146 results about "Depth-first search" patented technology

The invention relates to a real-time power grid topology analysis method based on a graph neural network, and the method comprises the steps: firstly, carrying out the electrical topology analysis at a substation level through constructing an optimized power system physical connection model, carrying out the electrical topology analysis in combination with a label propagation algorithm (LPA), precisely recognizing a calculation region, optimizing the division of the calculation region through employing the graph neural network (GNN), and enhancing the topology adaptability, mistaken division is reduced; and the robustness of equipment state change is improved. Besides, according to the method, the real-time performance, the accuracy and the intelligent level of power grid topology analysis are further improved by predicting the influence of the state change of the circuit breaker and the disconnecting switch on the power grid topology and training a GNN model and reinforcement learning (RL) optimization region division strategy through historical data. And finally, bus-branch connection is optimized through depth-first search (DFS), the topology integrity is ensured, and the efficiency of electrical island analysis, power supply area division and fault recovery is remarkably improved. The real-time performance, the accuracy and the intelligent level of power grid topology analysis are effectively improved.

The invention belongs to the field of data processing, and particularly discloses a method and system for automatically generating a document according to a template and structured data, and the method comprises the steps: processing an archive template file according to a document object model analysis method, extracting the position, the type and the associated tag of a placeholder, and obtaining a placeholder metadata set; according to the placeholder metadata set, traversing a document hierarchical nesting relationship by adopting a depth-first search algorithm to obtain a hierarchical placeholder mapping table; obtaining the structured data of the document repair record through an external data source, judging whether the structured data field corresponds to the tag in the mapping table according to the hierarchical placeholder mapping table, and if so, generating a matching pair list from the field to the placeholder to obtain a preliminary matching data set; the objective of the invention is to solve the problem of a business scene in which complex nested data and file template placeholders cannot be accurately matched in a document repair record in the prior art.

The invention discloses a decentralized identity management method based on a trusted execution environment, and the method comprises the steps: generating, isolating and storing a user decentralized identity in the trusted execution environment of equipment, carrying out the Hash signature of a decentralized identity document, and recording an uplink; the block chain consensus node verifies the decentralized document uplink transaction through a Byzantine fault-tolerant consensus mechanism and achieves consistency, and writes the metadata of the decentralized identity into the block; encrypting and storing the voucher in a trusted execution environment of the equipment by adopting a Pearson commitment, and supporting voucher Hash uplink and commitment uplink; and introducing a multilevel access structure based on a binary tree, and verifying the and / or logic combination voucher by adopting a depth-first search algorithm. According to the method, decentralized autonomy of the user identity and hardware security isolation are supported, and the reliability and privacy protection capability of user identity management are improved.

The invention relates to the fields of computer graphic processing, industrial software technology and the like, discloses a Cocos engine-oriented pipeline loop perception linear decomposition method, and aims to solve the problem that a complex topological graph which cannot be effectively processed and comprises a high-density nested loop in the prior art. The structure is completely and accurately decomposed into a linear drawing instruction sequence which can be accepted by a Cocos engine, and lossless conversion of original topological information is ensured; comprising the steps that after undirected topological graph data are input, a ring detection algorithm is called to recognize and record all independent ring structures in an undirected topological graph and nodes and edges contained in the independent ring structures; judging whether at least one ring is detected in the previous step, and if the at least one ring exists, assigning a determined traversal direction for each detected ring according to a preset ring direction rule; and if the ring does not exist, skipping, and directly performing depth-first search traversal on the undirected topological graph data.

