217 results about "Local topology" patented technology

The invention relates to the technical field of power system topology identification, in particular to a power distribution network frame topology identification method based on an improved graph neural network, and the method comprises the steps: collecting the real-time electric quantity data of nodes and edges of a power distribution network, and carrying out the modeling of a power distribution network graph structure; the method comprises the following steps: constructing a node, edge and hyperedge feature matrix by using real-time collected data, inputting an improved graph neural network topology identification model, dynamically weighting and adjusting an edge weight through a graph attention network, splicing and fusing local topological features and global topological features, and generating a prediction adjacency matrix; when the power distribution network is dynamically changed, a change area is positioned through adjacency matrix difference, and a sub-graph is extracted for incremental updating; and constructing a topological structure of the power distribution network based on the final adjacent matrix, and outputting a connection relationship between the physical positions of the nodes and the edges. Compared with the prior art, the real-time performance, generalization ability and applicability of the model are remarkably improved, and the problems of accuracy and efficiency of power distribution network topology identification in the dynamic environment are solved.

A three-dimensional point cloud semantic segmentation method based on graph convolution and a grouping vector attention mechanism adopts a network structure of a U-Net style, feature extraction and propagation are performed on point cloud through multi-level feature coding and decoding, each level firstly centralizes and standardizes local neighborhood features through a geometric affine module, and then the local neighborhood features are subjected to feature extraction and propagation; the capturing capability of the model on the diversity of the local region is enhanced; secondly, constructing a local neighborhood graph, and designing an asymmetric edge feature learning function to extract local topological information; deep edge features are further extracted in combination with a residual network; and finally, selectively fusing local features and global context information under the condition of low parameter quantity by using a grouping vector attention mechanism, and performing cross-level feature interpolation up-sampling to an original scale to complete point cloud segmentation. The method can significantly improve the point cloud segmentation precision, and is suitable for the perception of a mobile robot and other devices carrying a three-dimensional sensor on a complex scene.

The invention belongs to the technical field of data processing, and discloses a multi-modal project data analysis method, which comprises the following steps: collecting heterogeneous modal project data, carrying out denoising and timestamp correction on each modal project data based on an adaptive multi-scale filtering technology, carrying out cross-modal time alignment, and outputting a multi-modal signal. Constructing a multi-level Boltzmann machine energy network, and modeling the joint probability distribution of the multi-modal signals; a dynamic complexity measurement mechanism based on topological entropy is designed and used for representing project structure complexity, and a topological entropy dynamic field is generated; the topological entropy dynamic field is used as a constraint condition, a multi-dimensional covariant field frame is used, nonlinear coupling and space-time propagation between multi-modal signals are simulated, the multi-modal signals are abstracted into a string vibration mode, local topological defects in the topological entropy dynamic field are recognized, and an abnormal fluctuation map is generated; and the analysis of the complex multi-modal data in the project operation process is more intelligent and controllable.

The invention belongs to the technical field of electric power automation, and discloses a power grid load flow calculation method based on a graph neural network and a related device. The method comprises the following steps: acquiring power grid data to generate a power flow data set; constructing a node feature vector and an edge feature vector based on the power flow data set; splicing the node feature vectors and the position codes of the nodes in the power grid to obtain splicing features; and inputting the splicing feature and the edge feature vector into a pre-trained graph neural network model to obtain a power grid load flow calculation result based on the graph neural network. Compared with a traditional Newton-Raphson method and a graph neural network method considering a local topological relation, the method has the advantages that the calculation speed can be remarkably increased, and meanwhile, the accuracy and reliability of a result can be kept; the method has the advantages of being high in adaptability, wide in application scene and the like, can adapt to power grids of different scales and topological structures, has good generalization ability, and can be applied to multiple application scenes such as real-time power flow calculation and future state power flow prediction.

