140 results about "Model graph" patented technology

The invention discloses an oil product pipeline leakage monitoring method, device and equipment and a medium, and relates to the technical field of pipeline monitoring, the method comprises the steps that a graph model of a pipeline monitoring network is constructed, and each node pipeline in the graph model is provided with a measuring point; collecting pipeline data of each measuring point in real time, wherein the pipeline data comprises temperature, pressure and flow data; performing feature extraction on the pipeline data to obtain various feature data; determining a pipeline working condition for the various characteristic data corresponding to each measuring point, and adjusting a fusion weight for fusing the various characteristic data in real time by adopting a reinforcement learning algorithm based on the pipeline working condition; according to each fusion weight, performing feature fusion on the multiple feature data to obtain a multi-source fusion feature; inputting the multi-source fusion feature of each measuring point into a sensor fault recognition model, and outputting a sensor fault mark; and inputting the multi-source fusion feature of each node carrying the sensor fault mark into a leakage identification and positioning model, and outputting the leakage probability of each node. The prediction accuracy can be improved.

The invention discloses an unmanned aerial vehicle performance pattern generation method and device, computer equipment and a storage medium, and the method comprises the steps: collecting historical unmanned aerial vehicle performance patterns, and carrying out the text marking of the historical unmanned aerial vehicle performance patterns, so as to construct a training data set; training a text graph model and a corresponding control model by using the training data set so as to construct a pattern generation model; and generating a corresponding unmanned aerial vehicle performance pattern by using the pattern generation model based on a set pattern generation requirement. According to the method, historical unmanned aerial vehicle performance patterns are collected and text marking is carried out to construct a training data set, the data set is utilized to train a text graph model and a control model thereof, a pattern generation model is constructed, and finally patterns are generated through the model pattern generation model according to set pattern generation requirements. In this way, the process of manual direct design or repeated adjustment can be reduced, the generation period of the unmanned aerial vehicle performance pattern is remarkably shortened through automatic model generation, and the generation efficiency is improved.

The invention discloses a multi-level network threat dynamic identification method based on a graph neural network, and the method comprises the following steps: collecting multi-source heterogeneous network security data, and carrying out the preprocessing; constructing a multi-level network threat graph; inputting the multi-level network threat graph into an improved GraphSAGE network to carry out graph embedding modeling; identifying an attack propagation path, and extracting a risk sub-graph region serving as a candidate attack chain; performing threat level evaluation on the risk sub-graph region, and calculating an overall threat score of the risk sub-graph region; and comparing the overall threat score with a preset threshold value, if the overall threat score exceeds the threshold value, determining that the threat is a high-risk threat, and outputting an early warning result. The multi-level threat graph is modeled through the graph neural network, attack chain recognition and threat evaluation are achieved, and the method has the advantages of being high in expressive power, accurate in recognition and fast in response.

The invention discloses a robot path planning method for warehouse logistics cluster operation, and belongs to the technical field of path planning. The method comprises the following steps: constructing a two-dimensional model diagram by using a grid method; the ant colony algorithm is improved; an improved ant colony algorithm is adopted to plan an operation path for each logistics robot; a local obstacle avoidance strategy is carried out through a dynamic window algorithm, and whether obstacles exist around is detected; and the collision type is judged, and a corresponding collision avoidance strategy is adopted until all the robots safely and correctly arrive at the preset target point. According to the method, the pheromone concentration is initialized by introducing the Sigmoid function, blind search in the early stage is avoided, the self-adaptive slope parameter is introduced, and the steep degree of the Sigmoid function is dynamically adjusted according to environmental characteristics; meanwhile, the ant colony algorithm and the dynamic window algorithm are fused, parameters are dynamically adjusted according to the number of iterations, the obstacle rejection weight is introduced, the algorithm is prevented from falling into local optimum, and global path planning and local dynamic obstacle avoidance are achieved.

