353 results about "Depth-first search" patented technology

The invention relates to the technical field of big data analysis and artificial intelligence, in particular to a multi-source heterogeneous big data fusion and reasoning method and system based on a knowledge graph, and the method comprises the following steps: constructing mapping interfaces of various data sources, collecting and storing entities in the data sources, and constructing nodes in the knowledge graph; performing representation learning on a graph structure of the knowledge graph based on a graph neural network, extracting an embedded vector of a node, and extracting a fusion node through the embedded vector and a mapping interface; performing knowledge completion on the fusion nodes by using a neural network based on symbolic logic; generating candidate reasoning paths by adopting a depth-first search algorithm based on the knowledge graph; reasoning is carried out based on an attention mechanism and path similarity, candidate reasoning paths are screened and sorted to obtain an optimal reasoning path, a graph neural network is adopted to carry out representation learning of a knowledge graph, symbolic logic and the neural network are combined, the interpretability of the reasoning process is guaranteed, and the learning ability is high.

The invention relates to the technical field of automobile network security, in particular to an intelligent networked automobile penetration test path generation method and system based on a knowledge graph, and the method comprises the steps: constructing an intelligent networked automobile intranet penetration test security knowledge graph through fusing multi-source heterogeneous data based on an intranet E / E architecture; generating a potential attack path based on the knowledge graph by using a depth-first search algorithm of dynamic pruning optimization; a multi-dimensional space-time risk assessment framework is utilized to quantify path threat intensity and execution feasibility, and quantitative assessment on the generated attack path is realized; and according to an evaluation result, selecting a high-risk and feasible attack path to carry out an actual penetration test, and discovering security vulnerabilities and weaknesses in the system. According to the method, the attack surface is fully covered, the high-value attack path is efficiently generated, the threat intensity and the execution feasibility are accurately and quantitatively evaluated, and the safety test efficiency and accuracy of the intelligent network connection automobile intranet are remarkably improved.

The invention provides an SCA and SAST combined vulnerability reachable path detection method and device. The method comprises the steps of obtaining a to-be-detected item; extracting an entry function based on the to-be-detected item; when the to-be-detected item is a source code item or a product item, generating a function call graph based on call graph generation logic corresponding to the source code item or call graph generation logic corresponding to the product item; identifying a vulnerability function in the dependent component of the item to be detected through a software component analysis tool based on preset software bill of material information; taking the vulnerability function as a starting point, performing depth-first search traversal in the function call graph, and detecting whether an entry function is traversed or not; and if yes, determining a path from the entry function to the vulnerability function as a vulnerability reachable path. According to the method and the device, the reachable path of the vulnerability can be automatically detected, so that whether the security vulnerability is triggered or not is determined, invalid checking of the SCA tool in the use process can be avoided, and the reliability of the SCA tool is improved.

The invention discloses a power grid dispatching anti-error method and system based on topology analysis, and relates to the technical field of power grid dispatching anti-error, and the method comprises the following steps: building a remote signaling credibility evaluation model based on remote signaling evaluation data, and obtaining high-credibility remote signaling data; performing sequential logic verification based on high-credibility remote signaling data and multi-source data fusion to generate a power grid topology model; carrying out partition division on the power grid topology model based on spectral clustering and a state-driven edge weight method, and carrying out boundary equivalence processing to obtain a plurality of topology modules; and performing simulation scheduling operation on the plurality of topology modules based on a scheduling instruction dispatching order objectification analysis result, performing path traversal based on a depth-first search method, and performing anti-error analysis on the traversed path in combination with a reinforcement learning method. According to the power grid dispatching anti-error method combining remote signaling credibility evaluation, dynamic topology partition and reinforcement learning algorithm, the anti-error verification capability and the intelligent level of a power grid dispatching system are comprehensively improved.

The invention discloses a method for constructing a digital twinborn model of a full life cycle of an automobile part, which relates to the technical field of digital twinborn, and comprises the following steps: inputting a life cycle data set into an initial life digital twinborn model, carrying out time domain local feature fusion by a feature extraction layer, capturing a degradation trend by a time sequence analysis layer, and constructing a digital twinborn model. Extracting an actual detection value in the life cycle data set; calculating a deviation value between the life prediction value and the actual detection value of the automobile part; carrying out sequential relation mapping on the deviation value to generate a dynamic causal atlas; carrying out graph traversal on the dynamic causal atlas to obtain key degradation path nodes; the dynamic causal reasoning is used for carrying out correlation analysis on the key degradation path nodes, degradation characteristic parameters are generated, the depth-first search and analytic hierarchy process are adopted, dynamic causal correlation precise modeling of the life digital twin model is achieved, and the robustness of the life digital twin model under the actual complex working condition is remarkably enhanced.