The invention belongs to the related technical field of brain image processing, and provides an Alzheimer's disease classification method and system based on topology perception and a group hypergraph in order to solve the problem of inaccurate classification of the Alzheimer's disease in the prior art. Constructing a dynamic function connection network sequence through a sliding window strategy; a local topology perception encoder and a global topology perception encoder are respectively used for extracting local topology features and global topology features of each time window, deep interaction and fusion are carried out, and comprehensive feature representation of a tested level is generated; according to the method, each subject is used as a hypergraph node, hyperedges are constructed on the basis of comprehensive feature representation of a subject level and by combining feature similarity calculated by diffusion tensor imaging features and clinical embedded features of the subject, then a group hypergraph is constructed, a classification result is obtained by using a hypergraph neural network, and the early classification diagnosis accuracy of the Alzheimer's disease is effectively improved.

The invention relates to the technical field of unmanned aerial vehicle cooperative control, and discloses a multi-unmanned aerial vehicle cooperative control method based on data link networking. The method comprises the following steps of: constructing a cluster collaborative task data set comprising a flight path feature, a load state feature and a communication link feature; extracting dynamic cooperative nodes, including a cluster formation form switching node and a task target switching node, from the cluster formation form switching node and the task target switching node; generating a track cooperative trigger signal based on the dynamic cooperative node, and identifying a local topology adjustment node triggered by the formation form switching and a global topology reconstruction node triggered by the task target switching; generating a cooperative offset parameter containing a track offset and a communication delay offset according to the node classification; extracting cluster collaboration protocol parameters based on the collaboration offset parameters; and controlling the unmanned aerial vehicle cluster to execute a cooperative flight task according to the protocol parameters. The method can improve the cooperative efficiency and task adaptability of the unmanned aerial vehicle group in a dynamic environment, and is suitable for various multi-vehicle cooperative operation scenes.

The invention relates to the technical field of bridge safety monitoring, and comprises a bridge safety monitoring analysis system based on big data, and the system comprises a data collection processing module, a manifold feature dimension reduction module, an entropy change partition evaluation module, a health state dynamic analysis module, and a bridge safety early warning module. According to the method, a bridge measuring point topological relation matrix is established, measuring point spatial distribution characteristics are analyzed, spatial relevance of a bridge structure is reflected, measuring point local curvatures are calculated, a local curvature matrix is established, a neighborhood similarity matrix is combined, low-dimensional projection transformation is executed, local topological consistency of data is kept in the dimensionality reduction process, an entropy change partition matrix is established, and the spatial relevance of the bridge structure is reflected. The health states of different structural parts of the bridge are subjected to differential analysis based on mechanical characteristics, the entropy change trend and damage probability of a measuring point are calculated, and the overall safety state level of the bridge is judged through a damage risk threshold, so that the local damage severity of the bridge can be comprehensively considered in safety assessment, and the stability and adaptability of safety state judgment are improved.

The invention discloses a security monitoring abnormal behavior image recognition method based on time sequence convolution, and the method comprises the following steps: S1, obtaining monitoring video data, and constructing a key point time sequence; s2, modeling the key point time sequence by using a manifold embedded Sheng differential equation, and performing smooth change in a manifold space by using an exponential mapping method; s3, based on a self-adaptive step size solving strategy, dynamically adjusting the time step size according to the change rate of the optimized key point time trajectory; s4, constructing an adaptive topology time sequence convolutional network, and generating enhanced key point time sequence features; s5, using local topology regularization to constrain the amplitude of the topological change of the key points, and extracting the feature representation of the human behavior; s6, carrying out abnormal behavior classification by adopting a classification network; and S7, setting an alarm threshold value, and providing early warning information through the monitoring platform. According to the method, the manifold embedding Shenchang differential equation and the adaptive topology time sequence convolution technology are utilized to accurately identify abnormal behaviors.

