86 results about "Breadth-first search" patented technology

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 present application relates to the technical field of network security, and more particularly to a cloud platform vulnerability real-time quantitative analysis system based on a dynamic knowledge graph, which comprises a multi-source heterogeneous data acquisition unit, a dynamic knowledge graph construction unit, a vulnerability quantitative evaluation unit, an intelligent response disposal unit and a visual verification unit.The vulnerability quantitative evaluation unit identifies the possible propagation path of the vulnerability through an improved weighted breadth-first search algorithm, and introduces a real-time relationship weight and a path attenuation mechanism.The present application can improve the accuracy of vulnerability propagation path identification and the accuracy of risk level quantification by generating dynamic relationship edge weights through a real-time weight calculation module, combining a path attenuation processing module to adjust the weight of long-term non-vulnerability propagation correlation, and cooperating with the weighted breadth-first search algorithm to identify the vulnerability propagation path, thereby effectively solving the problem that traditional risk assessment relies on static parameters and is easily disturbed by invalid paths.

The invention discloses a geographic national condition comprehensive index monitoring method, and relates to the technical field of geographic national condition comprehensive indexes. The method comprises the following steps: collecting a geographic remote sensing image map, and segmenting the geographic remote sensing image map through a MaskR-CNN model instance to obtain a geographic semantic entity; reasoning through a causal discovery method based on an evolutionary algorithm to obtain a first causal intensity matrix; calculating a spherical distance between geographic semantic entities to obtain an entity distance matrix, generating an entity comprehensive influence degree matrix in combination with the first causal intensity matrix, and further constructing a geographic knowledge graph; inputting the geographic knowledge graph into the adversarial graph attention network model, and outputting a second causal intensity matrix; and based on the second causal intensity matrix, reversely traversing the geographic knowledge graph by using a breadth-first search algorithm to extract a change causal chain, calculating a feature contribution ratio of each geographic semantic entity in the chain, obtaining a geographic national condition comprehensive index, and realizing monitoring of the geographic national condition comprehensive index.

The invention provides a power distribution network power supply recovery method considering node importance and recovery dependence. The method comprises the following steps: 1) obtaining fault information of a power distribution network in real time; 2) comprehensively evaluating the importance weight of each node by adopting a comprehensive weight method combining an analytic hierarchy process and an entropy weight method; 3) using a reverse breadth-first search algorithm from the fault points to identify a repair dependency relationship among the fault points, and determining an upstream fault set which needs to be repaired firstly when any fault point is repaired; 4) quantifying the reduction amount of the ice melting device deployed before the disaster on the fault recovery time, and correcting the fault recovery time of each fault point in the fault set; 5) constructing a repair sequence optimization model with the goal of minimizing the total weighted load loss amount; 6) solving the repair sequence optimization model by the scheduling control center by adopting an improved genetic algorithm, and generating an optimal sequence for fault repair; and 7) dynamically adjusting the optimal sequence of fault repair.

The invention belongs to the field of tourism management and artificial intelligence, and provides a personalized tourism route planning method based on a knowledge graph, which comprises the following steps: constructing a scenic spot knowledge graph; calculating the mastering degree of the user on the scenic spot; based on the mastery degree of the user on the scenic spot, endowing mastery degree attributes to nodes of the scenic spot knowledge graph; performing breadth-first search on the to-be-recommended nodes from each first node of which the mastering degree is greater than a first preset value to form a second scenic spot knowledge graph; determining a current position of a user on the second scenic spot knowledge graph, and performing breadth-first search on a to-be-recommended node based on the current position of the user to obtain a first to-be-recommended node; and searching the to-be-recommended nodes from the first to-be-recommended node, and determining a plurality of to-be-recommended nodes based on user plan time and cost constraints to form a recommended route.

