according to the environment perception data flow updated in real time, performing dynamic path node replacement on the initial optimization path sequence, and generating a final inspection path; and controlling the intelligent inspection robot to execute an inspection task according to the final inspection path, and continuously collecting new environment sensing data streams in the inspection task process to update a parameter set of the dynamic reasoning model. According to the invention, the adaptability and reliability of path planning to a complex dynamic environment can be improved.

The invention discloses a base station facility abnormity intelligent monitoring method and system, and the method comprises the steps: employing a multi-scale wavelet fusion pulse neural network to construct a dynamic signal perception topology according to the real-time data of a base station physical layer, and generating a space-time aligned base station full-dimension feature tensor; inputting the full-dimensional feature tensor of the base station into a fault mode confrontation distillation module, reconstructing a normal working condition manifold of the base station based on a physical constraint variational auto-encoder, and outputting an abnormal feature vector with a fault fingerprint identifier; fault propagation graph modeling is carried out on the abnormal feature vectors, and a base station fault root cause topological graph is generated in combination with back propagation credibility verification; and inputting the base station fault root cause topological graph into a toughness self-healing strategy generator, and finally outputting a base station facility self-healing strategy set meeting real-time constraint. By utilizing the embodiment of the invention, the accuracy and the real-time performance of abnormity monitoring can be improved, and the operation reliability and the communication service quality of the base station are improved.

The invention relates to the technical field of cloud platform monitoring analysis, in particular to a cloud platform computing power resource performance monitoring and real-time scheduling optimization method, which comprises the following steps of: acquiring real-time performance data of heterogeneous computing nodes; performing multi-dimensional index fusion on the real-time performance data to generate a fusion performance index set containing time correlation features; constructing a dynamic computing power topological graph based on the fusion performance index set; generating an elastic scaling strategy set according to the dynamic computing power topological graph; mapping a to-be-allocated task to a physical node meeting the topology constraint condition; and carrying out topology conflict detection on the scheduling result through a feedback verification module, and dynamically correcting the parameter weight of the elastic scaling strategy set. According to the method, resource conflicts are reduced, the utilization efficiency of computing power resources is improved, and the system bottleneck problem caused by topology conflicts is avoided, so that the stability and the load balancing capacity of the system are enhanced.

The invention discloses a PCBA circuit board welding spot detection method based on multi-modal data fusion, and relates to the technical field of electronic manufacturing quality detection.The PCBA circuit board welding spot detection method comprises the steps that a distributed sensing network is constructed, multi-modal data are collected, welding spot information is obtained in an omnibearing mode, and time-space alignment of the multi-modal data is carried out; performing feature extraction on the multi-modal data, dynamically weighting each modal feature through an attention mechanism, and highlighting key defect characterization; a welding spot spatial topological graph is constructed by using a graph neural network, and a spatial relationship between welding spots is modeled. By integrating optical, X-Ray, thermal, mechanics, electricity and other multi-dimensional data, the information limitation of single-mode detection is broken through, the complementation of different mode data is utilized, the attention mechanism is combined to dynamically weight each mode feature, the complex defect is accurately identified, the graph neural network is utilized to model the welding spot space topological relation, the associated defect is further captured, and the defect detection accuracy is improved. And the defect classification accuracy is improved.

The invention discloses an underground oil and gas well fault prediction method based on a multi-modal space-time diagram neural network, and aims to solve the problems of insufficient multi-modal feature coupling, weak space-time correlation modeling, poor real-time performance and the like of a traditional method. The method is characterized in that multi-frequency sensor data features and maintenance log semantic knowledge are respectively extracted through a time expansion convolutional network (TCN) and a BERT-BiLSTM model, and a cross-modal gating attention mechanism is designed to realize heterogeneous data dynamic fusion; an equipment space topological graph is constructed based on Delaunay triangulation, dynamic causal association between nodes is quantized in combination with transfer entropy to generate a time graph, and a fault propagation path is jointly modeled through residual space-time graph convolution; a hierarchical prediction module is constructed by adopting a bidirectional LSTM and a graph attention network (GAT), short-term fault classification and long-term equipment residual life prediction are respectively realized, and the fault prediction precision and industrial landing feasibility under complex working conditions are effectively improved.