A system transforms video frames into event metadata reports on object recognition and occupancy patterns and trends. Each camera distinguishes foreground content and uploads meta data and changes. Occupancy within bounding boxes, and object pre-cognition are transformed to non-image event data sets. Objects and persons of interest are tagged for optical tracking and correlation. Cloud analytics estimate probabilities of occupancy and change. Periodic capture is augmented by expectation of peaks and valleys. Imagery and event meta data across multiple cameras are combined for object recognition. The cloud reports and predicts regions of interest due metrics of occupancy. Each edge device is trained on the local topology of other edge devices and actuators through which objects may pass before and after entering its region of interest. Edge devices collectively initiate physical access actuator controls, predict events for other edge devices, and transmit alerts when low probability events occur.

The invention discloses a three-dimensional physical field real-time prediction method and system based on geometric deep learning and physical constraint, and relates to the technical field of computer-aided engineering and artificial intelligence. The method comprises the following steps: directly extracting native boundary representation data (B-Rep) of a three-dimensional model from a computer aided design system; constructing a heterogeneous dual graph taking a parameterized curved surface as a graph node, skipping finite element grid division, and aggregating local and global topological features by using a graph neural network; in combination with a physical information driving mechanism, a partial differential equation (PDE) residual error is introduced as a loss function for constraint training, and generalization prediction of a novel geometric structure is realized; and finally, the physical field state quantity is predicted through direct regression and is rendered in real time. An incremental reasoning mechanism based on a local topology subgraph is adopted, millisecond-level physical field real-time feedback under design modification is achieved, and the method is suitable for scheme rapid screening and trend prediction in the initial stage of design.

The invention belongs to the technical field of space intelligence, and relates to a dynamic obstacle intervention-oriented space intelligent scene self-reconstruction navigation system, which comprises a dynamic intervention sensing module, a dynamic obstacle intervention processing module, a dynamic obstacle intervention processing module, a dynamic obstacle intervention processing module, a dynamic obstacle intervention processing module and a dynamic obstacle intervention processing module, the spatial semantic topology reconstruction module is used for automatically updating a spatial semantic map and a topology connection relation according to the dynamic intervention event set; the local topology rapid recombination module is used for carrying out rapid recombination on the intervened and influenced local topology based on a topology incremental learning principle; and the perception and navigation bidirectional consistency optimization module is used for establishing a reverse feedback path between perception and navigation. According to the invention, the real-time response and structure-level self-adaption of the navigation system to environment change can be realized, so that the stability and consistency of navigation decisions can be maintained in changeable scenes. The invention further provides a space intelligent scene self-reconstruction navigation method oriented to dynamic obstacle intervention.

The invention relates to the technical field of heterogeneous network optimization, and discloses a heterogeneous network dynamic routing collaborative optimization method based on reinforcement learning, which comprises the following steps: S1, deploying an agent at each node of a heterogeneous network, and initializing a state sampling period and an action space; s2, sensing the state of the heterogeneous network in real time and obtaining sensing data; s3, encoding the sensing data into a state vector; s4, designing a layered dynamic reward function, including short-term reward and long-term reward; dynamically setting a weight ratio according to the scene type and outputting the weight ratio as a comprehensive reward; s5, carrying out multi-agent collaborative decision-making, which comprises the following steps: constructing a local topological graph based on neighbor state information broadcasted by nodes; each agent selects an action based on the state vector through the DQN; and updating the network weight of the DQN by using the comprehensive reward. According to the invention, transmission real-time performance and sustainability can be considered, transmission delay and transmission cost are reduced, and the method is suitable for film and television production and industrial Internet of Things scenes with variable topology and complex interference.

The invention relates to the technical field of power inspection, and discloses a power cable monitoring analysis method and system. The method comprises the following steps: acquiring an overall topology network of a power system and the number of unmanned aerial vehicles, and partitioning nodes of the overall topology network to obtain a plurality of local topology networks; creating an inspection task and task data information based on the local topology network, and issuing the inspection task and the task data information to each unmanned aerial vehicle; controlling the unmanned aerial vehicle to go to the starting point of the corresponding partition, traversing and recording each node and branch based on a preset rule, and checking the electric wire in the traversing process; controlling the unmanned aerial vehicle to generate a new partition topology network based on the recorded nodes and branches; and after all the unmanned aerial vehicles complete the inspection tasks and return the inspection tasks, acquiring nodes and branches to be recorded by the unmanned aerial vehicles, summarizing the partition network as a total topology network, and deleting the repeatedly generated branches and nodes. The method has the advantage of reducing the problems of missing detection, re-detection and repeated path flight caused by positioning drift or line change.

