226 results about "Breadth-first search" patented technology

The invention aims to provide an intelligent diagnosis method and system for a temporarily-built photovoltaic fault based on a knowledge graph, and belongs to the technical field of temporarily-built photovoltaic fault diagnos.The method comprises the steps that firstly, current, voltage, temperature and irradiance data of a temporarily-built photovoltaic system are collected, and multi-dimensional feature parameters are generated through abnormal value elimination, noise filtering and feature fusion; secondly, constructing a temporarily built photovoltaic knowledge graph containing equipment entity nodes, fault mode nodes and a topological connection relation, and calculating a topological connection weight based on historical fault data and an equipment rated power difference value; then, the multi-dimensional characteristic parameters are matched with fault mode nodes of the knowledge graph, fault propagation path reasoning is carried out through bidirectional breadth-first search and path weight screening, and fault types and positioning information are output; and finally, dynamically updating the association weight of the knowledge graph according to operation and maintenance feedback. The rapid and accurate diagnosis of the fault is realized, the downtime is obviously reduced, and the operation efficiency and the economic benefit of the temporarily built photovoltaic system are improved.

An intention-driven low-orbit satellite network SRv6 routing management and control method comprises the following steps: step 1, receiving and analyzing a user service demand from an application layer, and generating a corresponding intention label according to a service type; step 2, collecting state information of a satellite network in real time, tracking and recording satellite node position change and link state conversion, and establishing a complete network state database; 3, calculating a routing path meeting the service quality requirement by adopting an improved breadth-first search algorithm; 4, monitoring the state change of the inter-satellite link in real time; step 5, continuously monitoring fault events in the network, immediately starting a pre-calculated standby path when a fault is detected, updating a corresponding SRv6SID list, and performing rapid path switching; and step 6, dynamically adjusting the network flow. The method not only can adapt to the dynamic characteristics of the low earth orbit satellite network, but also can effectively meet the differentiated service requirements, and has the rapid fault recovery and load balancing capability.

The invention discloses a knowledge graph reasoning method based on dynamic rule perception memory. The method aims at solving the problems that an existing neural combination rule learning method is insufficient in expression ability and prone to splitting global semantics and local relation modes. The method comprises the following steps: introducing a lightweight dynamic relationship memory module, and executing self-attention on all relationships in a knowledge graph to capture global semantics; meanwhile, local relation interaction features are extracted through convolution, semantic fusion is carried out through a Transform encoder with multi-head attention and relative position coding, and unified and parallel-computing high-quality combination representation is constructed for each pair of relations in parallel. And meanwhile, a closed high-quality reasoning path is generated in combination with bidirectional breadth-first search. Through global semantic and local interactive collaborative modeling, the extendibility is ensured, the understanding of global semantics is enhanced, and the reasoning accuracy is improved. Global and local relation dependence is fused, an interpretable reasoning path is generated, and reasoning accuracy, expandability and robustness are improved.

The invention discloses a multi-source tight coupling SLAM method based on global information in a dynamic scene, and belongs to the field of mobile robots. According to the method, efficient positioning and mapping are realized through multi-source sensor data fusion and algorithm optimization. The method comprises the following steps of: firstly, eliminating invalid point cloud and data distortion by combining a binocular camera, a single-line laser radar, an IMU (Inertial Measurement Unit) and odometer data and utilizing point cloud filtering, dynamic target detection and motion distortion correction technologies, and fusing observation data of the camera and the laser radar; secondly, IMU and odometer data are fused through unscented Kalman filtering (UKF), and high-precision initial pose estimation is output; and finally, improving a Gmapping algorithm, optimizing a grid map updating module by adopting a subgraph fusion and breadth-first search strategy, and enhancing the environmental feature retention capability of the SLAM module. In a dynamic and complex scene, the method has better robustness, the constructed map better conforms to a real environment, and meanwhile, the pseudo obstacle characteristics in the map can be effectively reduced.

