192 results about "Constraint graph" patented technology

The invention discloses an electric power capital construction process early warning method and system based on a high-precision three-dimensional model, and relates to the technical field of electric power capital construction, and the method comprises the steps: collecting multi-source data of an electric power capital construction site, and constructing a high-precision three-dimensional model comprising equipment space coordinates, construction time sequence labels and environmental parameters; inputting the space-time diagram into a space-time diagram convolutional network, outputting a risk propagation probability matrix of a three-dimensional space and a construction time sequence dimension, and identifying high-risk nodes and associated construction stages; and dynamically marking root cause coordinates and a risk diffusion range in the high-precision three-dimensional model, generating an early warning instruction containing a visual path, a root cause report and a disposal priority, and pushing the early warning instruction to a terminal in real time through an edge node. Based on the hierarchical space-time attention-physical constraint graph convolutional network, the inter-node cross-stage association weight is dynamically allocated, the convolution kernel parameters are optimized in combination with the physical connection strength, and the space-time association of high-risk node identification is significantly improved.

The invention provides an intelligent logistics distribution path optimization method under multiple constraint conditions, and relates to the technical field of logistics distribution, and the method comprises the steps: constructing a constrained graph model according to a time window, vehicle information and real-time traffic API data; performing initial path planning by adopting an improved genetic algorithm; performing dynamic path adjustment according to the initial path set and the real-time traffic API data; and in combination with the optimized path set and customer preference information in the order information, screening an optimal solution through a plant rhizome growth optimization algorithm, and then outputting a final path optimization strategy according to a preset rule engine. According to the method, a plant rhizome optimization algorithm is adopted, a biological growth rule is simulated to realize multi-target co-evolution, and customer preference and task migration flexibility are considered while premature convergence is avoided; through flexible adjustment of constraint conditions and algorithm parameters, the method can adapt to more scenes, and can still maintain stable solving capability under the condition of continuous congestion or sharp increase of orders, thereby remarkably improving the anti-risk capability of a logistics system.

The invention relates to the technical field of artificial intelligence, can be applied to the digital medical field and the financial field, and discloses a causal graph model-based data processing method, device and equipment and a storage medium, the method comprises the steps of obtaining target data and a corresponding to-be-explained reasoning result, and constructing a causal graph according to the target data, obtaining a corresponding structured causal graph; performing causal constraint on the structured causal graph by adopting a causal constraint graph network learning mechanism to obtain an optimized causal graph; and performing anti-fact reasoning on the reasoning result based on the optimized causal diagram, and generating an interpretation result of the target data according to an anti-fact reasoning result. By fusing the causal graph model and the graph neural network technology and introducing the anti-fact reasoning module, accurate causal modeling and explanation of the high-dimensional nonlinear data are realized, the limitation of a traditional causal graph model in the aspect of processing the high-dimensional nonlinear data is solved, and the defect of a graph neural network output result in causal explanation is made up.

The invention relates to the technical field of mutual inductor tests, and provides a mutual inductor test abnormal data automatic filtering analysis method and system, and the method comprises the steps: obtaining magnetic field data, electric field data and thermal field data, respectively carrying out the feature extraction and weighted fusion, and obtaining a multi-physical field fusion feature vector; constructing an electromagnetic induction chain type propagation graph, calculating the weight of an edge in the graph, constructing a magnetic flux conservation constraint graph convolutional neural network, learning propagation and evolution modes of transformer test abnormity, and obtaining a coupling abnormity feature vector; the coupling anomaly feature vectors are classified, normal data, single physical field abnormal data and multi-physical field coupling abnormal data are recognized, and corresponding data are judged as abnormal data and filtered; and carrying out physical mechanism analysis on the filtered abnormal data to obtain an abnormal detection report. According to the invention, high-precision automatic identification, filtering analysis and physical mechanism traceability diagnosis of transformer test abnormal data are realized.