The invention discloses a big and small model cooperation-based power equipment fault knowledge graph construction method, which comprises the following steps of: preprocessing a power equipment fault document, storing image information into a cloud database, and screening high-quality text information from a big language model; summarizing different text information by using a large language model to generate an initial ontology framework; performing pre-labeling on text information by using a large language model, obtaining a training data set in combination with rule matching scripts and experts, optimizing a traditional deep learning small model based on data characteristics to obtain entities, and eliminating error entities based on a hierarchical verification strategy; utilizing a large language model to carry out relation extraction on association degrees between entities and different entity relations; and performing entity alignment by using the large language model, the SBERT model, a depth-first search algorithm and context information to form a final power equipment fault knowledge graph. According to the method, the problems of high labor cost consumption and knowledge redundancy of the knowledge graph in the existing power equipment fault knowledge graph construction process are solved.

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 an enterprise network weak password evaluation method based on a logic attack graph and password evaluation, which comprises the following steps: firstly, collecting a network topology structure, a security policy and system configuration of an enterprise intranet, and combining ATTamp; according to the inference rule mapped by the CK knowledge base, logical inference is carried out by using a MulVAL tool to obtain a logical attack graph; then acquiring system account password information of each host, calculating password entropy and simulating password blasting time evaluation to obtain a password risk value; then, the password risk value is associated and combined with the logic attack graph, and a new weighted attack graph containing the password risk value is generated; identifying an attack starting point and an attack target point in the attack graph with the weight at the same time; traversing all attack paths in the weighted attack graph by using a depth-first search algorithm, and calculating a weak password risk value on each attack path; and finally, taking the attack path with the highest weak password risk value as an optimal attack path, generating and visualizing, and improving the network security defense level of an enterprise.

The invention relates to a method and a device for tracking a dynamic resource dependency relationship and terminal equipment. The method comprises the following steps: acquiring cloud resource dependency data of a cloud service platform and / or a data center in real time; constructing a cloud resource dynamic dependency graph according to the cloud resource dependency data; detecting the cyclic dependence of the cloud resource dynamic dependence graph by adopting a depth-first search algorithm to obtain a cyclic dependence detection result; and using a TGCN algorithm to predict the future cyclic dependence of the cloud resource dynamic dependence graph to obtain a cyclic dependence prediction result. According to the method, a real-time cloud resource dynamic dependency graph is constructed, a depth-first search algorithm and a TGCN algorithm are adopted to process the cloud resource dynamic dependency graph to obtain a cyclic dependency detection result and a cyclic dependency prediction result, and a basis is provided for determining corresponding resource cyclic dependency prevention measures and processing strategies. And data is provided for effective cloud resource management and optimization, so that problems and risks caused by cyclic dependence are reduced, and the reliability of cloud services is improved.

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 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 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 relates to an operation and maintenance change influence assessment method and system based on a knowledge graph, and the method comprises the steps: integrating a configuration management database, call chain data and network connection data, constructing a directed software and hardware knowledge graph through a graph database, querying a change influence path through a depth-first search algorithm, and carrying out the operation and maintenance change influence assessment. A graph visualization engine is applied to generate a change knowledge graph, and clear and accurate upstream and downstream associated information of a change object is provided for review and operation and maintenance personnel; according to the method, frequent item analysis is carried out on historical abnormal indexes, label propagation calculation is carried out on an abnormal index map, and a community propagation sub-graph is generated, so that potential and dispersed abnormal information can be aggregated and associated more scientifically and intelligently, and a rapid positioning auxiliary tool is provided for faults caused by changes; the scientificity and accuracy of change evaluation are improved, the change risk is effectively avoided, and quick positioning can be performed when a fault occurs.