The invention discloses a power distribution network photovoltaic and energy storage configuration method considering island division and network reconstruction. The method comprises the following steps: collecting basic information of a power distribution network; monitoring output change and load fluctuation of a photovoltaic power supply in real time, constructing a photovoltaic power supply output model and a load time-varying model, and predicting photovoltaic output data and load demand data in the fault duration; determining an island range by using a breadth-first search algorithm by taking a load node directly supplied with power by the photovoltaic power supply as a starting point; based on an island division result, identifying residual load nodes of which the power supply is not recovered and network lines connected with the load nodes; the lowest comprehensive cost is determined as an optimization target, an electrical constraint condition is converted into a second-order cone constraint form, the optimization target and the electrical constraint condition are used for solving a network reconstruction problem for the fault duration, and a switch operation strategy is determined; power supply of the fault area is recovered; and performing configuration optimization on the photovoltaic power supply according to the load operation state in the island during the fault period of the power distribution network.

The embodiment of the invention provides a water supply network pollution tracing and positioning method and device based on a graph theory. The method comprises the following steps: S1, abstracting a topological structure of the water supply network based on a hydraulic model of the water supply network, constructing an adjacent matrix of the water supply network based on the topological structure, and determining an initial dynamic detection point candidate range by combining the adjacent matrix of the water supply network and utilizing a breadth-first search algorithm in a graph theory; s2, according to the dynamic detection point candidate range, dynamic detection point candidate schemes are generated, the information gain of each dynamic detection point candidate scheme is evaluated, and a dynamic detection point most beneficial to pollution source positioning is selected based on the information gain; s3, repeatedly executing the step S2 to obtain water quality pollution information, and continuously narrowing the candidate range of the dynamic detection points; and S4, when the calculated node pollution source result meets the positioning success condition or the dynamic detection point candidate range is empty, determining the position of the pollution source. In this way, the position of the pollution source can be quickly positioned.

The invention discloses a graph theory-based industrial internet identification system full-process tracing method, which realizes full-process tracing in a supply chain by introducing identifier fields into metadata and constructing a directed graph structure. The identifier fields include fields with similar representations for representing associations of the product upstream and downstream of the supply chain. The system queries across enterprises through a graph traversal algorithm (such as depth-first search (DFS) or breadth-first search (BFS)), and tracks the full life cycle of a product. The method supports attribute pointing code management, is used for tracing multi-attribute information (such as allocation weight, time and the like) of a product, solves cross-enterprise and cross-system coding differences through an alias mechanism, and simplifies a tracing process. The method has a flexible traceability path setting capability, can monitor the product circulation process in real time, gives out an early warning when an abnormality is found, and improves the transparency and safety of a supply chain.

The invention discloses a power distribution network topology verification method based on a breadth-first search algorithm. The method comprises the following steps: step S101, constructing a topology model according to actual data of a power distribution network; step S102, using a breadth-first search algorithm to search from a starting node of the power distribution network; s103, in the breadth-first search process, the system can detect isolated nodes, loops and open circuits at the same time; and S104, after basic topology verification is completed, the system further detects whether redundant redundant connection exists in the power distribution network, and the invention further provides a verification system using the method. According to the method, multiple branches can be detected at the same time, once a certain branch goes wrong, traversal can be continued through other branches, the fault-tolerant capability of the verification process is improved, and the method is easy to implement, has good expansibility, is suitable for power distribution networks of different scales, can be combined with optimization algorithms such as artificial intelligence and the like, and has good application prospects. And the intelligent level of verification is further improved.