The invention discloses a civil engineering construction task automatic scheduling method and system. The method comprises the steps that a heterogeneous graph model representing construction task elements and the incidence relation of the construction task elements is constructed; processing the heterogeneous graph model by using a graph neural network to mine a dependency relationship between processes and extract risk association features related to uncertainty to form a construction state graph; constructing a Bayesian network based on the construction state atlas; probabilistic reasoning is carried out through a Bayesian network, the influence of uncertain factors is quantified, and key information representing risk conduction is fed back to the graph neural network so as to dynamically correct recognition of the graph neural network on a process dependency relationship; the dependency constraint output by the graph neural network and the risk quantification result output by the Bayesian network are fused, and a construction task scheduling scheme is generated; and synchronously updating the heterogeneous graph model, the graph neural network and the Bayesian network according to field construction feedback data to realize closed-loop iterative optimization of a scheduling scheme.

Disclosed in the embodiments of the present application are a model rendering method and a related apparatus. The method comprises: for an initial three-dimensional model to be processed, simplifying the model structure of the initial three-dimensional model on the basis of a topological structure feature of the initial three-dimensional model, so as to obtain a simplified three-dimensional model, wherein the number of meshes comprised in the simplified three-dimensional model is less than the number of meshes comprised in the initial three-dimensional model, and the difference between the simplified three-dimensional model and the initial three-dimensional model meets a preset similarity requirement; by means of lighting simulation processing, determining a lighting intensity corresponding to a vertex in the simplified three-dimensional model when the simplified three-dimensional model is in a virtual environment; and on the basis of the simplified three-dimensional model and the lighting intensity corresponding to the vertex in the simplified three-dimensional model, rendering the simplified three-dimensional model, so as to obtain a model image for representing the simplified three-dimensional model. The method can automatically reconstruct and render a three-dimensional model, thereby improving the reconstruction efficiency of the three-dimensional model and reducing labor costs.

The invention belongs to the technical field of model-driven system engineering and intelligent system modeling, and particularly relates to a test requirement formalized design method based on primitive intelligent extension. The method comprises the following steps: obtaining a semantic map, and defining semantic nodes in the semantic map as type modules or intelligent modules; instantiating the type module to obtain a type model instance node, and instantiating the intelligent module to obtain an intelligent model instance node; organizing the type model instance nodes and the intelligent model instance nodes into a model graph; constructing an extended state primitive, constructing a plurality of interactive combination states based on the extended state primitive, defining an intelligent migration mechanism and constructing an extended state machine diagram; respectively testing the model diagram and the expansion state machine diagram, and if the model diagram and the expansion state machine diagram are tested to be qualified, exporting the model diagram and the expansion state machine diagram; the method can solve the problems of dynamic behavior description fragmentation, multi-agent interaction mechanism stiffness and insufficient model reusability of a traditional system modeling language in intelligent system modeling.

In one aspect, a method for modeling graphs on distributed storage, comprising: receiving graph data including vertices, edges, and properties; mapping each vertex to a vertex record in a distributed storage system; mapping each edge to an edge record in the distributed storage system; mapping each property to a property record in the distributed storage system; determining, for each vertex, whether a number of incident edges exceeds a maximum record size threshold; storing pointers to incident edge records directly within vertex records when the number of incident edges does not exceed the maximum record size threshold; utilizing an adjacency index to lookup incident edge records when the number of incident edges exceeds the maximum record size threshold; and executing graph traversal operations that navigate from vertex to vertex by accessing the appropriate edge records through either the stored pointers or the adjacency index.

The invention provides a secondary equipment debugging intelligent auxiliary method and system based on a secondary logic model, and relates to the technical field of debugging assistance, and the method comprises the steps: collecting multi-source structured data related to secondary equipment; constructing a secondary logic model diagram for debugging assistance by taking the equipment nodes as objects and the communication relationship and the logic control relationship as connecting edges based on the multi-source structured data; obtaining a debugging task; mapping the debugging task to a corresponding node in the secondary logic model diagram through a semantic and configuration fusion matching mechanism; based on the secondary logic model diagram and the debugging task mapping result, generating a structured debugging operation template in a mode of combining template retrieval, state injection and logic verification; based on the structured debugging operation template, comparing and checking the running state data of the current debugging object equipment; and on the basis of the comparison check result and the structured debugging operation template, executing online monitoring of the link state, and assisting in realizing intelligent analysis of the debugging state.