The invention provides an industrial anomaly detection and root localization method and system based on data driving, and the method comprises the steps: carrying out the cleaning, feature extraction and normalization processing of original data collected in an industrial production process, and constructing a feature space; based on a local anomaly factor LOF and a mahalanobis distance MD method, jointly detecting local anomaly and global anomaly, and identifying an abnormal working condition; extracting space and time correlation characteristics of the abnormal variables through Pearson correlation weighting and Granger causal test to form a space-time correlation matrix; constructing an abnormal causal network based on the matrix, and tracing an abnormal root and a propagation path through depth-first search and abnormal propagation intensity evaluation; and finally, dynamic optimization of the anomaly detection and diagnosis method is realized based on parameter self-adaption and model incremental learning. According to the method, the anomaly detection accuracy and the anomaly traceability interpretation capability can be effectively improved, and the intelligent level and the self-adaptive capability of data processing are enhanced.

The invention provides a networking scene-oriented multipath service optimization method and system, and relates to the technical field of network scheduling optimization, and the method comprises the steps: obtaining topological structure information and service demand information of a communication network in a networking scene, and employing a depth-first search algorithm in combination with a pruning mechanism to generate a candidate path set; on the basis of a marginal value contribution degree theory, calculating a transmission value factor and a path complementary factor of each candidate path in the candidate path set, calculating marginal value contribution degrees of different candidate path combinations according to the two factors, and taking the path combination with the highest marginal value contribution degree as an optimal path combination; and aiming at each path in the optimal path combination, establishing a multi-objective optimization model, and obtaining a final scheduling scheme of the service flow among the paths in the optimal path combination. According to the invention, the problems of lack of differentiated path selection mechanism, poor fault tolerance of single-path transmission, insufficient resource coordination capability and the like in multi-service transmission in the prior art can be solved.

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

The invention discloses a novel power distribution network real-time topology tracing method and system, and the method comprises the steps: carrying out the comprehensive collection of the voltage, current and power of a key node based on a power distribution network SCADA system, and constructing a historical measurement data set containing a topological structure label; designing and training a Transform deep learning model suitable for the characteristics of the power distribution network; the feature space of the Transform model is optimized on the basis of the maximum margin principle; constructing a topological graph model of the power distribution network, carrying out weight assignment, and rapidly tracing and positioning a problem region when a fault or an abnormal condition occurs by using an improved depth-first search algorithm; and constructing a multi-scene topology traceability collaborative decision-making system, and generating a visual traceability report including a fault area topology structure, an influence range and a key equipment state. According to the method, the problems of incomplete data collection, limited learning ability of an identification algorithm, lack of an efficient local tracing mechanism and the like in novel power distribution network topology tracing are solved, and the fault processing efficiency and the operation reliability of the power distribution network are improved.

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 provides a distribution network dynamic partition power recovery optimization method considering uncertainty resource support under a typhoon extreme disaster, and belongs to the technical field of distribution network partition power recovery. A representative fault scene is generated by adopting a Latin hypercube sampling and spectral clustering method, the output characteristics and the damage degree of distributed power supplies such as photovoltaic wind power energy storage and the like under a typhoon condition are evaluated, and a centralizable power recovery region and an incentralizable power recovery region are dynamically divided by applying a depth-first search algorithm. A multi-target optimization model of fault reconstruction and island power recovery is constructed for different areas, a dynamic feedback adjustment mechanism based on a load recovery rate is established, and multi-period dynamic power recovery scheduling is implemented to realize multi-source coordinated dynamic power supply recovery in the whole process of typhoon disasters. The technical problem that the power restoration method of the power distribution network under typhoon disasters lacks dynamic self-adaptive ability and a multi-source coordinated scheduling mechanism is solved.

The invention discloses a transformer substation work ticket knowledge graph construction method based on a large language model, and belongs to the field of energy power and artificial intelligence. The method comprises the following steps: based on standardized electrical drawing annotation data, generating an independent knowledge graph through data recombination preprocessing, rough knowledge graph construction and attribute supplementary reasoning; all the independent knowledge maps are divided into a framework layer map and a detail layer map according to voltage grades, and full map fusion is completed by adopting a hierarchical fusion strategy; carrying out breakpoint detection by using an isolated point detection algorithm based on degree centrality, carrying out connectivity analysis through depth-first search, and re-fusing multiple non-connected sub-graphs; and screening abnormal nodes based on an electrical connection rule, inputting the abnormal nodes into a large language model to automatically supplement Agents to generate correction suggestions, and completing map optimization. Through cooperation of the large model Agent and multiple algorithms, the electrical data structuring efficiency and accuracy are improved, and knowledge network support is provided for automatic operation and fault diagnosis of the transformer substation.