The invention relates to the technical field of industrial Internet of Things, in particular to a predictive maintenance method for intelligent factory Internet of Things equipment, which comprises the following steps of: acquiring equipment operation parameters, environment monitoring data and historical maintenance records, constructing a multi-dimensional feature data set, extracting equipment degradation features by adopting a topological graph attention mechanism and a Bayesian network, and establishing a multi-dimensional feature data set; the method realizes equipment health state modeling and fault probability prediction, combines a dynamic adjacency matrix and a multi-objective optimization algorithm, comprehensively optimizes maintenance cost, equipment fault risk and associated equipment influence, dynamically generates an optimal maintenance plan, carries out constraint optimization based on a mixed integer programming method, automatically generates a maintenance instruction sequence, and achieves the optimal maintenance of the equipment. Tasks are issued through the computerized maintenance management system, the PLC control system and the industrial Internet of Things gateway, and the execution state is monitored and maintained in real time. The intelligent level of equipment maintenance is effectively improved, non-planned shutdown is reduced, and the equipment reliability and the production efficiency are improved.

The invention relates to a disaster multi-source situation intelligent sensing management and control method and system. According to the method, hydrometeorological and topographic data are collected, and a standardized data set is generated through space-time alignment and anomaly cleaning; constructing a directed topological graph containing node and edge attributes based on the extracted river network topological relation; designing a neural network model, and training through a physical constraint loss function embedded in a water balance principle to obtain a flood dynamic routing prediction model; inputting real-time hydrological data into the model for graph convolution operation, and predicting water level, flow and split ratio changes of each node in a future time period; and finally, carrying out submerging simulation analysis in combination with a digital elevation model, and generating a flood control scheduling scheme and risk early warning information. The deep fusion of a physical mechanism and data driving is realized, the flood propagation rule under the river network topology constraint is effectively captured by using the graph neural network, the calculation efficiency is remarkably improved while the prediction precision is ensured, and real-time and reliable decision support is provided for flood disaster prevention and control in a complex river network region.

The invention provides a multi-line collaborative scheduling method and device based on artificial intelligence, and the method comprises the steps: obtaining a resource scheduling topological graph, receiving a task execution request, and analyzing a resource demand type and an execution constraint condition corresponding to the task execution request according to a task demand parameter carried in the task execution request; traversing all hierarchical nodes in the resource scheduling topological graph from an initial hierarchical node of the resource scheduling topological graph, and obtaining a resource prediction model or a task allocation model associated with the current traversed hierarchical node; based on the resource demand type and the execution constraint condition, generating a resource allocation instruction corresponding to the current traversal hierarchy node through a resource prediction model or a task allocation model associated with the current traversal hierarchy node; and according to the resource allocation instruction, dynamically allocating a calculation task corresponding to the task execution request to the target resource node, and triggering the target resource node to execute the calculation task. According to the invention, the resource allocation precision and the system robustness can be improved.

The invention belongs to the technical field of bridge monitoring, and relates to a system and a method for fusing multi-source data of a bridge structure. Comprising a heterogeneous topological graph construction and manifold embedding technology module, a multi-scale space-time cognitive convolutional neural network module, a continuous manifold space-time alignment and Bayesian fusion module and a structure health index calculation and state evaluation module. The heterogeneous topological graph construction and manifold embedding technology module is used for obtaining a heterogeneous topological graph, a node embedding vector and a manifold model parameter; the multi-scale space-time cognitive convolutional neural network module is used for performing deep feature extraction on the heterogeneous topological graph to obtain multi-scale fusion features; the continuous manifold space-time alignment and Bayesian fusion module is used for obtaining space-time alignment parameters and fusion state vectors; the structure health index calculation and state evaluation module is used for carrying out structure health monitoring and state evaluation on the bridge to obtain a final health evaluation result; therefore, the intelligence, automation and reliability levels of the structure monitoring system are improved.

The invention provides a virtual power plant platform source network load storage equipment real-time monitoring and optimization method and system, and relates to the technical field of virtual power plant intelligent optimization scheduling, and the method comprises the steps: collecting equipment operation data, carrying out the noise reduction and feature compression to obtain feature data, and recognizing the equipment fault type based on the feature data; carrying out decoupling reconstruction on the feature data in time and space dimensions to obtain an equipment incidence matrix, constructing an equipment topological graph based on the equipment incidence matrix and a credible fault type, carrying out bidirectional tracking on the equipment topological graph according to a fault diffusion probability, and generating a fault propagation path; grading and sorting the equipment on the fault propagation path, and outputting a fault influence prediction result and an equipment priority list; and carrying out cooperative scheduling optimization on the equipment in the virtual power plant by adopting a hierarchical reinforcement learning algorithm to realize cooperative scheduling optimization of the equipment in the virtual power plant.