The invention discloses an electric carbon tracking method based on electric power flow topological grid modeling, and solves the problems that the electric power transmission path is not clear and the electric carbon factor calculation is not refined enough in various voltage level electric power grids. The method comprises the following steps: based on a regional power grid architecture, establishing an electrical load flow calculation model of an electrical carbon tracking standard grid; inputting power supply output, transformer substation and line power flow, generating a unit injection matrix, a branch power flow distribution matrix and a load distribution matrix, generating a node adjacency shunt matrix, calculating the proportion of outflow power line power flow of each node, and generating a node adjacency shunt coefficient matrix; calling a depth-first search algorithm for the nodes, calculating directed line paths from the starting node to the tail end node, forming shunting coefficients contributed by all paths from power flow generated by the starting node to the tail end node, obtaining a power distribution matrix in combination with the power injected into the starting node by the corresponding unit, and obtaining a power distribution matrix according to the carbon emission intensity of each power supply; a node carbon potential matrix is obtained through calculation, and electric carbon tracking of a power supply layer, a power grid layer and a load layer is achieved.

The invention belongs to the technical field of data mining and analysis processing, and particularly discloses a real-time data stream processing method and system based on distributed computing, and the method comprises the steps: obtaining real-time and historical data streams of a server program window, employing a time sequence synchronization mode to rapidly position and start a window matching path and historical abnormal performance, and obtaining a real-time data stream of the server program window; the high real-time requirement is met; historical abnormal event statistics and real-time data flow similarity analysis are combined to dynamically construct a distributed tracking link, a depth-first search algorithm is used to predict a risk event, and an abnormal probability and a deviation degree are accurately quantified; different optimization strategies are configured according to different performance parameters, a multi-level defense mechanism is formed, and the self-adaptive capacity of the system is improved. According to the method, the interaction track and the performance parameter set are comprehensively analyzed, the abnormal performance link relation is marked, abnormal event correlation analysis is refined, the maintainability and expansibility of the system are enhanced, and the data processing efficiency is remarkably improved.

The invention relates to the technical field of natural gas pressure regulating station automatic site selection, and provides a natural gas pressure regulating station automatic site selection method, system, equipment and medium, and the method comprises the steps: precisely extracting a water system contour boundary through employing an edge detection algorithm, generating a water system distribution diagram, and building a terrain constraint condition through combining with a gradient grading mask; the gradient attenuation range of the water system safety buffer area is dynamically adjusted based on the river channel bending curvature characteristic and the branch intersection density, and water body protection and land use requirements are effectively balanced; recognizing boundaries of a building area and a forest protection area through color and shape features of the remote sensing image, and performing superposition to generate a composite security constraint layer; and screening low-gradient contiguous effective candidate blocks after excluding the protected area, simulating a feasible path through a depth-first search algorithm, and selecting an area which is high in connectivity with a main road and small in topographic relief as a final site. The ecological environment damage risk is reduced, the engineering implementation feasibility is ensured, and the optimal balance of ecological protection, safety protection and construction cost is achieved.

The invention relates to a surveying and mapping unmanned aerial vehicle route planning method based on an intelligent algorithm, and aims to improve the route planning efficiency and task execution capability of an unmanned aerial vehicle in a complex environment. The method comprises the following steps: firstly, constructing a task environment model, performing two-dimensional rasterization on an outdoor low-altitude environment in a mountainous area through a grid method, and taking bird flock density as one of main constraint conditions into the model; and then, an improved depth-first search (DFS) algorithm is used for flight path planning. According to the algorithm, a bwlabel function and a FOrientitOK5 function are introduced, the search direction is optimized, the unmanned aerial vehicle is prevented from falling into a search dead zone, repeated traversal is reduced, and therefore the search efficiency is remarkably improved. In addition, by simplifying the parameter adjustment process, the universality and stability of the algorithm are improved, and the method is suitable for unmanned aerial vehicle surveying and mapping tasks in various complex environments. Through the improvement, effective technical support is provided for intelligent navigation in a complex environment, and the task completion efficiency of the unmanned aerial vehicle is remarkably improved.