The invention relates to the technical field of knowledge fusion, and discloses an interactive health science popularization question and answer guidance system for constructing a medical knowledge graph, comprising: a guidance instruction generation module extracting attribute variation dimensions between to-be-aligned data and candidate nodes and generating a guidance instruction; the feedback analysis module converts the feedback data into a feedback correction vector; the atlas fusion actuator determines an initial fusion weight according to the coordinate distance, determines a weight correction coefficient based on the topological centrality of the target node, constructs a weighted feature update function by using a feedback correction vector and a local topological relation to calculate a feature increment, and completes feature convergence fusion; according to the method, a priori constraint force balance feedback feature pointing value provided by a map structure is utilized, it is ensured that heterogeneous knowledge fusion conforms to medical ontology logic, and the global stability of science popularization map evolution is guaranteed.

The invention provides a communication optimization method for topological table persistent storage in efficient parallel computing, belongs to the technical field of storage communication, and aims to avoid pseudo sharing by aligning memory allocation through cache lines, expand local topological coverage through a topological entropy increment driven prefetching mechanism, and improve the reliability of the topological table persistent storage. Boundary processing is accelerated through a pre-calculation period mapping lookup table and a frequency domain transfer function vector, synchronization overhead is optimized through a concurrency control mechanism perceived by a read-write ratio, targeted cache preloading is achieved through stability and jitter degree two-dimensional evaluation, bandwidth consumption is reduced through an increment synchronization mechanism, and the stability of the cache is improved. The communication template is selected or the communication parameters are generated through adaptive conversion by matching the matching degree decision, and the technical problem that the parallel computing performance is reduced due to the fact that the communication overhead is too large in the topological table persistent storage process is solved.

The invention discloses a dexterous hand environment article sensing and modeling algorithm based on body sensing. The algorithm comprises the steps that a mechanical arm drives a dexterous hand to approach an object; tactile-spatial data acquisition: a three-axis force sensor is arranged at the tail end of each finger of the dexterous hand, and the system is in contact with an object through multiple fingers of the dexterous hand to acquire multi-dimensional information of the appearance, surface characteristics and local topology of the object; active exploration sampling, multi-finger movement of the mechanical arm and the dexterous hand, and active penetration of unknown object surface and enhanced sampling of local features through the dexterous hand by the system; the method comprises the following steps: modeling an object attribute, analyzing a mechanical response sequence obtained by tactile-spatial data acquisition and active exploration sampling, modeling a target object in three dimensions of elasticity, hardness and local geometric morphology, and realizing accurate identification and modeling of an unknown object appearance form and an object attribute in an environment. The distribution uniformity of the tactile point cloud and the model integrity are improved, the hardware dependency is reduced, and the environmental adaptability is enhanced.

The invention belongs to the technical field of image recognition and power grid capital construction sites, and particularly relates to a power grid capital construction dynamic risk recognition method based on a multi-modal image and LLM deduction, and the method comprises the steps: limiting the relation between an inspection object and a design based on a stress chain and an equipotential chain, and generating a constraint framework; performing multi-channel sampling on objects in the constraint framework; after the channels are aligned, a state evidence packet is generated, and the design relation and the state evidence packet are aligned; a stress chain and an equipotential chain are assembled, and a local topological graph is obtained through cutting and minimum necessary path selection; based on the local topological graph, mapping mechanism elements of the nodes and the edges into a fixed semantic slot position and action mode template, and generating mechanism description; and outputting a dynamic risk source list based on the mechanism description and the local topological graph. According to the method, the multi-modal image is subjected to constraint mapping to form the mechanism elements and the minimum necessary topology, and deduction is carried out on the stress chain and the equipotential chain, so that the dynamic risk source is accurately identified, and evidence-level traceable decision output is realized.