The invention discloses an enterprise family contract association and risk monitoring method and system based on a graph database, and the method comprises the steps: taking an enterprise as a node, taking an equity relationship as a side, constructing and maintaining an enterprise family graph, carrying out the recursion of the enterprise family graph through employing a bidirectional breadth-first search strategy, and obtaining shareholder and external investment information, setting a crawling termination condition according to the stock holding proportion, the registration capital and the enterprise state; mapping the identified contract main body to a corresponding enterprise node in an enterprise family map to form an association relationship between the contract and the enterprise node; multi-dimensional risk signals are collected, and forward or reverse conduction of the risk signals is carried out in the enterprise family map along the equity relation path based on a risk conduction rule; according to the transmitted risk assessment result, updating a risk state tag of the associated contract; and triggering an early warning instruction or contract state locking based on the risk early warning triggering rule. According to the method, the reliable association between the contract and the related enterprise main body is efficiently established, and the contract-level risk is perceived in time.

The invention relates to an open-pit mine area online mapping and boundary line fitting method based on sparse perception, belongs to the technical field of open-pit mine area automatic driving, and solves the problems that in the prior art, a step-by-step processing mode is used in an open-pit mine area environment perception method, information transmission loss exists, and environment perception is incomplete. Extracting a multi-scale feature map; s2, decoding the multi-scale feature map to obtain a dynamic instance and a static instance; s3, constructing a local vector map, and dynamically updating the local vector map; s4, key points of road boundaries are extracted from the local vector map, and matching point pairs meeting matching conditions are obtained; s5, carrying out breadth-first search on the matching point pairs to obtain an initial road boundary sequence; s6, screening the obtained initial road boundary sequence to obtain a screened road boundary sequence; and S7, re-projecting the screened road boundary sequence back to the original image to obtain a fitting road boundary.

The application discloses a three-dimensional human skeleton fitting method and system based on a multi-camera joint association graph and combined search, and relates to the three-dimensional human skeleton fitting method, which comprises the following steps: S1, acquiring images collected by multiple cameras, detecting human two-dimensional joint nodes by using a two-dimensional pose estimation algorithm, and projecting the two-dimensional joint nodes into three-dimensional rays; S2, taking the joint nodes as nodes and the geometric consistency between three-dimensional rays of different cameras as edge weights, constructing a multi-camera joint node association graph; S3, performing group clustering on the cross-camera joint nodes with high geometric consistency on the association graph by using a breadth-first search, and forming a candidate skeleton cluster; S4, reserving the first K joint nodes with the highest geometric consistency with the candidate skeleton cluster in each camera, and obtaining candidate joint node pairs; S5, using a depth-first search (DFS) to enumerate the cross-camera joint node pairs, and screening optimal joint node pairs in combination with a pruning strategy; and S6, performing triangulation processing on the screened optimal joint node pairs, obtaining three-dimensional skeleton points, and outputting a three-dimensional human skeleton structure.

The application provides a symbol distance function value determination method, an electronic device and a storage medium, relates to the field of data processing, and can conveniently and quickly obtain the SDF value of a space point in a three-dimensional object. The method is applied to an electronic device, and the method comprises the following steps: generating a three-dimensional grid for representing the surface of a target object based on a shooting image and a depth image of the target object; interpolating a sub-grid in the three-dimensional grid to obtain interpolation points on the sub-grid; determining the vertices of the sub-grid and the interpolation points on the sub-grid as the center points of the voxels of a layer of voxels that are the outermost layer of the voxels constituting the target object, and determining the SDF values of the voxels as 0; taking the vertices of the sub-grid and the interpolation points on the sub-grid as starting points, taking the edge length of the voxels as a search step, taking the vertical direction of the sub-grid towards the inside of the three-dimensional grid as a search direction, and adopting a breadth-first search algorithm to determine all the voxels in the target object and the SDF values of all the voxels.

This invention discloses a method and system for coal mine safety risk assessment based on causal subgraph enhancement and LLM dynamic completion. The method includes: 1. Acquiring textual data in the field of coal mine safety and constructing a knowledge graph; 2. Extracting entities based on user queries and selecting initial triples in the knowledge graph through semantic relevance scores; 3. Expanding reachable triples using breadth-first search to construct an initial causal subgraph; 4. Performing causal reasoning and dynamic completion on the subgraph using a Large Language Model (LLM) to generate an enhanced complete causal subgraph; 5. Finally, generating an interpretable coal mine safety risk assessment report using LLM. This invention aims to solve the problems of missing causal logic and knowledge illusion in existing coal mine risk assessments by improving the accuracy of risk assessment through causal subgraph enhancement technology.