The invention relates to the technical field of intelligent decision making, in particular to a multi-source reasoning-based confrontation environment target behavior analysis method and system. Comprising the following steps: detecting a blue target under a simulation system, obtaining target position and state information, and generating a knowledge graph; dynamically extracting an entity sub-graph associated with the blue target through a space-time constraint graph sampler; constructing a dual-channel decision generator to respectively output an action probability vector driven by an expert rule and an action probability vector derived by a Bayesian network; designing a high-dimensional decision space projection module to map the two types of probability vectors into n-dimensional space coordinate points, and performing probability trajectory fusion and error correction through an improved Kalman filter; and a game utility corrector is introduced to optimize the final action decision based on the Nash equilibrium principle. The problem that in the prior art, a static knowledge graph is difficult to support real-time fusion of multi-source decision information in a dynamic confrontation environment is solved, and the timeliness and accuracy of complex situation behavior reasoning are remarkably improved.

The invention discloses a logistics transportation personalized recommendation path planning method and system based on a cloud platform, and belongs to the technical field of logistics transportation, and the method comprises the steps: constructing a heterogeneous traffic map model fusing road sections, geographic interest points and traffic event information; collecting historical transportation task records of a plurality of users, generating user semantic intention tags based on delivery behaviors, and mapping the user semantic intention tags to traffic map nodes and edge attributes to form a semantic constraint graph structure; performing joint modeling on the user intention and the path reachability by using a heterogeneous graph neural network to generate a path scoring sequence; after the transportation task is completed, abnormal events are collected, and the semantic intention label mapping relation is dynamically updated based on reverse alignment errors; the model and the mapping module are deployed on a cloud platform, the traffic state is obtained in real time in combination with edge equipment, and path recommendation online generation and rapid pushing are achieved; according to the method, the personalized matching degree and the real-time response capability of path recommendation can be effectively improved, and the method is suitable for intelligent logistics path planning in a complex scene.

The invention provides a mountain power station current collection system state online monitoring method and device, and relates to the field of data processing. According to the method, distributed optical fiber data, environmental meteorological data and electrical measurement data are acquired to construct a multi-mode time sequence matrix, an operation state diagram is formed through graph structure representation and graph signal processing, a working condition mode cluster is obtained by combining dynamic clustering, a constraint diagram optimization framework is input, thermal, electrical and mechanical constraints are introduced, and an operation state track is generated. And constructing and fusing a non-stationary degradation path library and environment exposure history to form a dynamic degradation evolution model, and finally outputting in-service reliability and predicting residual life by a risk inference engine to realize online monitoring of the state of the current collection system of the mountain power station. By implementing the technical scheme provided by the invention, the accuracy of online monitoring of the state of the current collection system of the mountain power station is improved.

The invention discloses a knowledge graph-based software material consistency automatic checking method, which comprises the following steps of: collecting and processing a software declaration material to form a standardized original material set; forming an initial entity set and a candidate relation set based on the normalized original material set; constructing a heterogeneous knowledge graph based on the initial entity set and the candidate relationship set; executing cross-modal semantic anchoring on the heterogeneous knowledge graph to generate a semantic anchoring mapping table and an anchoring confidence value set; generating an enhanced constraint graph based on the semantic anchoring mapping table and the anchoring confidence value set; outputting a consistency detection result set and a minimum conflict subgraph set based on the heterogeneous knowledge graph and the enhanced constraint graph; and based on the consistency detection result set and the minimum conflict subgraph set, generating a difference check report and a repair suggestion set. According to the method, heterogeneous knowledge graph construction and semantic anchoring are adopted, and automatic consistency checking of the soft material is realized.