The invention discloses an improved robot motion planning method based on Jump Point Search (JPS), and relates to a robot motion planning method based on JPS. According to the method, the search direction and the path planning process of the JPS algorithm are optimized, the path direction guidance of BFS (breadth-First Search) is combined, and the search direction priorities are sorted, so that the overall search efficiency and the path security are remarkably improved. Specifically, according to the method, a cosine value is used for measuring the similarity degree of each search direction and a BFS path, and the directions are divided into high priorities and secondary priorities. In the hop point searching process, the heuristic function value only evaluates the extended hop points in the high priority direction, and selects the hop point corresponding to the minimum value, so that the extension in the redundant direction is reduced, and the searching efficiency is improved. In addition, the invention also designs a set of path optimization strategy which mainly comprises two parts, namely a'security node updating strategy 'and a'redundant node eliminating strategy'. Aiming at the dangerous boundary path points, a security node updating strategy is utilized, and adjacent nodes are replaced by common security neighbor points, so that paths with potential safety hazards are prevented from being formed; according to the redundant node elimination strategy, the number of control nodes is reduced and the overall path length is shortened by detecting the direct connectivity between continuous nodes.

The invention relates to a modeling and formalized verification method for cache consistency protocol verification, and the method comprises the steps: building a protocol model which comprises a plurality of processor nodes, directory nodes and a communication mechanism through a structured modeling mode, and constructing an asynchronous message mechanism to simulate disordered concurrent communication; protocol behavior rules such as processor requests, directory responses and network receiving are defined, protocol property invariants are set, semantic constraints such as data consistency, confirmation before writing and sharing state legality are covered, model verification is carried out in a state space traversal mode, breadth-first search and a symmetry recognition mechanism are supported, and the method is suitable for the protocol behavior rules. A protocol error or deadlock state is effectively found, a traceable error path is generated, a verifier can automatically generate source codes through a compiler and operate on a host platform, and rapid verification and result output of a protocol model are achieved. The method has good protocol adaptability and model reusability, and is suitable for formalized verification of various cache consistency protocols.

The invention relates to a distribution network topology analysis method and system, and the method comprises the steps: carrying out the metadata analysis of a distribution line SVG file, and constructing an undirected graph, a subgraph and a connected subgraph; traversing the sub-graph by using a breadth-first search algorithm with a specified root node as a starting point, and marking a hierarchy for each node according to the distance from the root node to the current node; carrying out edge pruning based on the marked hierarchy, and converting the undirected graph into a directed graph according to a hierarchical relationship; on this basis, distribution transformer-switch relation analysis is carried out to form a basic information table of a switch to which a distribution transformer belongs, and connection line analysis is carried out to obtain a simple line connection path. According to the method, it is ensured that the sub-graphs conform to the radial structure of the power distribution network through hierarchical pruning, connection of the same hierarchy is removed, formation of an annular structure is prevented, detailed line connection paths and simple line connection paths are formed, and contact points between lines can be checked quickly and conveniently.

The invention provides a belt tearing detection method and device based on three-dimensional vision. The method comprises the steps of calculating a spatial distance value between each target point and an adjacent point in three-dimensional point cloud data in a direction perpendicular to a tangential direction of a laser arc, screening points of which the spatial distance values exceed a preset distance threshold value, forming an initial candidate point set, starting from each point, expanding a space through a breadth-first search algorithm, and obtaining a target candidate point set, calculating the length and depth of each connected domain; and taking the connected domain meeting the threshold condition as a corresponding potential crack, forming a potential crack set by the spatial position information of all the potential cracks, and generating a belt tearing detection result containing the spatial position information of the real crack and the corresponding alarm level. According to the technical scheme provided by the invention, high-precision dynamic identification and alarm of the surface crack of the belt are realized, the false detection rate is effectively reduced, and the industrial safety monitoring efficiency is improved.