The invention provides a binary program static analysis method based on a comprehensive control flow diagram. According to the scheme, the method comprises preprocessing, a control flow diagram generation module, a reverse control flow diagram generation module, target address ambiguity instruction recognition and a specific basic block path. The control flow diagram generation module obtains binary program information through preprocessing and generates a control flow diagram by using a breadth-first search algorithm. The reverse control flow diagram generation module initializes a reverse control flow diagram and generates a reverse edge by traversing the control flow diagram. A target address ambiguity instruction analysis module identifies a target address ambiguity jump or call instruction in the basic block. The specific basic block path analysis module uses a path search algorithm to obtain all paths of a specific basic block, and analyzes execution conditions of the paths through a symbolic execution technology. According to the method, the binary program can be comprehensively analyzed, the analysis efficiency and accuracy are improved, and powerful support is provided for software security and quality improvement.

The invention discloses an intelligent event assessment method and device based on an artificial intelligence technology, and the method comprises the steps: carrying out the clustering analysis of an alarm-related address through a DBSCAN density clustering algorithm, and assessing the geographic space risk of the alarm-related address; recursively extracting an alarm-related family knowledge graph by using a BFS breadth-first search algorithm of a graph database, and analyzing the risk of an alarm-related family relationship network; performing text vector representation on the received information text and the processed information text by using a transform neural network, identifying similar event conditions based on the distance between text vectors, further identifying repeated event conditions and series-parallel case clues, and analyzing event condition association risks; by establishing a multi-dimensional scoring system, weighted summation is carried out on factors including geographic space risks, family relation network risks and event condition association risks, and a comprehensive score of event condition risk degrees is obtained. According to the method, an open source big data platform is used as a basic framework, artificial intelligence components such as machine learning, knowledge graph and natural language processing are integrated, a unified data service port is packaged, and risk assessment of historical event conditions and specific event conditions is achieved.

The invention discloses a positioning method and device in event change, electronic equipment and a storage medium, and relates to the technical field of big data or other related technical fields, and the method comprises the steps: determining an abnormal code file based on an abnormal event, and obtaining a historical change event set; calculating the file dependency of the code files, clustering the code files based on the file dependency, and constructing a dependency loss undirected graph based on a clustering result; based on the abnormal code file, searching in the dependency loss undirected graph by adopting a breadth-first search algorithm to obtain a coupling file list corresponding to the abnormal code file; and calculating the responsibility degree of each responsible object for the coupling file list based on the historical change information of each coupling file in the coupling file list, and configuring a target responsibility object for the abnormal event based on the responsibility degree. According to the method and the device, the technical problems of relatively low change accuracy and change efficiency in a mode of depending on manual event change processing in related technologies are solved.

The application discloses a roadside ground object multi-stage clustering segmentation algorithm based on vehicle-mounted LiDAR point cloud, which comprises the following steps: (1) vehicle-mounted LiDAR point cloud data preprocessing, to obtain roadside ground object point cloud data; (2) three-dimensional gridding and breadth-first search are sequentially performed on the roadside ground object point cloud data, to realize coarse clustering segmentation of the roadside ground object point cloud data; the coarse clustering segmentation obtains separate ground object point cloud and connected ground object point cloud, and the separate ground object point cloud is output as a clustering result of the roadside ground object; (3) the connected ground object point cloud is subjected to Euclidean clustering, to be segmented into coarse clustering point cloud sets with complete boundaries; (4) all clustering objects of the coarse clustering point cloud set are aggregated by using multi-section nearest neighbor search; and the clustering threshold value is adaptively adjusted until the point cloud aggregation speeds of all the clustering objects are similar, and the connected ground object clustering result is output. The application solves the problems of the vehicle-mounted LiDAR point cloud segmentation, such as too much manual intervention and unstable segmentation effect.