The embodiment of the invention discloses a self-adaptive merging method and system based on multiple single BIM models, and the method comprises the steps: carrying out the projection of the single BIM models from three dimensions to two dimensions, obtaining a two-dimensional model diagram on a two-dimensional plane, and extracting feature intersection points and feature edges from the two-dimensional model diagram; on the basis, combining vertexes and edges in the single BIM models and feature intersection points and feature edges corresponding to the two-dimensional model graph to form a model feature vector matrix, calculating model feature values, and obtaining a model feature value set of the multiple single BIM models; and carrying out clustering analysis on the model characteristic value set to obtain a fitted and combined target model, and carrying out combination to realize light weight of the model. And moreover, the single BIM models are automatically processed in the whole process, manual operation of designers is not needed, and the model merging efficiency is improved.

Provided are an explainable artificial intelligence (XAI) apparatus and a method for analyzing a model thereof. The XAI apparatus according to the present invention may use bytecode of a model to generate a model graph even for models including dynamic control flow, which are otherwise unable to generate a graph, in order to provide visualization of the decision-making process of an XAI algorithm.

The embodiment of the invention discloses a model rendering method and a related device, and the method comprises the steps: carrying out the simplification processing of a model structure of a to-be-processed target three-dimensional model based on the topological structure characteristics of the target three-dimensional model, and obtaining a simplified three-dimensional model; wherein the number of grids included in the simplified three-dimensional model is smaller than the number of grids included in the target three-dimensional model, and the difference between the simplified three-dimensional model and the target three-dimensional model meets a preset difference requirement; through illumination simulation processing, determining illumination intensity corresponding to a vertex in the simplified three-dimensional model when the simplified three-dimensional model is in the target virtual environment; and based on the simplified three-dimensional model and the illumination intensity corresponding to the vertexes in the simplified three-dimensional model, rendering the simplified three-dimensional model to obtain a model image for representing the simplified three-dimensional model. According to the method, the three-dimensional model can be automatically reconstructed and rendered, the reconstruction efficiency of the three-dimensional model is improved, and the labor cost is reduced.

The application relates to the technical field of image processing, and provides a model material replacement method and device for a design drawing. The method comprises the following steps: determining a target model from each model of a design drawing according to a received material selection instruction; obtaining a target material map corresponding to the material selection instruction from each material map of the target model; and merging the target material map, a light map, a diffuse reflection map and a reflection refraction map of the design drawing to obtain a model image of the target model in the design drawing; wherein the design drawing is an image obtained through rendering; the light map, the diffuse reflection map and the reflection refraction map are each layer obtained after the design drawing is rendered. The model material replacement method for the design drawing provided by the application can reduce the calculation amount of the model in the design drawing when the material is replaced, and reduce the resources consumed when the model material is replaced.

The invention discloses an LDPC (Low Density Parity Check) code check matrix construction method, which is used for processing a low earth orbit satellite ground foundation zone, and comprises the following steps of: A1, determining degree distribution of a basis matrix under a high code rate by adopting an extrinsic information transfer graph EXIT, and realizing construction of the basis matrix under the constraint of optimal degree distribution through extrinsic information transfer P-EXIT based on a basic model graph; a2, based on the code length and the code rate of FEC coding of the DVB-S2 protocol, determining the size and the expansion factor of a basis matrix corresponding to each code rate; a3, determining a finite field and a generator based on an expansion factor, and constructing a linearly independent shift value row vector and cycle coefficient table; a4, selecting row vectors from the cyclic coefficient table to construct a cyclic shift matrix, and performing short ring identification and elimination on the cyclic shift matrix; and A5, performing matrix hashing on the basis of the basis matrix and the cyclic shift matrix to obtain an LDPC check matrix.

This application relates to the field of simulation technology and discloses a method, apparatus, electronic device, and storage medium for simulation result analysis and evaluation. The method includes: in response to receiving any simulation result, determining whether the simulation result meets a performance target; when the simulation result meets the performance target, at least extracting the model diagram of the simulation model to which the simulation result belongs and at least one directly related scheme parameter, and obtaining at least one non-directly related scheme parameter related to the performance target and / or the simulation result, to determine the evaluation value and influence coefficient corresponding to each non-directly related scheme parameter; calculating a simulation evaluation score based at least on the evaluation values ​​and influence coefficients corresponding to all non-directly related scheme parameters, and then outputting an overall simulation evaluation; calling a simulation report template, and generating simulation optimization suggestions and an evaluation report based on the template, at least according to the overall simulation evaluation, the model diagram, and at least one directly related scheme parameter. This application facilitates the automated optimization and evaluation of simulation results.