The invention discloses an automatic coupling method and device for a photoelectric detector wafer, and the method comprises the steps: employing a visual recognition technology to achieve the coarse positioning of the coupling of a to-be-detected wafer chip, taking an optical power count value collected in real time as a search signal, carrying out the real-time analysis of the signal through combining with a breadth-first search algorithm, and carrying out the detection of the signal. Path planning is carried out on a three-dimensional space to determine a search path, precise control of a motion path is carried out on the three-axis precision platform through a software control system, finally, optical power value signals under each path position are traversed to find out a maximum value signal, the maximum value signal is moved to the position, and the maximum value signal is the optimal coupling position. Therefore, automatic coupling of wafers in a limited space is realized. The wafer coupling device has the beneficial effects that the automation degree of wafer coupling can be improved, the manual operation cost and the time cost are reduced, meanwhile, the precision and the stability of wafer coupling are improved, and adverse effects caused by manual operation errors are reduced.

The invention discloses a key path and node identification system and method, and relates to the technical field of cloud services, the system comprises a data processing module, a graph structure module and an identification module; the data processing module is used for preprocessing API data of the cloud service to obtain standardized data; the graph structure module is used for performing relation mapping on the system topology structure of the cloud service according to the standardized data to obtain a graph structure; and the identification module is used for identifying a core key path of the graph structure through a preset search algorithm, and exploring through a depth-first search strategy and a breadth-first search strategy to obtain a key path and a node. According to the application, the API data of the cloud service is converted into the graph structure, and the graph structure is explored by the preset search algorithm, the depth-first search strategy and the breadth-first search strategy in the identification module, so that key paths and nodes of the cloud service can be efficiently and accurately identified.

The invention discloses a dynamic knowledge fusion image question-answering method based on path correlation, which comprises the following steps of: S1, carrying out relationship type classification on an external knowledge base, and reserving a corresponding relationship type according to the characteristics of an image question-answering task to obtain a lightweight knowledge graph; s2, respectively extracting entities from the image and the question text, and mapping the entities to the lightweight knowledge graph nodes in the S1 to obtain a seed node set; s3, starting from the seed node set in the S2, generating candidate knowledge sub-graphs through breadth-first search; s4, performing correlation scoring on knowledge paths in the knowledge sub-graph in the S3 according to the text matching degree and the visual consistency; s5, iteratively optimizing the knowledge sub-graph through a greedy pruning strategy according to the knowledge path score in the S4; and S6, calculating a knowledge weight coefficient, realizing dynamic fusion of external knowledge, adaptively adjusting a knowledge embedding degree according to a question text, and inputting an image question and answer model to generate a final answer.

The invention relates to the technical field of network security, in particular to a cloud platform vulnerability real-time quantitative analysis system based on a dynamic knowledge graph. Comprising a multi-source heterogeneous data acquisition unit; a dynamic knowledge graph construction unit; the vulnerability quantitative evaluation unit is used for identifying possible propagation paths of vulnerabilities through an improved weighted breadth-first search algorithm and introducing a real-time relation weight and path attenuation mechanism; an intelligent response processing unit; and a visual verification unit. According to the invention, a dynamic relation edge weight is generated by fusing real-time operation parameters of a cloud platform through a real-time weight calculation module, then the weight of an association relation without vulnerability propagation for a long time is adjusted in combination with a path attenuation processing module, and a vulnerability propagation path is identified in cooperation with a weighted breadth-first search algorithm. The accuracy of vulnerability propagation path identification and the accuracy of risk level quantification can be improved, and the problem that traditional risk assessment depends on static parameters and is easily interfered by invalid paths is effectively solved.

The invention is applicable to the technical field of 3D printing, and provides a 3D printing adaptive support algorithm, which comprises the following steps of: performing vertex clustering simplification and Laplacian smooth denoising on an OFF format three-dimensional model, reducing the number of vertexes and patches by utilizing space division and centroid substitution, and calculating and optimizing surface quality in combination with an iterative mean value; recognizing a suspension surface and clustering adjacent surfaces through normal vector analysis and breadth-first search, and performing three-dimensional mapping and duplicate removal processing after generating supporting points; selecting an optimal supporting point pair by adopting a greedy algorithm, calculating lower-layer intersection points to form a tree-shaped skeleton, and avoiding collision by combining an AABB tree and a ray detection technology; a supporting cone, a cylinder and a base are generated, and a complete supporting structure is formed. According to the algorithm, the stability and the printing efficiency of the supporting structure are remarkably improved through multi-stage optimization, and an innovative solution is provided for efficient 3D printing of a complex model.