The invention discloses a network fault prediction method, and relates to the technical field of fault prediction. The method comprises the following steps: constructing a cross-level semantic topology model; screening a key link by using a node importance algorithm and extracting a structural fingerprint vector; calculating the deviation degree between the current link and the structure fingerprint vector, and judging that the structure is abnormal if the deviation degree is smaller than a threshold value; constructing an anomaly propagation rule base according to historical faults and operation and maintenance data; when a plurality of structure anomalies appear at the same time, the conflict resolution algorithm determines an anomaly dominant factor; potential signals are collected according to the abnormal dominant factors, weights are given according to a rule base, and weighting is carried out to obtain comprehensive potential signals; based on this, a fault propagation path is constructed, and a path weight is calculated by using a Pearch ranking algorithm; a fault chain is rehearsed through a depth-first search graph traversal algorithm; and verifying the rehearsal chain by using a preset test statistic circumstantial evidence to obtain a fault prediction result. According to the invention, on the basis of the fault prediction result, cross-level network fault prediction in network propagation is realized.

The invention, which relates to the technical field of interactive script generation, discloses a script-centered interaction-driven scene generation system comprising the following modules: a script management module, a script arrangement module, a dependency propagation module, a thesaurus interaction cooperation module and an execution output module. Creating a basic data structure of the script, distributing script attributes, and managing script elements, constraints and logics; identifying scene nodes and events, constructing three-layer dependency and weighting, and generating a reverse causal chain through recursive dependency and depth-first search; by maintaining a dependency table, event-driven updating and dirty node marking for each node, efficient dependency propagation and low-delay updating are realized through a priority queue and differential generation; dividing three levels to cooperatively realize dynamic script generation and interaction, and realizing script control based on gating residual connection, a reverse causal chain and a time attention mechanism; and constructing an interactive execution layer, integrating a real-time feedback mechanism, automatically generating a script, and outputting and displaying an executable format.

This invention belongs to the field of target recognition technology and discloses a target association recognition method based on multi-frame images, including the following steps: S1, determining the search starting point; S2, based on the determined starting trajectory, selecting two target points on one of the trajectories to predict the target position in the third frame; S3, starting the next process, searching from the second frame, with the coordinates of all targets in the second frame as the starting point, referring to S1 to S2, until the coordinates of all targets in the Nth frame are the starting point; S4, deleting duplicate false alarm tracks. This invention performs data pruning during the depth-first search process, pruning data not in the velocity gate and excluding it from the least squares estimation calculation, while also pruning erroneous tracks. This not only avoids the curse of dimensionality but also greatly improves computational power under high false alarm conditions.

The invention relates to a depth-first search method based on a quantum computing theory. The depth-first search method comprises the following steps: mapping a system state into a first quantum model; selecting a head node and building a quantum model of the head node, namely a second quantum model; establishing a quantum model of the path, namely a third quantum model; the first quantum model, the second quantum model and the third quantum model form a total quantity sub-model, and the total quantity sub-model outputs a system depth-first search result. According to the method, the third quantum model of each path is established by using the Tofli gate, so that the node which is the same system as the selected head node can be identified, multiple times of search are not needed, and depth-first search can be directly performed through the fixed quantum models; a total quantity sub-model of each path is measured by utilizing a measurement operator, a conduction node number on each path is respectively output, the total quantity sub-model with the maximum output node number is selected, the output of the total quantity sub-model is the node number and the node number of a system connected with a first node, and a quantum depth-first search result can be quickly obtained.