The application provides a hierarchical automatic layout method and device for internal module graphs, comprising the following steps: constructing a preliminary shape of a model graph layout; scanning each node, if there is a nested relationship, taking the nested information as a subgraph of the current node; scanning each node of the nested information, if there is a nested relationship, taking the nested information as a subgraph of the current node, and constructing a preliminary shape of a nested graph layout; determining virtual connections between different node nested relationships; and correcting the preliminary shape of the model graph layout to obtain a final shape of the model layout. In the layout process, the ports and graph element nesting involved in the internal module graph are considered, and a hierarchical automatic layout method completely covering the internal module graph elements is realized.

The invention discloses a protein and small molecule binding structure prediction method, device, equipment and medium, and relates to the technical field of protein and small molecule binding prediction.The method comprises the steps that a first modeling graph structure and a second modeling graph structure after a protein block and a to-be-tested small molecule compound are modeled respectively are obtained; the first modeling graph structure and the second modeling graph structure are input into a TANKBind model, and a predicted protein small molecule compound is output through the TANKBind model; generating a first topological file and a second topological file corresponding to the protein structure file and the small molecule structure file of the protein small molecule compound; performing molecular dynamics simulation by using a preset molecular dynamics simulation tool according to a simulation file generated by the first topological file and the second topological file to obtain a molecular dynamics simulation track file; and analyzing the molecular dynamics simulation track file, the first topology file and the second topology file to obtain a simulation analysis result.

The application provides a large model inference optimization method, system, medium, terminal and program product for Ascend processors. The method comprises: comparing the automatically detected version with a built-in compatibility table; if the versions do not match, automatically loading a runtime patch or calling a backup library; if the versions match, processing a user request based on a preset hybrid scheduling mechanism; dynamically selecting a MindIE inference framework or a vLLM inference framework based on a runtime adaptation layer according to the user request parameters; converting large model weights into low-bit weights and compensating based on an error compensation matrix; identifying subgraphs and fusing operators for a model graph according to the computing characteristics of the Ascend processor, and rearranging intermediate tensors according to the 512B memory access alignment rule of the Ascend processor; performing pipelined overlap processing on the matrix multiplication calculation of the current token and the full aggregation operation based on an asynchronous execution flow; and performing inference and generating an inference result according to the user request. The application can improve the efficiency and reliability of large model inference.

The invention relates to an automobile automatic window falling method, system and device, and belongs to the technical field of automobile control. The water falling working condition is analyzed based on the real-time invasion speed of the water surface relative to an automobile door and the distance between the water surface and the lower edge of an automobile window, and an analysis result is obtained; the method comprises the following steps: acquiring environment characteristic data information around an automobile by using an outside-automobile look-around fisheye camera and a millimeter-wave radar, and performing abnormality judgment according to the environment characteristic data information around the automobile and a model graph loaded by the automobile to obtain a first abnormality judgment result or a second abnormality judgment result, and generating an emergency window descending scheme according to the first abnormity judgment result or the second abnormity judgment result. Through multi-sensor fusion and multi-condition judgment after the automobile falls into water, the window falling function can be provided when the automobile falls into water, whether obstacles exist around after the automobile falls into water to prevent opening of automobile windows or not is judged, passengers can be further prevented from being trapped in the automobile after the automobile falls into water, and great life safety hidden dangers brought to the passengers are avoided.