The invention discloses a graph theory analysis-based power grid project supplier equity relationship intelligent identification method. The method comprises the following steps of S1, collecting supplier relationship data; s2, stock equity structure features and transaction features of suppliers in the standardized data set are extracted; s3, constructing a supplier equity relation graph, traversing the supplier equity relation graph by adopting a depth-first search algorithm and a breadth-first search algorithm, and calculating a control path length and an equity control weight; s4, optimizing the stock right control relation identification of the suppliers by using an improved Grapporter network, and predicting the stock right control relation strength between the suppliers; s5, calculating the risk scores of the suppliers, setting a threshold value according to the risk scores, and classifying the risk levels of the suppliers; and S6, an incremental learning method is adopted to optimize the Grapher network. According to the method, graph theory analysis and Grapher network improvement are combined, the supplier stock equity control relation is intelligently identified, the risk is dynamically evaluated, and the method has the advantages of being accurate in identification, high in adaptability and high in real-time performance.

The invention discloses a method for identifying and sensitizing a digital path in a digital-analog hybrid circuit, which comprises the following steps of: firstly, establishing a directed dependency graph of the digital-analog hybrid circuit, dividing all power supplies in the circuit into digital power supplies and analog power supplies, and enabling the voltage values of the digital power supplies to be high or low and the voltage values of the analog power supplies to be default values to obtain value modes of all the power supplies; and performing digital path identification sensitization based on breadth-first search in each power supply value mode to obtain an identification sensitization result, and only considering circuit elements with digital characteristics in the process until node voltage high-low state updating and MOS tube state judgment on all digital paths are completed. Based on the breadth-first search strategy, the transistor-level digital path in the digital-analog hybrid circuit is identified and sensitized in a targeted manner, so that an accurate digital path identification and sensitization result is obtained.

The invention relates to the field of power distribution of power systems, in particular to a power distribution network digital deduction method and system based on big data. The method comprises the following steps: step 1, carrying out topology verification on a power distribution network based on a breadth-first search algorithm; step 2, completing unknown measurement information of the power distribution network based on data flow mapping; and step 3, deducing the state of the power distribution network impacted by the distributed power supply and the multi-element load. A deduction system used by the method comprises a data acquisition module, a data storage module, a calculation module, a display module and a power supply. Wherein the calculation module receives data of the data acquisition module, processes the data, stores the data in the data storage module, transmits a result to the display module, and meanwhile, depends on the power supply module to provide power support. The data acquisition module is used for acquiring operation data of the power distribution network in real time, including parameters of voltage, current, power, frequency and phase angle, and transmitting the data to the data storage module through a sensor and a communication network.

The invention discloses a directional sensor network communication method considering hop count and distance constraints, which comprises the following steps of: traversing each node serving as a main node to establish a network topology, and traversing the network layer by layer by adopting a breadth-first search algorithm and considering the distance and hop count constraints; and screening potential sub-nodes of the current node by using a sub-node selection strategy algorithm, wherein screening conditions comprise communication distances between the nodes, azimuth angles and sub-node number limitation. Through a sliding window mechanism, an angle range which contains the most child nodes and has the shortest communication distance is selected, and the antenna azimuth angle of the main node is calculated, so that the connection efficiency and the energy consumption are optimized. After a plurality of possible network topologies are generated, the optimal connectivity and energy efficiency of the network are realized by evaluating the connectivity rate, the hop count and the communication distance of the network. Compared with the prior art, on the premise that the connectivity requirement is met, the communication energy consumption is remarkably reduced, and the network efficiency is improved.