The application discloses a kind of microservice performance anomaly detection methods based on Transformer, comprising: obtaining the span log of microservice system, the span with same tracking ID in span log is combined together, to obtain multiple different call chains, using depth-first search algorithm searches all paths of the call chain from root node to leaf node, to obtain multiple service call sequences in the call chain and its corresponding multiple response time sequences, each service call sequence is input into the detection model trained in advance, to obtain the probability distribution of each feature value in the path vector set corresponding to the service call sequence, according to the probability distribution of service feature value output by detection model, the probability of service actual feature value in path vector is obtained as the abnormal score of service, according to the abnormal score of service, whether service is the service that appears performance anomaly in call chain is judged.The application can solve the technical problems of poor convergence speed and detection effect of existing method model.

The invention discloses a method for identifying a molecular structure and constructing a reaction network based on atomic coordinates, and belongs to the technical field of material simulation. According to the method for recognizing the molecular structure based on the atomic coordinates, by obtaining trajectory data, atoms outside a box are converted into atoms inside the box through minimum mirror agreement; grids are divided according to a space truncation radius, potential bonding atoms are accurately screened, and redundancy of distance calculation between every two atoms of the whole system is avoided; atomic clusters are quickly divided in combination with depth-first search, and molecular structure recognition is achieved. The method only depends on basic data such as atomic coordinates and does not depend on a simulation software post-processing module, and the limitation that only a single simulation system is adapted in the prior art is broken through. According to the method for constructing the reaction network, molecular structure description is compared frame by frame, and invalid reactions are offset and filtered through positive and negative reactions to obtain net reactions; counting the net reaction times to determine the reaction probability so as to quantify the tendency of the reaction; and constructing a reaction network by taking the molecular structure as a node and the reaction frequency as an edge weight.

The application discloses a road network site selection method based on a shortest path and path enumeration, first reads a real road network data set, extracts vertex and edge data therein, and establishes an undirected and weighted graph according to the extracted vertex and edge data; the undirected and weighted graph is read and processed by using a function readGraph() to obtain a starting point set; the undirected and weighted graph is read and processed by using the function readGraph() to obtain a terminal point set; the starting point set and the terminal point set are processed by using a depth-first search DFS method to obtain all paths between all starting points and all terminal points and within a preset path threshold d range; all paths between each starting point and each terminal point and within the preset path threshold d range are processed in descending order to obtain a maximum path quantity from each starting point to all terminal points and corresponding terminal points. The application can solve the technical problem of low query efficiency of the existing road network site selection method based on a Dijkstra algorithm.

The invention provides a municipal road BIM edge computing real-time control method and system based on an OPC UA protocol, and the method comprises the steps: monitoring the communication state of each node in an edge computing network and the continuity of sensor data, recognizing a data missing region, and determining the specific position of the data missing region; according to the position of the data missing area, a depth-first search algorithm is adopted to traverse the connection relation of adjacent nodes, and a fault boundary corresponding to the data missing area is constructed; and extracting redundant storage data from the fault boundary, analyzing whether the coverage capability of the redundant storage data meets the requirement or not, and if so, filling the data missing region to generate a preliminary repair result.

The present application relates to the technical field of data storage optimization, and particularly relates to a user behavior data storage optimization method based on an approximate neighbor search algorithm, which specifically comprises the following steps: collecting user behavior data through various sensors and intelligent devices, analyzing data blocks involved in the historical query process of the user behavior data, constructing a historical use matrix of the data blocks, further constructing a weighted undirected graph according to the matrix information, then constructing a maximum spanning tree based on the weighted undirected graph using Kruskal's algorithm, generating the longest path of the tree through two depth-first searches, extracting the optimal data block storage sequence subsequence from the longest path, removing the related nodes of the subsequence from the weighted undirected graph, repeating the search and removal operations until the weighted undirected graph is empty, and generating a complete data block optimal storage sequence. Through the introduction of the approximate neighbor search and the graph optimization algorithm, the storage efficiency of the user behavior data can be improved, and the access delay during the query can be significantly reduced.