A breadth-first autonomous selection method for a hexagonal pyramid flywheel configuration includes: determining flywheel combinations that cannot be simultaneously connected to the control system; counting the number of faulty flywheels; counting the number of faulty flywheels initially connected to the control system; when the number of faulty flywheels is 2 or 3, directly selecting existing fault-free flywheels to connect to the control system; when the number of faulty flywheels is greater than 3, directly setting all flywheels not to be connected to the control system; when only 1 flywheel is faulty when initially connected to the control system, prioritizing traversing all available flywheels to obtain 4 flywheels containing the shortest path to all vertices, and using these as the initial value for connection to the control system, determining the 4 available flywheel connection methods through a breadth-first search algorithm; when flywheel combinations that cannot be simultaneously connected to the control system are found during the search process, continuing the breadth-first search algorithm to determine the final flywheel connection combination method, thus completing the breadth-first autonomous selection design of the hexagonal pyramid flywheel configuration.

This invention discloses a graph computing optimization method and system based on multi-centrality metrics and globally optimal ranking. The method first calculates the multi-centrality metric of vertices based on an industry knowledge graph, generating candidate ranking sequences adapted to different business operations, and then filters out efficient sequence subsets through performance testing. Subsequently, a parallel computing architecture and a business-region combined partitioning strategy are employed to perform a breadth-first search in parallel, starting from key nodes, generating locally optimal sequences. By incorporating sequence similarity calculations with industry chain weights, the filtered efficient sequence subsets are merged with the locally optimal sequences to form a globally optimal sequence. This effectively overcomes the limitations of traditional single ranking in adaptability across multiple business scenarios and significantly reduces computational complexity. The constructed globally optimal sequence demonstrates higher accuracy and adaptability in businesses such as supply chain risk identification and precise investment attraction, providing an efficient and stable graph computing solution for industrial cloud platforms in the equipment manufacturing field.

The invention discloses a mine transportation scheduling method and system based on a hybrid genetic algorithm, and belongs to the technical field of intelligent optimization scheduling. The method comprises the following steps: S100, inputting mine tunnel network, facility and equipment parameters; s200, a Dijkstra algorithm and breadth-first search are combined, and an optimal path set containing a shortest path and a secondary short path is generated for each pair of ore unloading fields and mining sites so as to disperse traffic flows; s300, establishing a multi-objective optimization model for minimizing waste time and fuel consumption and maximizing transportation volume; s400, a non-dominated sorting genetic algorithm is adopted for solving, the core of the non-dominated sorting genetic algorithm is that a special triple coding mode is adopted, an execution vehicle is allocated for each transportation task, a going route is selected for each transportation task, a return route is selected for each transportation task, and complex constraints are fused into a chromosome structure; s500, performing dynamic simulation on each scheduling scheme, updating the vehicle state and speed based on the real-time road congestion degree, and accurately evaluating the performance of the scheme; s600, selecting a final scheme from the Pareto optimal solution set by using a multi-attribute decision-making method; and S700, when a fault occurs, automatically re-evaluating and allocating tasks. According to the method, the problems of single path, inaccurate model and uncomfortable coding of a traditional method are effectively solved, and high efficiency, energy conservation and high robustness of vehicle scheduling in a complex mine tunnel network are realized.

The invention discloses a pump station additionally-started pump set selection method, system and device considering electric constraint and a storage medium, and belongs to the technical field of water conservancy project automation control. According to the method, an innovative framework of'priority ranking + iterative electrical analysis verification 'is provided for solving the problems that an existing pump set starting strategy neglects frequency conversion bus conflict, transformer overload, operation economy and the like. Specifically, after a machine increasing instruction is received, according to a priority primary selection pump set and frequency converter combination, an algorithm based on graph theory depth / breadth-first search is adopted, and multiple electrical verifications such as frequency conversion bus conflict analysis, transformer overload analysis and electricity charge analysis are sequentially carried out. And if the verification is not passed, automatically iteratively selecting a next priority combination until a safe and economic optimal solution is found. According to the method, automation and intelligentization of pump set and frequency converter selection in the augmented scene are achieved, pump starting failure and equipment overload are avoided, the safety and economical efficiency of a pump station are remarkably improved, and the limitation of manual experience decision making is solved.