The invention relates to the technical field of water conservancy projects, in particular to a construction scheduling method for collaborative linkage of cross-basin water conservancy projects, which comprises the following steps of: constructing a space-time water conservancy topological network to realize multi-source data fusion and spatial association and provide a basis for accurate scheduling; potential contradictions between construction and hydrological regulation and control are found in advance by recognizing conflict areas and performing quantitative analysis; a space-time safety window strategy set is formulated, the construction period is optimized in combination with risk prediction, and operation safety and efficiency are guaranteed; through task decomposition and collaborative factor allocation, an atomic operation unit and a dependency relationship are defined, and the cross-project linkage capability is improved; by generating a preliminary scheduling scheme, and through a resource-collaborative double-constraint graph and a genetic algorithm, efficient resource configuration and conflict minimization are realized; through dynamic risk monitoring and scheduling scheme reconstruction, hydrological abrupt change and construction deviation are quickly responded, cooperative factors are triggered for rebinding, and scheme adaptability and robustness are ensured.

The invention provides a dynamic space-time air quality prediction method based on a physical constraint graph attention network, and the method comprises the steps: carrying out the collection and preprocessing of multi-source data, and obtaining the pollutant concentration and meteorological element historical sequence of a monitoring station; constructing a dynamic space-time diagram network fusing distance attenuation and a wind direction driving transmission path based on the longitude and latitude of the station and the real-time meteorological field; multi-cycle time sequence features are extracted in a self-adaptive mode through an enhanced time sequence network ETNet, and attention weight distribution is guided through physical priori; time sequence features are embedded into the dynamic graph, a physically constrained graph diffusion attention network PC-GDAN is input, and space-time diffusion modeling is achieved through a differential operator embedded into an atmospheric diffusion equation; time sequence and space features are fused, mass conservation and diffusion smoothness constraints are introduced in decoding prediction, and a concentration prediction result conforming to a physical rule is generated. The method effectively fuses physical mechanisms and data, has strong space-time modeling capability and physical interpretability, and significantly improves the precision and robustness of air quality prediction.

The invention provides a solid electrolyte material screening method based on a physical constraint diagram attention network, and the method comprises the steps: calculating an ionic conductivity parameter corresponding to a solid electrolyte material through dynamic Monte Carlo simulation, and constructing an initial data set containing a mapping relation between a crystal structure and ionic conductivity; constructing a physical constraint graph attention network prediction model; performing iterative training and hyper-parameter adjustment on the physical constraint graph attention network prediction model by adopting the initial data set based on cross validation and Bayesian optimization to obtain an optimal physical constraint graph attention network prediction model; a novel crystal structure is generated through a generative large language model, and a candidate material data set is constructed; and taking the candidate material data set as input, utilizing the optimal physical constraint diagram attention network prediction model to predict the ionic conductivity of the material, and screening to obtain the solid electrolyte material with high ionic conductivity. The method breaks through the inherent mode that a traditional prediction method is limited to known materials, the material exploration space is greatly expanded, and development of solid electrolyte key materials is accelerated.

The invention belongs to the field of electric power inspection, and provides an electric power scene full-chain ubiquitous dynamic sensing body intelligent inspection operation method and system, and the method comprises the steps: carrying out the semantic analysis of an initial electric power task, combining a target electric power scene model, constructing an executable constraint set, and matching each body intelligent agent in an inspection domain; fusing the task information and a pre-constructed electric power knowledge graph, performing graph path search on the fused electric power knowledge graph by using a pre-trained large language model to obtain a disassembled atomic action sequence, and issuing the disassembled atomic action sequence to a matched and determined corresponding body agent; and evaluating the risk of candidate actions in the atomic action sequence by using the multi-modal task-environment constraint graph to realize preliminary optimization, and dynamically adjusting according to real-time environment information in the target power scene, the state of the agent with the body and the current task completion condition until the atomic action sequence is executed. According to the invention, the intelligence, the accuracy and the safety of the inspection operation can be obviously improved.