The invention relates to the technical field of order management, and discloses a supply chain-oriented intelligent order management method, which comprises the steps of obtaining corresponding multi-modal data through an order demand flow, a production equipment state, logistics sensor dynamic information and an inventory topological graph; analyzing relevance between orders and equipment based on a space-time diagram convolutional network, and generating a capacity allocation scheme; calculating a logistics path planning scheme, predicting a stock stockout risk and generating a replenishment suggestion; if the high-priority order exists, inserting a productivity plan and adjusting an equipment process chain; if resource conflicts occur, dynamically allocating resources; if the path risk value exceeds the threshold value, standby path switching is triggered; adjusting weighting parameters through an adaptive federation algorithm, generating a global strategy and issuing the global strategy to the client; the client dynamically adjusts local configuration and uploads execution effect data in real time; and if abnormity is detected, triggering global strategy regeneration and updating the model through federated learning increment. According to the invention, efficient management of supply chain orders can be realized.

The invention provides a data all-flash storage optimization method and system based on cloud computing, and the method comprises the steps: receiving a storage operation request stream of a user terminal through collecting a multi-dimensional performance data set of each node of an all-flash storage cluster in real time, wherein the multi-dimensional performance data set comprises a storage space fragmentation rate, an input / output request queue depth and a network link bandwidth utilization rate; analyzing a to-be-stored data block identifier set and an operation mode label, traversing a historical access record library based on to-be-stored data block identifiers, extracting historical access time sequence characteristics and physical storage position proximity of associated data blocks, and combining the multi-dimensional performance data and the historical access time sequence characteristics to obtain a data block identifier set; according to the method, a dynamic access popularity prediction model in a preset time period is constructed, and finally, a cross-node fragmentation storage topological graph and a cache preloading strategy matrix are generated according to the proximity of the dynamic access popularity prediction model and a physical storage position, so that the optimization of all-flash storage is realized, and the data storage performance in a cloud computing environment is improved.

The invention discloses a defect detection method and system based on joint distribution optimization and structural knowledge guidance. The method comprises the following steps: analyzing an equipment structured document to construct an equipment physical topological graph containing a physical connection relationship; processing the topological graph by adopting a graph convolutional network to generate a structured feature vector, and extracting a sensing feature vector of the multi-modal sensing data; constructing a knowledge-enhanced attention model, and guiding a perception feature vector to perform cross-modal fusion by using a structured feature vector as a key and a value; and training the model through a combined optimization target containing joint distribution divergence loss and Lipschitz stability constraint so as to ensure the consistency and physical authenticity of the multi-modal features in the shared semantic space. According to the method, the defect detection accuracy can be remarkably improved, and the root cause diagnosis report with physical interpretability is generated.

The invention discloses a power grid power flow risk control method based on topology analysis, and relates to the technical field of power flow risk control, and the method comprises the following steps: obtaining first data of a power grid in real time, and constructing a node admittance matrix; when a switch displacement signal or distributed power supply power mutation is detected, topology reconstruction is carried out based on a node admittance matrix, and a dynamic topological graph is generated; establishing a fault current characteristic matrix based on the dynamic topological graph in combination with the bidirectional power flow characteristics of the active power grid; establishing an adaptive risk early warning model based on the dynamic topological graph and the fault current characteristic matrix, and outputting the overload probability and the voltage out-of-limit risk index of each branch; and performing analysis according to the branch overload probability and the voltage out-of-limit risk index, and generating a multi-target optimization control instruction set. Through adaptive topology adjustment, load flow calculation and a risk early warning model, automatic power grid optimization control can be realized, and in combination with convergence verification of a virtual impedance observer, effectiveness of topology adjustment and control operation is ensured.

The invention relates to the technical field of cloud computing, and discloses a resource scheduling method, device and equipment, and the method comprises the steps: generating a multi-dimensional data topological graph of cloud environment monitoring data, decomposing a to-be-scheduled job set into quantum computing unit sets which can be processed in parallel, and adding priority labels for the quantum computing unit sets through a quantum heuristic algorithm, according to priority labels and a multi-dimensional data topological graph, physical resources of a cloud environment are divided into non-uniform resource fragments matched with task requirements, based on the non-uniform resource fragments, resource scheduling is carried out through an improved hybrid particle swarm optimization algorithm, and weighting and fitness evaluation and resource constraint correction are introduced. According to the resource scheduling method and device, the multi-dimensional data topological graph is generated to comprehensively present the resource state and association, then the to-be-scheduled job set is decomposed into the quantum computing unit sets, the priority labels are added, and then the non-uniform resource fragments are divided for resource scheduling, so that the resource adaptation accuracy is improved, and the scheduling efficiency and the resource utilization rate are improved.