This invention relates to a multi-agent orchestration and execution method supporting complex cyclic graph structures, comprising: S1: receiving workflow configuration and constructing a directed graph data structure; S2: performing a depth-first search traversal on the directed graph data structure, detecting and outputting strongly connected components in the directed graph data structure; S3: abstracting strongly connected components containing multiple nodes into super nodes, and keeping single-node strongly connected components as ordinary nodes, thereby constructing a directed acyclic graph containing super nodes; S4: performing topological sorting on the directed acyclic graph to generate a hierarchical execution plan containing multiple execution layers; S5: scheduling execution items according to the hierarchical execution plan, and when an execution item is the super node, starting a loop executor for iterative execution. This method, by constructing a multi-layered technical system, integrates graph algorithm innovation, unified context management, abstraction layer design, metadata-driven techniques, etc., to achieve automated orchestration and execution of multi-agent workflows supporting cyclic graph structures.

The invention discloses a static analysis tool test case generation method based on a program slicing technology, which comprises the following steps: step 1) according to a defect report of a static analysis tool, collecting and extracting variable information related to generation of a specific defect in a program as a slicing criterion; 2) through control flow analysis and data flow analysis, recording data and a control dependency relationship between statements, constructing a program dependency graph, and calculating a statement set having a dependency relationship with a slicing criterion; 3) positioning nodes corresponding to the statement set in the abstract syntax tree of the source code through a depth-first search algorithm, and deleting other nodes; the method comprises the following steps of (1) collecting a user-defined type or an external function declaration which is used through static program analysis, and outputting the type definition and the function declaration to an independent header file, and (5) writing edited codes into a file, and clearing redundant information to serve as a test case, the method adopts two-section analysis, and the test case can be generated efficiently and precisely.

The invention discloses a medical image feature selection method based on population tree search and hybrid intelligent heredity. The method comprises the following steps: acquiring a to-be-processed medical image feature data set; initializing a population tree; evaluating and calculating the fitness; and executing branch evolution, starting from the current tree node, and generating a plurality of sub-generation branches in parallel. Each branch generates a new population by applying a hybrid genetic algorithm to the parent medical image feature subset population; analyzing statistical summary information of all filial generation branches by using a decision model; and performing depth-first search on the population tree until a preset termination condition is met. And finally, outputting a medical image feature subset with the highest fitness found in the whole search process as an optimal feature selection result. According to the method, the problems that a traditional feature selection method is prone to falling into local optimum in high-dimensional data and lacks intelligent guidance are solved, and the global search capability, the convergence speed and the solution quality of the feature selection process are improved.

The invention provides a method for searching a longest circuit link based on a layer connection file, and the method comprises the steps: constructing a bidirectional connected graph model based on all layers and connection relationships in the layer connection file; traversing the graph model by adopting an improved depth-first search algorithm to search for a longest path; the improvement comprises the following steps: managing via hole layer nodes and physical layer nodes as access states together, and executing complete backtracking containing the states; and after all traversal is completed, outputting the finally obtained longest path. According to the method, the complex topology can be robustly processed and the longest circuit link can be accurately extracted by constructing the bidirectional connected graph model and bringing the via hole layer into a complete backtracking mechanism of state management.