The invention provides a complex manufacturing system production scheduling engine based on large language model spatio-temporal reasoning, and relates to the field of intelligent manufacturing, a scheduling demand is obtained, time-related information and space-related information are extracted, then spatial features and a time constraint graph are jointly coded into LLM analyzable spatio-temporal semantic vectors, and the LLM analyzable spatio-temporal semantic vectors are used for scheduling. And explicitly modeling a multi-dimensional interaction relationship of spatial proximity-time overlapping degree-resource competition intensity through association analysis of scheduling space-time semantics, so that LLM can realize more reliable space-time causal reasoning, and finally a scheduling scheme conforming to complex space-time constraints is generated. Thus, high-dimensional spatial-temporal characteristics in a manufacturing system can be adapted, spatial-temporal information required by user scheduling can be highlighted to adapt to an input format of LLM, then a nonlinear spatial-temporal coupling relationship in the production scheduling of a complex manufacturing system is concerned, and the generalization ability is improved.

The invention discloses an industrial equipment remote operation and maintenance management method based on digital twinning, and aims to solve the problems of deduplication, association and suppression of events and alarms and root cause fusion positioning under complex working conditions. According to the method, a multi-relation layered signed causal graph is constructed, a dynamic causal mask is generated according to a twinning state, causal structure learning is carried out under constraint, and graph anti-fact intervention and simulation of physical budget constraint and historical dual-channel consistency verification are combined; and the root cause priority and the evidence chain are generated by adopting topology perception contribution degree sorting, and the suppression strategy parameters are formed, so that the technical effects of reducing false alarms and repeated alarms, improving the root cause identification accuracy and processing efficiency and stably outputting the linkage work order are realized.

The invention discloses a multi-terminal detection score offset correction method and system, particularly relates to the field of financial risk control, is used for solving the problem of steady-state offset generated in the merging process of multi-terminal detection scores, and comprises the following steps: receiving cross-terminal original behaviors and collecting metadata under unified time reference and unified field specifications; the method comprises the following steps: extracting a terminal fingerprint table according to a sampling beat, a sensing path, a rendering mode and a quantification strategy, generating a time alignment table, dividing a stable fragment, a change fragment and a transition fragment by taking the terminal fingerprint table and the time alignment table as conditions, and constructing a segmented order-preserving mapping relation table; a cross-terminal consistency constraint graph is generated based on the segmented order-preserving mapping relation table to form a correction rule set, rules are verified in an offline playback window, a micro-correction list is output, and the correction rule set and the micro-correction list are read in the online merging stage to execute correction and output a unified scale score. And meanwhile, generating a feedback abstract, submitting, filing and publishing a version.

The invention relates to a physical constraint fused graph structure UNet neural network for long-time time domain difference optical simulation method and system, and the method comprises the steps: 1, randomly constructing a device parameter data set through an FDTD method, obtaining FDTD uniform grid data, and carrying out the data preprocessing; 2, constructing a graph structure Unet model; 3, inputting the device parameter data set into the graph structure Unet model for end-to-end training; 4, predicting electromagnetic field scattering after a long-time light source encountering period is carried out on given device parameters and initial electromagnetic field distribution; and 5, comparing the electromagnetic field distribution of different device shapes, which is predicted for a long time, with the electromagnetic field value calculated by FDTD, and further judging the prediction accuracy of the graph structure Unet network model. According to the method, one-time long-time sequence prediction of electromagnetic scattering is completed by using the graph structure Unet model.

The invention discloses a high-precision robust robot detection method and system based on constraint pose graph optimization, and the method comprises the steps: collecting point cloud data of a detection object in multiple poses, and introducing a standard sphere and a standard ball rod in the collection process for constructing a graph constraint; a robust constraint pose map optimization algorithm framework SYMM-RCPGO is constructed; substituting the point cloud data into SYMM-RCPGO to construct an attitude graph and a constraint attitude graph to obtain a target function; iteratively solving the SYMM-RCPGO in the SE3N by combining the objective function according to the graph constraint, and using a dynamic constraint boundary algorithm to relax the tightness of the constraint to obtain the three-dimensional point cloud data of the detection object with a complete appearance; and registering the measured three-dimensional point cloud of the detection object with the designed CAD model, and accurately calculating the pose matrix of the detection object to complete the positioning of the detection object. According to the method, the SYMM distance function with a higher D-optimal measurement upper limit, the robust Welsch function and the constraint graph are utilized, so that the method is superior to a traditional PGO in the aspects of accuracy and robustness.