The invention provides an intelligent mechanical safety protection system based on industrial Internet of Things equipment, which comprises a dynamic topology modeling module, a risk propagation analysis module, a dynamic protection generation module and a collaborative triggering execution module, the composite topological graph comprises a physical layer and a logic layer; the risk propagation analysis module is used for analyzing a propagation path of the risk along a physical connection edge and a logic dependence edge based on the composite topological graph, and generating a risk propagation full-link sub-graph; the dynamic protection generation module is used for calculating a minimum necessary protection set based on the risk propagation path and generating a hierarchical response strategy; and the cooperative triggering execution module is used for sending a cooperative control instruction to the equipment in the minimum necessary protection set based on the hierarchical response strategy. By adopting the system, a risk propagation path under a complex scene can be automatically identified, and the safety protection level of equipment is improved.

The invention discloses a fault diagnosis method and system based on a multi-scale adaptive hypergraph convolutional neural network, and belongs to the technical field of fault diagnosis. The method comprises the following steps: acquiring original vibration data of mechanical equipment, and carrying out data preprocessing; constructing a fault diagnosis model based on the multi-scale adaptive hypergraph convolutional neural network; and inputting the vibration data after data preprocessing into the constructed fault diagnosis model, and outputting a fault diagnosis result. According to the method, a vibration signal is decomposed into different frequency bands, features under various scales are extracted respectively, prior knowledge is fused, local topological graphs are constructed through various weighting measures, and a plurality of local topological graphs jointly form a spatial hypergraph to serve as input of a model; a dual-channel graph convolutional network is introduced to extract features, weights of different channels are adaptively distributed through an attention mechanism, and deep fusion of the features is realized. Experiments on two reference data sets show that the method can effectively improve the fault diagnosis accuracy, and has good stability and generalization.

The invention discloses an intelligent flow arrangement method based on fusion expert network and deep reinforcement learning, which comprises the following steps: collecting network node and link state data in real time, and constructing a time sequence input vector and a topological graph structure; a time sequence neural network and a graph neural network are used for extracting traffic spatial-temporal features and node topological features respectively, future traffic is predicted through a classification network after fusion, and coarse-grained arrangement of network slices of different service levels is completed; modeling resource scheduling into a multi-agent Markov decision process, and designing a state space, an action space and a reward function; a deep reinforcement learning agent is initialized, and training is carried out through interaction experience; fusing a pre-trained expert strategy network, and constructing a total loss function to optimize network parameters; and finally generating an intelligent strategy capable of dynamically optimizing the flow path and resource allocation according to the real-time state. According to the invention, efficient resource scheduling under multi-service differentiation service quality requirements can be realized.

The invention relates to the technical field of power distribution operation and maintenance management, and discloses an intelligent power distribution operation and maintenance management system based on 5G transmission. The system comprises a 5G real-time acquisition module, a multi-dimensional feature fusion module, a dynamic topology generation module, an anomaly propagation analysis module and a strategy optimization feedback module. The 5G real-time acquisition module acquires operation state data streams such as current and voltage waveforms, an equipment temperature sequence and environment monitoring indexes of the power distribution equipment through a 5G network; the multi-dimensional feature fusion module is used for separating equipment state features, calculating mutual information amount and generating equipment state feature tensors; the dynamic topology generation module constructs an association intensity matrix according to the feature tensor, and generates a hierarchical connection path and a dynamic equipment topological graph; the abnormal propagation analysis module extracts a state fluctuation sequence, identifies an abnormal transmission path and marks a core propagation node; and a strategy optimization feedback module generates a maintenance strategy priority queue according to the dynamic topology map, and feeds back an execution result to update the dynamic topology map, so that the intelligence and accuracy of power distribution operation and maintenance management are improved.