The embodiment of the application provides a dual-arm action sequence planning method for on-orbit multi-module replacement tasks, comprising: mathematically modeling the multi-module replacement task and the dual-arm system; task decomposition is performed on the multi-module replacement task, a dynamic programming algorithm and a depth-first search algorithm are used to search for a task decomposition scheme for dividing the large-scale module replacement task into multiple batches of module replacement tasks according to single-module replacement task constraints and task points in series; a dual-arm task allocation and scheduling model for multi-module replacement is constructed by considering multiple optimization objectives and constraint conditions, and an intelligent optimization algorithm is used to solve the dual-arm action sequence; the obtained solution set is evaluated by using a non-dominated solution distance method, the dual-arm action sequence scheme is sorted, and an ideal optimal planning solution is obtained. According to the technical scheme provided by the embodiment of the application, the efficiency of the dual-arm system in executing the on-orbit multi-module replacement task can be improved under the premise of avoiding dual-arm conflicts, and the continuous and healthy operation of the dual-arm system can be ensured.

The application discloses a kind of flat plate type parts fast identification method based on equal-thickness surface splicing, by extracting the B-rep data of part, whether there is parallel plane in current part based on B-rep data is judged, for the part of existing parallel plane, equal-thickness surface in part is spliced using depth-first search algorithm to obtain splicing surface, the area of splicing surface and the area of part original surface are calculated, the maximum face thickness corresponding to the largest area is screened;The minimum direction size of the bounding box of current part in three coordinate axis direction is calculated using AABB bounding box algorithm, the maximum face thickness is compared with direction size, if the maximum face thickness is equal to the minimum direction size, then the current part is judged as flat plate type part;The application can be applicable to large-scale part library batch efficient analysis and identification, and flat plate type parts can be efficiently and accurately identified.

The application discloses an annealing clustering combined probability data association method based on an adaptive threshold, constructs a variable time interval Kalman filter prediction model, and adapts the observation characteristics of non-uniform sampling; generates an adaptive gating threshold based on an inter-frame interval, compensates for the uncertainty accumulation caused by long-time prediction; completes candidate association screening through Mahalanobis distance and a verification matrix; uses an annealing clustering strategy to decompose a global giant association graph into small-scale independent sub-clusters, greatly reduces the calculation complexity; uses a heuristic pruning depth-first search to quickly generate a high-probability combined association event in the sub-cluster; finally, completes target state updating in a probability weighting manner, sets a Hungarian hard allocation safety degradation mechanism, and guarantees stable operation of the system in an extreme scenario. The application can significantly improve the track keeping ability, association accuracy and tracking precision under sparse observation conditions, and controls the calculation time consumption within an engineering allowable range.

The present application relates to the field of automatic drawing, in particular to a substation secondary circuit drawing multi-element collaborative identification and topology reconstruction method, the method comprises the following steps: receiving scanning drawing, photo, CAD export image multi-source input, drawing standardization pretreatment; terminal and device symbol are recognized by using target detection, text information is extracted and classified through OCR, and connection line is tracked by using skeleton extraction; confidence data of each element is generated synchronously; based on distance, direction and format constraint, matching score is calculated, terminal and number binding is realized by using optimal matching, and correlation is iteratively corrected by device and terminal attribution, connection line and terminal adsorption rule; the recognition result is converted into graph structure, topology network is constructed, loop type and closure are identified by using depth first search; abnormal elements are automatically marked, quality evaluation report is generated; output data supports drawing incremental update and version traceability, and connects downstream application platform.

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.

This application provides a domain name risk assessment method, apparatus, electronic device, and storage medium, relating to, but not limited to, the field of data processing technology. The method includes: determining a DNS resolution dependency path tree corresponding to a target domain name; performing a depth-first search on the DNS resolution dependency path tree to determine each DNS resolution dependency path of the target domain name; obtaining attribute risk indicators of node risk attributes for each node in the DNS resolution dependency path tree; calculating path risk indicators for each DNS resolution dependency path based on the DNS resolution dependency paths and each attribute risk indicator; and determining the target risk indicator for the target domain name based on the path risk indicators corresponding to the target domain name. This application's embodiments diagnose domain name risks from a management perspective, supplementing and improving existing DNS resolution-side risks. Furthermore, based on the dependency path tree, it enables further analysis of various dependency risks in the DNS resolution process, thereby improving the accuracy and efficiency of domain name risk assessment.