The embodiment of the invention provides a shipyard steel framed bent scheme automatic optimization method and system. The method is applied to the technical field of buildings and comprises the following steps: selecting an initial size of a shipyard steel bent column beam section and bent design information based on engineering experience library data, and constructing high-precision input characteristics based on factory design information; based on the high-precision input features, constructing a physical constraint graph neural network, and predicting an initial design scheme meeting the physical constraint; and carrying out multi-objective reinforcement learning optimization on the initial design scheme meeting the physical constraint, and carrying out dynamic adjustment in a virtual simulation environment to obtain optimized steel bent design parameters. In this way, according to the method, the shipyard data are monitored in real time, the graph neural network is constructed for feature extraction and parameter optimization, the optimal steel frame parameters are obtained in dynamic adjustment, and the practical value is high.

The invention relates to the technical field of geological disaster early warning, and discloses a geological deformation early warning method and system based on AI vision, and the method comprises the steps: obtaining surface micro-deformation features through an AI vision system, and constructing a geological map structure network; fusing a nonlinear geomechanical equation in a graph neural network to realize physical constraint driven feature extraction; creating a multi-dimensional graph structure model representing different depth layers, and realizing cross-layer information transmission through a physical information guide type graph attention mechanism; deploying a graph neural network of a Bayesian probability framework to carry out geological stress field distribution modeling, and quantizing prediction uncertainty; and finally, generating intelligent early warning decision information with risk grade division. According to the method, the AI vision and the physical constraint graph neural network are combined, accurate inference of the underground stress field is realized, the uncertainty can be quantitatively predicted, and the reliability and timeliness of geological disaster early warning are improved.

The invention relates to the technical field of city cadastral data analysis, and discloses a city update district boundary division method and system based on cadastral data, and the method comprises the steps: collecting the division associated cadastral data of a target city update district, and employing an improved DTW algorithm to obtain a city update district boundary; the method comprises the following steps: screening current situation boundary effective points and newly-added candidate points with history continuity from current situation measured data, extracting spatial features and ownership features for fusion, constructing a feature matrix by using a fusion feature vector, constructing a spatial ownership double-constraint graph neural network, predicting a final boundary point set of a target city update area, and obtaining a target city update area. And generating a final land parcel boundary. Therefore, by introducing the improved DTW algorithm and the spatial ownership double-constraint graph neural network, while accurate association of historical and current boundary data is established, joint optimization of spatial rationality and ownership rationality is realized by utilizing double-constraint feature fusion, and the division precision and division efficiency of the urban update district boundary boundary are improved.

The invention discloses a scheduling optimization method and system for production project management, and relates to the technical field of production scheduling. A scheduling optimization system for production project management comprises a production project management module and a production project scheduling module. According to the invention, through the construction mode of the main project triad and the sub project triad, the complex production project information is standardized and semantized, and the analytic property of the scheduling model to the business logic is effectively enhanced; through scheduling perception coding and clustering processing of sub-project feature vectors, similar production unit tasks which can be merged can be automatically identified, the resource utilization rate is improved, and the redundancy scheduling cost is reduced; historical progress fusion and idle time window information are introduced, a scheduling constraint graph is constructed in combination with a graph attention network model, various resource, personnel and equipment state restrictions can be comprehensively considered, and a globally optimal solution of a task scheduling path is realized.