The present application relates to the technical field of hub polishing robots, and particularly relates to a hub polishing robot trajectory planning method based on 3D vision curve technology; by comparing an actual 3D curved surface graph with a preset 3D curved surface graph, a to-be-polished region model graph of each to-be-polished region in a target hub is generated; according to the to-be-polished region model graph of each to-be-polished region, optimal polishing parameters for polishing processing of each to-be-polished region in the target hub are obtained by pairing in a database; according to the optimal polishing parameters for polishing processing of each to-be-polished region in the target hub, optimal polishing trajectories of a polishing robot are planned; and the optimal polishing trajectories of the polishing robot and the optimal polishing parameters for polishing processing of each to-be-polished region in the target hub are sent to a control terminal. More accurate, self-adaptive, efficient and consistent polishing effects can be achieved, and production efficiency and product quality are improved.

The application aims to provide a network community class group and user unified modeling graph pre-training method, which comprises the following steps: constructing a heterogeneous graph model with hierarchical features by extracting semantic information and structural information of two different objects of community level and user level; in the pre-training process of the model, by introducing a self-supervised task that can adapt to different hierarchical objects, the model can represent the characteristics of different hierarchical objects and the mutual relationship between the hierarchical levels; in the pre-training process of the model, by designing a hierarchical iterative training method, the model can promote the mutual representation between different hierarchical levels, and complete various complex inference tasks with community and user as target objects.

The invention relates to the technical field of drawing recognition, in particular to an architectural drawing element intelligent recognition and labeling method based on graphic feature extraction, comprising: acquiring an architectural drawing file to be processed, the architectural drawing file to be processed comprising a two-dimensional CAD drawing and a three-dimensional building information model drawing; format analysis is conducted on the to-be-processed building drawing file, graph geometric data and layer description information are extracted, the graph geometric data comprise point coordinates, line coordinates and plane coordinates forming a building graph, and the layer description information comprises name information and layer priority information of building drawing layers; and building a building drawing feature database according to the graphic geometric data and the layer description information. According to the method, through deep fusion analysis of the graphic geometric data and the layer description information, and through the structured drawing feature database and the feature fusion module, various elements in the architectural drawing can be accurately identified and labeled, and the accuracy of drawing labeling can be effectively improved through the accurate identification.

The model graph receives the data cue as input. The data cue is segmented into a plurality of segments. An instance of the model graph is generated for each segment of the data cue. Each instance of the model graph is also pruned according to policy information associated with a corresponding data cue segment of the model graph instance. Each instance of the model graph generates an intermediate output. A final output for the model graph of the entire data cues is generated based on the combination of the intermediate outputs.

The application relates to a low-altitude signal coverage prediction method and system based on spherical non-uniform sampling, wherein the method comprises the following steps: determining the region complexity of different regions according to a digital surface model graph, constructing a direction sampling weight function according to the region complexity and an antenna directional diagram gain; performing spherical non-uniform sampling on a unit sphere based on the direction sampling weight function to generate a ray direction set matched with the sampling weight; calculating the spherical solid angle corresponding to each sampling direction based on the ray direction set, calculating the transmission power corresponding to each ray according to the spherical solid angle and a base station antenna directional diagram; taking the ray direction set and the transmission power as inputs to perform ray tracking propagation calculation to obtain the received signal power of each sampling point at low altitude, and the low-altitude signal coverage prediction is completed. The problems of how to improve the efficiency and accuracy of low-altitude signal coverage prediction in the related art are solved.

A coupled graph database pair for management of a platform is presented. The coupled graph database pair comprises a model graph database managed in a manufacturer environment, an instance graph database managed in a customer environment, and a plurality of relationships between the nodes of the model graph database and the nodes of the instance graph database. The model graph database comprises nodes of assemblies, sub-assemblies, and parts for a designed configuration of a model of the platform. The instance graph database comprises nodes of assemblies, sub-assemblies, and parts for a real-time configuration of the platform.