The application discloses a geometric embedding knowledge graph question answering method based on a large language model, first, aiming at the structural heterogeneity of knowledge graph chain, level and cycle, entity and relationship embedding is learned in Euclidean space, hyperbolic space (Poincare ball model) and spherical space (unit sphere) respectively, and the triple score of the three geometric spaces is optimized by combining negative sampling and maximum interval loss. Secondly, in the reasoning stage, the large language model analyzes the problem and identifies the key entity, constructs the relevant subgraph, and generates the candidate path by using the breadth-first search or beam search; the path node is projected in three ways and spliced to calculate the attention weight, the fusion node representation and edge level fusion score are obtained, and the path score is accumulated. Finally, the structured prompt is formed by selecting the Top-K evidence path and its weight, contribution value, the large model is constrained to generate the answer and explanation, the traceability and accuracy are improved, the consistency is enhanced, and the illusion is reduced.

The invention discloses a wiring diagram processing method and device, and belongs to the technical field of image processing. According to the method, a component model of equipment in a to-be-processed wiring diagram is generated in a standardized manner, subjective errors of manual construction are avoided, and accuracy of equipment parameters and connection relations is guaranteed; generating a device connection topological graph by using a depth-first search algorithm, accurately presenting a device hierarchical relationship and a connection path by using a systematic traversal characteristic of the algorithm, and improving analysis efficiency and accuracy; a loop model is determined on the basis of the equipment connection topological graph, the element model and preset loop type attribute information, and empirical judgment deviation is avoided; then generating a loop systematic model, and realizing integration and association of a single-loop model; and finally, generating a loop topological relation by using a breadth-first search algorithm to ensure that the data is complete and available. The method standardizes the process, improves the processing accuracy and efficiency, provides reliable data support for equipment management, system reliability evaluation and the like, and is high in practical value.

The invention relates to a research and development cost collection method, system and device based on an intelligent robot and a medium. According to the method, sensor equipment carried by an intelligent robot autonomously moves in a new material research and development environment, research and development activity data are collected in real time, and the data are uploaded to a research and development project management platform; constructing an attribute graph data model based on data received by the platform, taking a formula version, a sample entity and a test report as nodes, and taking a derivative relationship and a cost allocation weight as edges; backtracking the traversal model from a target sample node by adopting a reverse breadth-first search algorithm to obtain a traversal path and node association relationship, and calculating a cost allocation coefficient in combination with a cost allocation weight so as to accurately allocate the amortization fee and the depreciation fee to formula batch nodes; finally, the labor cost, the depreciation cost and the amortization cost are collected based on the distribution result, accurate collection of the research and development cost in a complex pedigree scene is achieved, the cost allocation error caused by a traditional linear structure is effectively avoided, and the collection accuracy and efficiency are improved.

This invention provides a method and system for optimizing mining methods in metal mines, relating to the field of mining method optimization technology. The method first constructs a candidate mining method library and an evaluation index system for mining method optimization; secondly, it sets multi-level quality grades for mining methods and establishes grading standards for the evaluation indicators; further, it constructs a relationship network of evaluation indicators and uses graph theory analysis and breadth-first search algorithms to achieve objective weighting of the evaluation indicators; it constructs a TOPSIS evaluation model to calculate the proximity of each mining scheme to its ideal optimal value; it constructs a cloud model to calculate the membership matrix of each mining method; finally, it obtains the final score through a joint probability function and selects the mining method with the highest score. This invention can reduce the influence of subjective human weighting on the evaluation results, improve the accuracy of the evaluation results, and enable the evaluation process to present more diverse information, providing a quantitative basis for scientific decision-making in mining methods.

This application relates to the fields of power distribution network technology and deep learning technology. It discloses a method, apparatus, and device for generating power distribution network topology diagrams based on temporally explicit networks. The method includes: a power distribution network automation system generating an adjacency list for the current time; dividing multiple nodes in the adjacency list into multiple regions, selecting the bus node of each region as the traversal start node for each region, creating a corresponding worker thread for each region, and performing a breadth-first search traversal operation on each region starting from the traversal start node to obtain a connection tree for each region; merging the connection trees of each region to generate the connection tree of the power distribution network; converting the connection tree of the power distribution network into a topology matrix of the power distribution network; inputting the topology matrix of the power distribution network into a drawing tool; and having the drawing tool generate the topology diagram of the power distribution network at the current time. This application is beneficial for improving the efficiency of topology diagram generation.