The invention relates to the technical field of retail management, in particular to a retail end online management method and system based on concurrency control, and the method comprises the steps: constructing a retail knowledge graph based on historical transaction logs through employing a space-time interaction entropy weighting algorithm; calculating a local topology edge density increment by applying a streaming dense subgraph increment mining algorithm, and determining a topology aggregation state of a target node; if the state is the mixed concurrent state, constructing an inventory time slice mirror image containing a reference inventory domain and a dynamic increment domain; and writing the request into the lock-free annular buffer during checking, and merging data by adopting a time sequence alignment algorithm after checking to carry out atomized updating. According to the method, static inventory and dynamic sales are physically isolated by constructing the inventory time slice mirroring and lock-free buffer mechanism, the read-write conflict in the scene of inventory while sales is solved, and zero-blocking hybrid concurrence is realized while the final consistency of inventory data is guaranteed.

The present invention belongs to the technical field of active distribution network fault self-healing in the power system, and provides a distributed fault self-healing method and system for an active distribution network based on hierarchical partitioning. Among them, the method includes that when the active distribution network system is operating normally, the intelligent terminal device uses the local topology information configured by itself to perform hierarchical area division on the active distribution network system, and distributes and stores the hierarchical area information; when the operating state of the active distribution network system changes, the intelligent terminal device adaptively updates the stored hierarchical area information; when a fault occurs in a certain section of the feeder in the active distribution network system, the fault section is located and isolated based on the adaptive hierarchical area information and the differential protection principle; after the fault section is isolated, each relevant intelligent terminal device uses the stored network hierarchical area information to perform distributed power supply restoration.

The invention relates to the field of image recognition, discloses an image recognition method and an image recognition system, and systematically solves the core pain point of traditional document recognition through optical-topology fusion processing and a dynamic resource allocation mechanism. The image recognition method is composed of an acquisition module, a grid module, a phase module, a setting module and a distribution module, pixel brightness is calculated and coded based on document RGB data, and a two-dimensional coding matrix is generated; constructing a geometric correction grid, forming a nonlinear constraint field, and enhancing the anti-deformation capability of the image; generating spiral phase light waves in the constraint field by using a spatial light modulator, and generating a time-varying phase map; determining a local topology index by detecting the number of phase jump times; and extracting the closed boundary region as a character block, and generating an analysis result file. The system breaks through traditional limitation, improves image geometric correction precision, feature extraction sensitivity and boundary judgment accuracy, efficiently completes document analysis, and is suitable for scenes such as document digitization and information retrieval.

The invention belongs to the technical field of computer vision and smart agriculture, and discloses a leaf point cloud reconstruction method and system based on projection constraint and hybrid supervision, and the method comprises the steps: obtaining multi-modal data of seedlings, and carrying out the preprocessing of the multi-modal data; constructing a point cloud self-encoding network with feature circulation consistency to output a prediction point cloud; the method comprises the following steps: according to the dynamic three-dimensional geometric loss of a sample integrity label dynamic switching constraint mode, using a micro rendering technology to project a predicted point cloud and then calculate the predicted point cloud and a two-dimensional segmentation mask so as to calibrate the micro projection consistency loss of an edge; the local topology regularization loss of complementing the smoothness of the region is ensured; and dynamically adjusting the weight of each loss item by adopting a course learning strategy to carry out network training. According to the method, sub-millimeter-level edge reconstruction is realized by using two-dimensional image information which is easy to obtain, and the quality of incomplete blade point cloud completion and the accuracy of blade area measurement are remarkably improved.