1. Name of the product in this design: Response item for a graphical user interface of an intelligent dialogue system for electronic devices. 2. Intended use of this design: for use in an electronic device. 3. The key design features of this product are: the graphical user interface that uses non-dashed lines to represent the interface content. 4. The picture or photo that best illustrates the key design points: Design 1 front view. 5. Design 1 is designated as the basic design. 6. Purpose of the graphical user interface: The graphical user interface of this design is used as a whole for an intelligent dialogue system, and the protected part is used for the response items of the graphical user interface of the intelligent dialogue system. Intelligent dialogue systems are representations of intelligent agents. The response items of the intelligent dialogue system include cards for presenting feedback information from multiple different models, a control bar for switching between multiple cards, and interactive controls for the control bar. In Design 1, users can click on the model icon in the interactive controls of the control bar to switch between different model feedback information cards corresponding to different model icons in order to view the detailed feedback information of different model feedback information cards. In Design 2, users can click on the model icon in the interactive controls of the control bar to switch to the card containing the feedback information of the model corresponding to the clicked model icon. The interface after clicking is shown in the changing state diagram. In Design 3, users can hover over the control bar to bring up its interactive controls, as shown in the changing state diagram. In Design 4, users can click on the model icon in the interactive controls of the control bar to switch to the card containing the feedback information of the model corresponding to the clicked model icon. The interface after clicking is shown in the changing state diagram. 7. Other situations requiring explanation: Other explanation: "X" in the interface represents a text, number, or symbol area. Content represented by dashed lines in each design is not required to be protected.

The invention relates to the technical field of communication network operation and maintenance, in particular to a method and system for constructing a home width intelligent number asking agent based on a large model, and the method comprises the following steps: constructing a data intelligent question and answer mechanism: constructing an intelligent search mechanism by adopting a large model + graph database + vector database technology, converting the intention of a user into a vector, and constructing a data intelligent question and answer mechanism; data search is performed through a large model technology, so that data are locked by one sentence, and a user is prevented from manually searching reports and positioning indexes in hundreds of report menus; the method has the beneficial effects that aiming at a current home wide report index library, standardized / normalized sorting of indexes is carried out, index logic is sorted, names, descriptions and calibers are unified, and the data accuracy is ensured; for abnormal fluctuation of the data, whole-process monitoring, data anomaly detection, data consistency auditing and the like of the index are provided, and the stability of the index data is ensured.

The invention provides a hierarchical automatic layout method and device for an internal module graph. The method comprises the following steps: forming a preliminary shape of a model graph layout; scanning each node, and if a nesting relationship exists, taking nesting information as a sub-graph of the current node; scanning each node of the nested information, and if a nested relationship exists, taking the nested information as a sub-graph of the current node to form a preliminary shape of a nested graph layout; determining a virtual connection line between different node nesting relationships; and correcting the initial shape of the model graph layout to obtain a final shape of the model layout. In the layout process, ports and graph element nesting conditions related to the internal module graph are considered, and the hierarchical automatic layout method completely covering the elements of the internal module graph is realized.

The application discloses an interactive system and method based on digital audio and video family tree and family memory simulation, and aims to solve the problems of weak interaction of traditional family tree, rigid digital clone model and split memory personality. The system constructs an interactive family tree knowledge base containing member nodes, blood relationship and related data, and forms a structured family knowledge graph in multiple dimensions. The model graph co-evolution is proposed: multi-modal data is collected through daily interaction, incremental learning and knowledge graph updating are adopted in parallel, parameter efficient fine-tuning is used for lightweight learning of the digital clone, only a small amount of parameters are updated to overcome forgetting, and new event relationships are extracted to update the graph. Through bidirectional indexing, the personality and memory semantics are consistent, the multi-modal interaction is relationship-aware and dynamically evolved. The application synchronously updates the personality and memory, solves the problem that the digital clone cannot continuously evolve with the family, and realizes stereoscopic inheritance of family memory.

The invention discloses a power distribution network load flow calculation method based on graph synchronization parallel and forward-backward substitution, and the method comprises the steps: building a graph model in a graph database based on a power network, and enabling the graph model to be divided into a plurality of sub-graph models; setting an initial state for nodes in the sub-graph model, traversing the sub-graph model in a graph node parallel computing mode, and recording a node layer number and a node path of the sub-graph model; and in combination with the sub-graph model, completing graph calculation through forward tracking current calculation and backward tracking voltage calculation in sequence by utilizing a forward-backward substitution method, and judging whether an absolute value of a voltage difference value of nodes in the sub-graph model is smaller than the maximum iteration convergence precision or not. According to the method provided by the invention, the graph model is established, and graph calculation is carried out in a graph parallel mode in combination with a forward-backward substitution method, so that the calculation speed can be obviously improved.