The invention relates to the technical field of electronic design automation, and discloses a standard cell layout legalization method and system based on a graph theory constraint graph, and the method comprises the steps: building a bidirectional directed constraint graph based on an existing layout and a design rule, and building a directed edge between adjacent graph edges according to the design rule; traversing the constraint graph, detecting whether a directed edge which does not meet a constraint condition exists, and if so, identifying the directed edge as a constraint violation edge; for two end points of each constraint violation edge, searching a key path based on a depth-first search algorithm, and determining a maximum movable distance in combination with a greedy strategy; moving the endpoint to a legal position according to the critical path and the maximum movable distance, and updating the position of a related node in the constraint graph; and mapping the position changes of the nodes in the constraint graph back to the corresponding graph edges in the layout, and outputting the legalized layout, so that efficient and automatic repair of design rule violation is realized through the method, and the layout compliance rate and the area optimization effect are remarkably improved.

The invention relates to a large-scale multi-robot coordination method under a complex and uncertain time sequence task, belongs to the technical field of robot task allocation, and solves the problems that the efficiency is low and the real-time requirement cannot be met due to the fact that an existing multi-robot coordination method needs frequent re-planning in a dynamic and uncertain environment. The method comprises the following steps: constructing a task constraint graph based on an inter-task constraint relationship of each to-be-completed task, and obtaining a current to-be-completed task set based on a preset single processing task number; based on the information of each task, the task constraint graph and the information of each robot, obtaining a local optimal task allocation result under the quantity of each task subgroup by dynamically adjusting the quantity of the task subgroups, and taking the optimal task allocation result as a global optimal task allocation result; based on a global optimal task allocation result, allocating each robot to each task subgroup, and planning a path of each robot based on an overall task type; and when a re-planning triggering condition is met, re-planning the uncompleted task.

The invention relates to the technical field of power task execution, and provides a vision-language-action fused power task security execution method and system, and the method comprises the steps: obtaining a natural language instruction, a visual image and an intelligent agent state of a current task, constructing a current state, initializing a candidate action set of an intelligent agent in the current state, and executing the current state; in combination with the multi-modal task-environment constraint graph, evaluating risk scores of candidate path points contained in the candidate actions, and carrying out preliminary screening on the candidate actions; for each candidate action subjected to preliminary screening, through a risk assessment model, obtaining an action risk probability, and screening out candidate actions of which the action risk probabilities are greater than a threshold value; and encoding the natural language instruction, the visual image and the intelligent agent state, obtaining observation information characteristics through a cross attention mechanism, selecting an optimal action from the reserved candidate actions based on the observation information characteristics, controlling the intelligent agent to execute an electric power task, and continuously updating the state and the action. And dangerous operation is avoided from the source.

The invention discloses a stable diffusion method for few-sample meter defect image generation, which is characterized in that a pre-training model is finely adjusted, structural features and defect knowledge of a transformer substation meter are embedded, and the similarity between a generated image and an actual meter is improved. A crack feature modeling module is innovatively designed, a line draft graph, a crack mask and a constraint graph are combined, a control image with geometric constraints is generated, and the shape and the position of the crack are accurately expressed. Meanwhile, a super network mechanism is introduced, weight distribution in the generation process is dynamically adjusted, and consistency and diversity of the generated image in form and position are ensured. According to the method, the transformer substation meter defect image with specific crack characteristics can be generated under the condition of a small number of samples, and the diversity and quality of the generated image are remarkably improved. Through application in a downstream detection task, the generated data effectively improves the precision and robustness of a defect detection model, and powerful support is provided for safe and stable operation of a power system.

The invention discloses a motor temperature prediction transfer learning method based on a physical constraint graph neural network, and the method comprises the following steps: S1, constructing an undirected graph with a label based on a physical structure of a source domain motor; s2, on the basis of the undirected graph of the label, constructing a physical constraint graph neural network; s3, training the physical constraint graph neural network by using operation data and temperature data of a source domain motor to obtain a pre-training model; s4, on the basis of the physical structure of the target motor, constructing an undirected graph of the target motor, and multiplexing the heat capacity parameter and the heat conductivity function in the pre-training model so as to obtain an initialized physical constraint graph neural network for the target motor; and S5, obtaining a temperature prediction model of the target motor. According to the method, the physical heat transfer rule is embedded into the transferable graph neural network structure, so that a high-precision temperature prediction model with physical interpretability can be quickly constructed for a new motor only by using small-batch data.