The invention relates to the technical field of intelligent inspection, and discloses a regional abnormal condition real-time early warning method based on high-point panoramic intelligent inspection, which comprises the following steps: collecting visible light and infrared thermal imaging video streams of a target region to form a panoramic video sequence, and carrying out intelligent analysis, feature extraction and analysis, construction of a spatio-temporal topological graph and detection of an abnormal behavior mode. Predicting environmental risks, constructing a spatio-temporal evolution model, and further generating graded early warning information of regional abnormal conditions; the method effectively solves the problems of large panoramic inspection data volume, exception complexity, easy environmental influence on target detection and the like in a large-scale scene, significantly improves the accuracy and real-time performance of exception early warning, and guarantees the regional safety.

The invention belongs to the technical field of computers, and particularly relates to an Internet of Things security data real-time monitoring and protection method and system, and the method comprises the steps: collecting equipment communication and state data through an edge agent, and analyzing and extracting standardized metadata; constructing an equipment behavior contour vector based on a sliding window, and dynamically maintaining a global equipment topological graph; triggering a primary alarm in combination with behavior deviation detection and topology abnormity; outputting a threat score and an attack intention through rule matching and Bayesian network double-engine collaborative reasoning; and executing automatic response according to grading, and feeding back and correcting a behavior baseline to realize closed-loop optimization. The system comprises a data acquisition module, a protocol analysis module, a behavior modeling module, a topology maintenance module, an anomaly detection module, a collaborative reasoning module, an automatic response module and a baseline correction module. Through full-link real-time modeling and cross-device collaborative analysis, the attack detection rate is significantly increased to 98% or above, the false alarm rate is lower than 2%, the response delay is controlled within 800 milliseconds, and the security and adaptive ability of the Internet of Things system are enhanced.

The invention provides a wharf facility health monitoring method and system based on digital twinning and machine learning, and relates to the field of wharf facility health monitoring, and the method comprises the steps: building a weight coefficient of a monitoring point network topological graph according to a synchronism change value of the same type of sensor data corresponding to adjacent monitoring points; determining an abnormal transmission path based on the weight coefficient; determining an abnormal state propagation rule of each abnormal transmission path in each preset abnormal state according to the historical monitoring data corresponding to each preset abnormal state; after abnormal sensor data of any monitoring point is detected, parameter predicted values of other monitoring points in the corresponding abnormal transmission path are calculated based on an abnormal state propagation rule; and if the parameter prediction values of the preset proportion number deviate from the corresponding actual monitoring values, generating early warning information. By implementing the method, false alarms caused by single-point data fluctuation summarizing in a traditional health monitoring mode can be dealt with, and the accuracy and reliability of abnormal state early warning are improved.

The invention discloses a multi-GPU and multi-SSD-based graph neural network training acceleration method and system, and belongs to the field of graph neural network training. According to the method, physical architecture information of a server is read, a communication link relation among a CPU, a GPU and an SSD is modeled into a communication topological graph, the link process that the GPU pulls data is converted into the maximum flow problem of a single source and a single sink, an optimal access flow distribution scheme is solved through Ford-Fulkerson augmented path calculation, and the access flow distribution efficiency is improved. Then, considering the access frequency of the graph vertexes, and distributing the graph vertexes to the storage node with the optimal current performance index for storage; and finally, after storage of all graph vertexes in the graph data set is completed, the server executes batch-by-batch training tasks of the graph neural network. According to the method, the training throughput of the system can be greatly improved, training of TB-level super-large-scale graphs is met, and efficient and low-delay multi-GPU and multi-SSD data access is achieved.

The invention discloses a carbon neutralization target-oriented electricity-carbon-green certificate multi-target scheduling optimization method and system, and relates to the technical field of carbon neutralization control, and the method comprises the following steps: obtaining first data of a target energy storage unit, constructing a constraint generation network, and dynamically updating an energy storage operation constraint domain based on a dynamic time warping algorithm; based on the updated operation constraint domain, establishing an electricity-carbon coupling model, extracting a nonlinear mapping relation between an electric energy output behavior and carbon emission intensity, and outputting a dynamic incidence matrix through a multi-dimensional time sequence predictor; constructing a carbon flow distribution topological graph according to the dynamic incidence matrix, and obtaining an optimal carbon flow path through a Dijkstra algorithm; based on the operation constraint domain and the optimal carbon flow path, establishing a multi-subject collaborative decision-making mechanism, solving a multi-target unit equilibrium solution by adopting a distributed alternating direction multiplier method, and generating a global scheduling instruction set; according to the method, a carbon flow topology and dynamic constraint domain cooperation mechanism is constructed, so that efficient solving of a multi-main-body carbon response optimization scheduling problem is realized.