The embodiment of the invention provides a knowledge graph embedding model-oriented multi-level poison attack method, which comprises the following steps of: after an attacker obtains target data and an operation authority, selecting an entity as an attack target as required; hierarchical semantic extraction: based on different types of knowledge graph embedding models, extracting hierarchical information of the corresponding types of knowledge graph embedding models; constructing a multi-layer poisoning tree according to the hierarchical information of the target entity by using a breadth-first search algorithm; according to the selected target multi-tuple and the hierarchy where the target multi-tuple is located, an attacker designs poison data of different types; according to the constructed poisoning data, adding the poisoning data into a training data set, using the updated data training model to obtain a knowledge graph embedded model after poisoning, destroying the expression of a target entity in an embedded space, and influencing the reasoning and application effects of the knowledge graph; and attack effect evaluation: evaluating the attack effect by calculating the embedding performance change of the target entity.

The invention provides a charging module dynamic allocation method based on a graph model and improved breadth-first search, and the method comprises the following steps: S1, abstracting a power module in a charging system into a vertex set V in a directed graph, and respectively modeling the power module and a connection contactor into vertexes and edges in the directed graph, constructing a dynamic updatable graph model; triggering an event-driven scheduling process when the system state changes, adopting a multi-source step-by-step improved breadth-first search algorithm, combining charging gun activation control and a configurable priority strategy, and arbitrating and allocating a unique available power module round by round; meanwhile, abnormal conditions such as connection contactor adhesion, operation rejection and module faults are processed in real time, and a graph model is dynamically reconstructed to guarantee continuous operation of the system; and after basic requirements are met, an idle module is called based on a load rate threshold value, and the system energy efficiency is optimized. The method has the advantages of low calculation overhead, strong topology adaptability, flexible scheduling capability and high power utilization rate, and is suitable for a high-density intelligent charging scene.

The invention relates to the technical field of constructional engineering, and particularly discloses a reinforcing steel bar digital twinning full-process management system, which comprises the steps of quantifying reference model updating time through historical change data, and judging whether a systematic lag risk exists or not; secondly, a correlation modeling module splits the steel bar model into three-dimensional units, establishes a correlation model of the number of steel bars and lag time and determines the number of critical steel bars; thirdly, a topology construction module constructs a steel bar association topological graph containing composite weights, and a change propagation rule is defined; then, a change traversal module outputs a change influence set through weight priority breadth-first search traversal; and finally, a collision detection module allocates computing power based on unit risk and complexity, realizes local accurate detection and determines a conflict resolution priority. Quantitative management of the whole steel bar construction process is achieved, the model updating efficiency and the conflict avoidance precision are improved, the construction cost and the safety risk are reduced, and the method is suitable for steel bar construction management of various constructional engineering.

The invention relates to the technical field of power distribution networks and the technical field of deep learning, and discloses a power distribution network topological graph generation method, device and equipment based on a time clear network, and the method comprises the steps: a power distribution network automation system generates an adjacency list at a current moment; dividing a plurality of nodes in the adjacency list into a plurality of areas, selecting a bus node of each area as a traversal starting node of each area, creating a working thread corresponding to each area, and starting from the traversal starting node in each area through the working thread corresponding to each area; performing a traversal operation of breadth-first search on each region to obtain a connection tree of each region, merging the connection trees of each region to generate a connection tree of the power distribution network, converting the connection tree of the power distribution network into a topological matrix of the power distribution network, inputting the topological matrix of the power distribution network into a drawing tool, and drawing the topological matrix of the power distribution network. The drawing tool generates a topological graph of the power distribution network at the current moment. The generation efficiency of the topological graph can be improved.

The application relates to a blockchain cryptocurrency fund flow searching method and a control system. The method comprises the following steps: acquiring transaction detail data information, preprocessing the transaction detail data information, and generating a materialized view; presetting a search condition of a query address, and acquiring the query address from the materialized view according to the search condition; traversing the query address, obtaining a first result graph, saving a target address in the first result graph, and searching from the target address in sequence through breadth-first search and depth-first search to obtain a target path list. The breadth-first search and the depth-first search are combined, the searching efficiency is effectively improved, the requirement of fast searching a feature address is met, the data searching speed and precision can reach an optimal level, and the calculation pressure of a server is reduced.