The invention relates to the technical field of medical care informatics, in particular to a cancer type prediction method and system based on methylation data. The method comprises the following steps: acquiring whole genome methylation data of a sample; performing layering processing based on an epigenome functional region on the whole genome methylation data to obtain functional region layering data; according to the layered data of the functional regions, constructing a local methylation topological mode in each functional region to obtain a local methylation topological representation, the local methylation topological mode comprising a methylation space gradient, a methylation curvature feature and a region methylation entropy; mapping of methylation mode variable coefficients among different cell types is carried out according to local methylation topological representation, and a regional methylation stability index is obtained through calculation. According to the method, functional region layering and local topology construction are performed on methylation data, so that the regulation state of each functional region in the genome can be reflected more finely.

The invention belongs to the technical field of power distribution equipment monitoring, and relates to an intelligent power distribution equipment monitoring and fault early warning system and method, and the system comprises an acquisition unit which is used for obtaining an electric parameter, an environment parameter and an operation state quantity of power distribution equipment and forming time synchronization feature data; the topology unit is used for generating a local topology model according to the relation between the cabinet, the loop and the busbar and outputting topology attributes; the evaluation unit is used for generating an operation state evaluation result based on the feature data and the topological attributes and updating an early warning threshold value; the governance unit is used for performing deduplication, suppression and credibility calculation on the alarm to form a disposal level; the interlocking control unit is used for checking and outputting a control action when the disposal level meets an interlocking condition; and the operation and maintenance unit is used for generating a preventive test plan and updating parameters based on a test result. According to the technical scheme of the invention, topological association and threshold update are introduced into monitoring, evaluation, disposal and control links, so that state recognition and control execution are kept consistent.

The invention provides a sea grass bed classification method based on feature screening under a small sample limitation condition, which belongs to the technical field of marine surveying and mapping, extracts airborne LiDAR sounding data and hyperspectral image data features, completes spatial registration and fusion, screens out high contribution rate features based on a Relief-F and KPCA multi-strategy feature optimization model, and performs nonlinear dimensionality reduction. Generating a preferred feature set; establishing an edge structure based on a K-nearest neighbor strategy so as to form a graph structure with a local topological property, introducing a graph attention mechanism to adaptively update node features, and dynamically adjusting an information transfer weight according to feature similarity between samples; a prototype network is constructed based on updated node features for classification, each type of prototype is represented by a supported sample feature mean value, query samples are classified by calculating Euclidean distances between the query samples and the prototypes, the problems of feature redundancy and small samples existing in actual seaweed bed classification application are effectively solved, and the classification efficiency is improved. And an effective technical support is provided for sea grass bed identification and restoration.

The invention relates to the technical field of artificial intelligence, in particular to a knowledge distillation acceleration method and system for a multi-modal large model, and the method comprises the steps: obtaining multi-modal input data; feature mapping of topology maintenance is executed, topological representation of a multi-modal feature space is constructed, and a feature mapping module is designed based on a residual module; a topological hierarchical attention mechanism is applied, and multi-head topological attention fusion is realized through sensing of a local topological attention unit and a global topological structure; topology-driven multi-task knowledge distillation is executed, five cross-modal distillation tasks are designed, and distillation loss is calculated based on topological similarity measurement; a topology-aware model acceleration technology is adopted, adaptive quantization is performed based on feature topology importance, neural network structure search for topology maintenance is executed, a knowledge distillation process is guided through a topology principle, a topological structure relationship of a feature space is maintained, and multi-modal knowledge is effectively transmitted. Experiments show that compared with an original large model, the method has the advantage that the size of the model is reduced by 80-90%.

The invention discloses an evaluation model training method and an evaluation method for classroom teaching comfort. The method comprises the following steps: collecting a data sample through an Internet of Things sensor in a classroom; expanding the labeled data sample by using a data expansion model, wherein the data expansion model is a generator in the trained adversarial network model; the expanded labeled data samples are input into a feature extraction model for feature extraction so as to obtain multi-dimensional feature expression, and the feature extraction model is obtained through training of a neural network model subjected to dynamic group evolution optimization; the multi-dimensional feature expression is input into a feature dimension reduction model for feature dimension reduction so as to obtain low-dimensional feature expression, and the feature dimension reduction model is an encoder in a trained self-encoding neural network algorithm; and inputting the low-dimensional feature expression to a classifier for training, the classifier being a trained local topology deep neural decision tree algorithm, and the classifier being used for outputting a classroom teaching comfort level.