The invention provides an intelligent memory leak prediction and tracking method and system for a micro-service architecture, and relates to the technical field of memory leak prediction, and the method comprises the steps: collecting the runtime data of a service node, constructing a service calling topological graph, extracting the time sequence and topological features, and carrying out the feature fusion. And constructing a deep learning prediction model by combining a bidirectional long-short term memory network with a multi-head attention mechanism and a graph attention network, and predicting a memory use trend and a risk score of a service node. Potential memory leak nodes are identified according to the risk score and the abnormal degree predicted value, a key propagation path is identified through a service calling topological graph and a minimum cut algorithm, and finally a root service of memory leak is positioned by using a belief propagation algorithm, so that accurate prediction and rapid positioning of the memory leak in the micro-service system are realized. And the system stability and reliability are improved.

The invention belongs to the technical field of fault analysis and monitoring, and particularly relates to an intelligent monitoring and fault isolation method and system for a branch box, and the method comprises the steps: carrying out the multi-physical field data fusion collection of the branch box, and obtaining a spatial-temporal feature matrix; performing dynamic feature extraction on the multi-physical field data of the branch box, and outputting a fused dynamic feature vector of the branch box; obtaining a branch box topological graph structure, and obtaining a branch box node fault probability matrix; building a physically constrained fault propagation model based on the branch box node fault probability matrix, obtaining a pre-isolation set, and outputting a dynamic blocking instruction; taking the pre-isolation set as an initial solution space constraint, outputting a fault type and obtaining an isolation instruction; and setting a branch box topology reconstruction constraint, and performing closed-loop control on each branch box node in combination with the isolation instruction to obtain a switching action sequence of each branch box node and a reconstructed power supply path. Precise monitoring, rapid isolation and intelligent reconstruction of the fault of the branch box can be realized, and the operation and maintenance efficiency of the power distribution network is remarkably improved.

The invention discloses a power distribution cabinet maintenance system based on artificial intelligence, and belongs to the technical field of intelligent power distribution cabinet diagnosis. Normalization, time sequence feature extraction and denoising are carried out on the collected data, an electrical topological graph is automatically constructed, node states are vectorized, text semantics are coded by utilizing a BERT class model, and a maintenance knowledge graph is generated; through fusion of multi-source data, a multi-branch neural network is constructed, state perception and risk determination are realized, and a fault trend and structure degradation are identified. Calculating a potential fault risk coefficient, and triggering a structure health monitoring mechanism; analyzing the structural health based on a topological graph and a graph neural network, and starting semantic strategy retrieval when the structure is abnormal; comparing the semantic conformity between the state and the historical strategy, and assisting in generating a precise maintenance strategy; according to the system, dynamic monitoring, intelligent early warning and strategy recommendation of the operation state of the power distribution cabinet are realized, and the operation and maintenance efficiency and the equipment reliability are improved.

The invention relates to the technical field of industrial automation control, and discloses a change gear assembly processing technological parameter intelligent optimization system, which comprises a multi-dimensional data synchronous acquisition unit, an angular domain static map construction unit, a phase lead constraint control unit and an angle trigger execution unit, according to the method, a static angle-load topological graph with a main shaft angle as an index is established, a phase lead angle is calculated by combining inherent physical response lag time of a servo system, and a parameter adjustment instruction sequence is directly injected into a servo current loop at a preset angle window before physical load impact occurs; according to the feed-forward control mechanism based on angular domain reconstruction and phase lead constraint, the problem of physical lag of traditional time domain feedback control in response to high-frequency intermittent cutting is avoided, and zero time difference of periodic load impact and even pre-judgment torque compensation are achieved.

The invention relates to the technical field of production resource management, and discloses a production resource management system based on an AI neural network. The system comprises a multi-source data acquisition module for acquiring multi-source heterogeneous data of a production environment; the multi-dimensional feature extraction module is used for extracting feature vectors of each data source; the heterogeneous data fusion module is used for generating a resource state semantic representation based on a cross-modal attention mechanism; the resource topological graph construction module is used for constructing a double-layer topological library; and the intelligent optimization decision module is used for generating a real-time resource scheduling strategy and a task allocation optimization scheme based on the result. In addition, the system also has the functions of anomaly detection, path re-planning, resource aging prediction and the like. The system can comprehensively process production data, optimize resource scheduling and task allocation, improve the resource utilization rate, shorten the task completion time, enhance the stability of the production process and the scientificity of equipment maintenance, and improve the production management level of enterprises.