The invention discloses a method and device for counting microorganisms by using a combined image, and relates to the technical field of microorganism counting, and the method comprises the steps: extracting core feature points of a preprocessed microscopic image by using a convolutional neural network model, calculating the similarity between the core feature points, and obtaining a microorganism microscopic image containing a splicing position. And splicing the microbiological microscopic images by using a self-adaptive splicing algorithm, identifying and correcting appearing errors, and obtaining a spliced panoramic image. And inputting the spliced panoramic image into a graph cut algorithm, constructing an energy function of the panoramic image, finding out a segmentation path with the lowest energy by using a minimum cut algorithm, and generating a panoramic image of the microbial region. According to the method, the matching precision between adjacent images and the stability of geometric transformation are greatly improved, the segmentation precision of image segmentation is improved by utilizing the breadth-first search algorithm and the minimum cut algorithm, and the conditions of image dislocation and inconsistent overlapping are avoided.

The embodiment of the invention provides a NoC adaptive routing method and device based on a deep neural network and a storage medium, and the method comprises the steps: obtaining an atlas of the NoC network, and a source node and a target node of a data packet; based on a breadth-first search algorithm, according to the atlas of the NoC network, the source node and the target node, determining a shortest path; outputting an actual transmission path based on a deep neural network model according to the NoC network and the shortest path; and adding the actual transmission path to a preset global routing table. Therefore, the transmission efficiency of the data packet is improved, and load balancing is realized.

The invention discloses a layout parameterization hierarchical parallel mesh generation method based on Net. The method comprises the following steps: inputting a layout design file, analyzing hierarchical structure identification ports, generating a port set P and creating an accessed port set V (initial V =); a graph G = (VG, EG) is constructed, VG comprises key vertexes or geometric centers of ports and conductor fragments, an electrically independent Net is generated through traversal of a breadth-first search algorithm or a depth-first search algorithm, and iteration is performed until V is completely equal to P; configuring independent subdivision parameters such as a grid size and a grid algorithm for each Net; net is used as an independent task, grids are divided through parallel computing frameworks such as multithreading, and a complete grid file for subsequent simulation is generated after merging. According to the method, Net automatic identification is realized through graph theory analysis; different Net precision requirements are adapted by differential subdivision parameters, so that waste of computing resources is avoided; through parallel processing, the time consumption of layout grid division is shortened to the time consumption of processing the maximum Net, and the efficiency and the flexibility of simulation pre-processing are remarkably improved.

The invention discloses a question and answer method and system based on large model causal graph discovery and causal graph enhanced reasoning, and belongs to the technical field of natural language processing and artificial intelligence. The method comprises the following steps: firstly, carrying out hierarchical expansion from an initial root node by adopting a breadth-first search strategy, and efficiently constructing a reliable causal graph conforming to directed acyclic graph constraints with linear complexity in combination with real-time loop detection; in the question and answer stage, key entities in user questions are analyzed, and causal paths connecting the key entities are retrieved in a causal graph; and taking the retrieved structured causal path as a constraint condition to be injected into a decoding process of a large-scale language model, and generating a natural language answer which is strict in logic and can trace reasoning steps. According to the method, the resource consumption of large-scale causal discovery is remarkably reduced, the accuracy and interpretability of the answers of the questions and answers are effectively improved, and technical support is provided for medical treatment, finance and other scenes needing high-reliability reasoning.