The invention relates to the technical field of power grid graph-reality consistency checking, and particularly discloses a graph-reality consistency checking method and system based on local topology updating. Aiming at the condition that more terminal devices which cannot be directly replaced exist in an existing transformer area, edge nodes are arranged on a target transformer area according to an area division mode so as to construct a basic hardware architecture, a power grid topology is constructed in a mode of combining edge calculation with a dynamic updating mechanism, and local updating is realized; according to the method, the topological graph of the target transformer area is completed, data transmission and calculation loads of a power grid cloud control end are saved under the condition that the topological updating accuracy is guaranteed, and the topological updating efficiency of the target transformer area is improved. According to the method and the device, the problem that the topology updating is not timely and inaccurate due to the fact that more terminal devices which cannot be directly replaced exist in the existing transformer area is solved.

The invention discloses a power distribution network abnormity positioning and warning method based on a time sequence power flow model, and relates to the technical field of power distribution network power flow monitoring. Layered verification is carried out on source diameter nodes of the power distribution network, power flow parameter verification is carried out, a real-time power flow graph under a dynamic time sequence is generated based on a constructed time sequence power flow model so as to carry out dynamic time sequence feature extraction, and then comparison is carried out based on an extraction result and a power flow parameter corresponding to a power flow starting point time sequence so as to obtain a dynamic time sequence feature extraction result. Meanwhile, anomaly detection is carried out, an anomaly detection dynamic tidal current diagram is obtained, the situation that tidal current parameters of a plurality of source path nodes in a certain hierarchy are abnormal at the same time is positioned, finally, hierarchical positioning and local topology adjustment of the abnormal source path nodes are carried out, and the abnormal source path nodes in the power distribution network are isolated. Meanwhile, warning scheme verification and dynamic power flow graph updating are carried out in combination with a time sequence power flow model, and the problem that in the prior art, the false warning rate is high when the topological structure changes in the topological correlation operation process of the power distribution network is solved.

The invention provides a distributed robust topology control method and system for cluster unmanned aerial vehicles, and the method comprises the steps: carrying out the construction of a midpoint coverage circle with a node as a center for each node of a cluster unmanned aerial vehicle network, and judging potential key nodes; acquiring local topology through k times of neighbor information exchange for potential key nodes, generating a simplified block tree model based on depth-first search, traversing connected components by taking the simplified block tree model as a root node to form block point sets, and judging that the root node is a key node when the number of the block point sets to which the root node belongs is greater than 1; and constructing an enhanced edge candidate set which is arranged according to a cost ascending order by the determined key nodes, selecting the edge with the minimum cost to communicate different blocks, sending a moving instruction to controlled nodes in k hops to establish a communication link, and finally enabling the network topology to meet vertex 2-communication. According to the method, the key nodes in the network are detected, and the key nodes are eliminated in a node moving mode, so that the stable and reliable unmanned aerial vehicle cluster network topology is constructed at low cost.

The invention provides a molecular property prediction method and system, computer equipment and a storage medium, and belongs to the field of compound intelligent analysis, and the method comprises the steps: obtaining an SMILES character string of a to-be-recognized molecule; converting the character string to obtain a molecular object, extracting a fingerprint vector from the molecular object, and performing linear transformation processing to obtain chemical substructure features; constructing a two-dimensional molecular map based on the molecular object, and extracting features of the two-dimensional molecular map to obtain local topological structure features; rendering the molecular object to generate a molecular image and extracting image features; performing feature splicing on the chemical substructure features, the local topological structure features and the image features to obtain fusion features; mapping the fusion feature into an output space to determine a predicted value, and generating a molecular property prediction result of the to-be-identified molecule based on the predicted value. According to the method, complementation among different information types can be realized, more information of molecules can be explored from different angles, and the accuracy and the universality of predicting molecular properties are enhanced.