The invention relates to the field of intelligent equipment, and discloses a cargo handling method and system based on spatial relationship reasoning, and the method comprises the steps: obtaining a color image and a depth image containing cargos, screening effective cargos, and obtaining the spatial relationship information of the effective cargos, the spatial relationship information comprises depth information, height information, depth difference, vertical overlapping degree and horizontal overlapping degree; according to the depth difference, the vertical overlapping degree and the horizontal overlapping degree of the effective cargos, an unconstrained cargo set is identified and obtained; according to the unconstrained cargo set and based on the depth information and the height information of the unconstrained cargos, priority ranking is carried out, and an optimal carrying sequence is generated; and according to the priority sequence of the optimal carrying sequence, mechanical equipment is driven to execute actual grabbing operation, and carrying operation of the goods is completed. According to the method, the constraint graph model based on the spatial dependency relationship is innovatively constructed, and the unconstrained target which can be safely grabbed is intelligently identified.

The invention relates to the technical field of the intelligent power grid industry, in particular to a power distribution and utilization ring network fault intelligent troubleshooting method and system. According to the method, a looped network situation primitive is generated through multi-source heterogeneous data fusion. The primitives are input into synchronous compression transformation to extract transient fault initial features, and the transient fault initial features are mapped into fault propagation path features through a graph convolutional network considering topological structure constraints; then, inputting the features into a space-time correlation prediction model, integrating defect type judgment and trend pre-judgment features, and generating a fault diagnosis evidence set; and starting an investigation mechanism based on the evidence set to determine an interval to be investigated, and performing closed-loop verification by constructing a fusion judgment matrix based on multi-modal physical sensing information to realize physical space locking of a fault defect point. According to the method, deep learning diagnosis and multi-mode positioning are integrated, and the detection precision and the positioning efficiency of looped network defects and the identification capability of transient hidden faults are effectively improved.

The invention discloses a dynamic constraint driven chemical three-dimensional pipeline construction drawing generation system and method, and the system comprises a multi-modal data fusion module which obtains multi-modal fusion data; the dynamic constraint map module is used for carrying out structured representation on the three-dimensional pipeline system by utilizing the multi-modal fusion data to obtain a pipeline dynamic constraint map; the three-dimensional space reasoning module is deployed with a graph neural network, carries out deep fusion of a space attention mechanism based on a pipeline dynamic constraint graph, and generates a three-dimensional pipeline construction drawing; and the intelligent drawing output and verification terminal module is used for automatically marking the three-dimensional pipeline construction drawing and synchronously verifying the marking information and the mechanical property. According to the method, the dynamic constraint graph is constructed by fusing the dynamic process parameters and the construction constraints, and spatial reasoning is carried out by utilizing an intelligent algorithm, so that the spanning of chemical pipeline design from static modeling to dynamic response is realized, and the problems of low efficiency, high conflict rate, dynamic constraint deficiency, cooperative lag and the like in a traditional method are effectively solved.

The invention discloses a wind turbine blade internal damage diagnosis method based on a sound field graph neural network, and belongs to the technical field of wind turbine blade state detection, and the method comprises the steps: deploying a microphone array in a cabin, and collecting an acoustic signal when a blade rotates; constructing a space sound field graph structure by taking the microphone as a node and the sound wave propagation path as an edge; extracting nonlinear acoustic features by using a physical constraint graph neural network PC-GNN; generating a damage embedding vector based on self-supervised training contrast learning; and outputting a damage probability thermodynamic diagram and positioning information. According to the method, a directional microphone array is deployed in a cabin, and a sound wave propagation space diagram structure is constructed; designing a physical constraint graph neural network PC-GNN, and embedding an acoustic wave equation as a regularization item; the problem of scarcity of damaged samples is solved by adopting self-supervised contrast learning; and finally outputting a positioning thermodynamic diagram of the internal damage of the blade.