The invention relates to the technical field of data analysis, in particular to a data and knowledge combined driven intelligent decision-making method and system.The method includes the steps that multi-source heterogeneous data and knowledge base concepts are unified into a structured graph structure through multi-dimensional feature extraction and multi-head attention fusion context prompt, and a structured semantic basis is provided for intelligent decision-making; by combining data-driven mining and knowledge prior constraints, the accuracy and robustness of causal reasoning are improved, and the reliability of root cause diagnosis is ensured; a root cause path is searched and enumerated in a reverse breadth-first manner by taking an abnormal entity as a starting point, so that rapid and traceable abnormality positioning is realized, and a user is assisted to focus a high-probability root cause; through modular assembly and full-link element traceability, while the analysis threshold is reduced, decision full-link transparency is realized, and the auditing requirement of a high-risk scene is met; by automatically converting natural language requirements into executable scripts, the decision-making efficiency of non-technical users is improved.

The invention discloses a digital power grid network security protection efficiency quantitative evaluation method, system, equipment and medium, and belongs to the technical field of digital power grid network security, and the method comprises the steps: constructing a digital power grid network threat situation deduction graph, and constructing a digital power grid network threat situation deduction graph through a reverse construction algorithm facing key equipment and a forward construction algorithm facing whole network topology; acquiring a complete threat path by adopting depth-first and breadth-first search strategies respectively; based on the threat situation deduction graph, establishing a network real-time threat event stack through a threat event real-time analysis algorithm, performing threat event matching and time-space correlation analysis, and constructing a real-time threat situation deduction graph; a multi-level quantitative evaluation system is established, and evaluation indexes of a threat event, a threat path, a threat target and a whole-network threat situation are gradually and recursively calculated by calculating four dimensions of an asset value degree, a threat event influence degree, a successful support rate and a concealment degree. The problem that digital power grid network security protection efficiency is difficult to quantitatively evaluate is solved.

The invention discloses a machine learning data security processing and remote proof model protection system and method, and belongs to the field of computer program analysis technology, information security and data service, and the method comprises the steps: combining a Pearson correlation coefficient, an FP-Growth algorithm and cosine similarity to act on discrete data to fill missing values, carrying out the minority group oversampling based on SMOTE, and carrying out the data security processing and remote proof model protection based on SMOTE. And a measurement engine is constructed, and instrumentation is carried out in machine learning model program codes to complete measurement of the modeling process and the modeling result integrity. According to the data security processing and remote proof model protection system and method based on machine learning, a control flow diagram of a static model program code is traversed in a breadth-first search mode to calculate a credible execution process metric value of each sub-process; and comparing each measurement value with the credibility measurement value to verify the measurement result so as to ensure that the code and the result are not tampered in the modeling process.

The invention discloses a power grid fragile link identification method and device based on network flow betweenness and a medium, and relates to the technical field of power system safety. According to the technical key points, an actual power transmission path is obtained through a power flow tracking algorithm and a breadth-first search algorithm; calculating a power transmission distribution factor of each power transmission path on the generator load node pair and a power transmission maximum capacity of each power transmission path on the generator load node pair; and calculating the network flow betweenness of each power transmission path according to the power transmission distribution factor and the power transmission maximum capacity, and carrying out vulnerability sorting on the lines and the nodes according to the network flow betweenness. According to the identification method, more comprehensive system state and topological information are considered, and the identification result is more accurate.

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to an enhanced retrieval generation method supporting dynamic knowledge clipping, which is based on a retrieval enhanced generation framework optimized by a knowledge graph and comprises the following steps: firstly, extracting (h, r, t) triples from an unstructured knowledge text through a design template and cleaning and storing the triples; then, the constructed knowledge graph is optimized; further calculating semantic similarity between user query and knowledge graph nodes by using an embedded model, preliminarily screening out related nodes, specifically, carrying out hybrid similarity candidate screening and dynamic candidate set generation, then constructing a multi-hop associated sub-graph by using breadth-first search, cutting redundant edges by using a maximum spanning tree, retaining high-score nodes, and finally constructing a multi-hop associated sub-graph by using breadth-first search; realizing dynamic context truncation; and finally, combining the retrieved and pruned context information with the user question, promoting the LLM to generate an answer for the user question by constructing a cue word, continuously optimizing the LLM, and generating a more accurate and deeper answer.