The invention discloses a wafer defect image classification method based on adaptive parameter selection OPTICS. The method comprises the following steps: performing isolation and twin outlier screening on wafer defect images; the designed intra-cluster density and inter-cluster density function considering the shape and size of the cluster is used as a comprehensive clustering index; a K-D tree algorithm is adopted to select the range of MinPts parameters of the OPTICS, and the optimal MinPts clustering parameters are obtained based on the comprehensive clustering indexes; a depth-first search algorithm based on a graph theory is adopted to analyze connectivity between data points to merge spatial adjacent clusters, and a clustering result is obtained; and using the improved neural network model to classify clustering results. According to the method, the OPTICS is improved from the three aspects of outlier removal of the wafer defect image, range selection of MinPts parameters of the OPTICS algorithm and self-adaptive parameter selection of the MinPts parameters of the OPTICS algorithm, and the preprocessed data set is classified by adopting the improved neural network classification model, so that the wafer defect classification precision is effectively improved.

The invention relates to the technical field of big data, and discloses a database deadlock detection and analysis method, which comprises the following steps of: loading a configuration file, extracting a monitoring interval parameter and a log level parameter, connecting a target database according to the configuration file, periodically acquiring state information of affairs and resources through a database interface, and storing the state information as a state snapshot; initializing a data structure of the resource waiting graph, adding transaction nodes, resource nodes, request edges and occupation edges based on the state snapshots, performing depth-first search on the resource waiting graph to detect whether a loop exists or not, judging whether transactions and resources related to the loop form deadlocks or not, and recording related information; a report of a deadlock cause analysis and solution is generated based on the transaction state, the resource state, the loop information, the occupancy relationship, and the waiting relationship. According to the method, the state snapshot is constructed by periodically collecting the state information of the affairs and the resources, and the deadlock problem is quickly positioned and accurately analyzed in combination with initialization and dynamic updating of the resource waiting graph.

The invention provides an invoice closed-loop transaction risk assessment method based on a graph neural network, and relates to the technical field of artificial intelligence and machine learning, and the method comprises the steps: constructing a graph model (an invoice is a node, the upstream and downstream relation of an enterprise is a directed edge, and the amount of money, a timestamp and enterprise information are recorded); automatically learning node features by using a graph neural network and predicting a risk probability; identifying a closed-loop path through depth-first search based on the node representation vector, and extracting a hierarchical depth, a total amount and an average risk probability; and finally, a comprehensive risk value is calculated through a weighting formula (combining the hierarchy depth, the sum of money and the risk probability), and multi-dimensional closed-loop transaction risk assessment is realized.

The invention provides a detection method and system for earthquake search and rescue, and relates to the technical field of search and rescue robots, and the system comprises an air detection module which is used for obtaining life body information through a composite life detector; the life body information is transmitted to the ground search and rescue module through the communication device; receiving path planning information by using a communication device, and controlling the unmanned aerial vehicle to detect according to a planned path; the ground search and rescue module is used for performing optimal path planning on the unmanned aerial vehicle and the search and rescue trolley based on a depth-first search algorithm by utilizing the processing sub-module to obtain path planning information; based on the path planning information, controlling the search and rescue trolley to perform a search and rescue task; and the base station sub-module is used for converting the life body information into life body information digital signals, sending the life body information digital signals to search and rescue personnel, and sending path planning information to a communication device of the air detection module to complete earthquake search and rescue. The problem that trapped persons are difficult to effectively rescue in a complex post-disaster environment is solved.

The invention relates to the technical field of network security, in particular to a depth-first search attack graph generation method and system based on node asset weight, which introduces a deep learning priority algorithm of dynamic adjustment of node weight and intelligent node weight calculation, reduces the scale and complexity of an attack graph as much as possible while ensuring the accuracy of the attack graph, and improves the security of the attack graph. The attack graph generation efficiency is improved; comprising the following steps: calculating a comprehensive importance score of each node by comprehensively evaluating characteristics of each node in a network, and obtaining an initial weight value according to the comprehensive importance score; the method comprises the following steps: dynamically adjusting an initial weight value of each node in a network through real-time flow monitoring and regular polling of a network state to obtain a corrected weight value; traversing the nodes in a descending order according to the corrected weight values, and recording accessed nodes and paths; the current node has no adjacent unaccessed node or has reached the preset search depth limit; and stopping after all possible paths are searched, and generating an attack graph.

The invention relates to a method for identifying a picture block and a picture label in an architectural drawing, a storage medium and electronic equipment. The method comprises the following steps: acquiring coordinate information of end points of all straight line segments detected from the architectural drawing; constructing a two-dimensional line segment tree by utilizing the coordinate information of the end points of all the straight line segments; performing traversal search on the two-dimensional line segment tree by adopting a depth-first search algorithm to obtain a plurality of target straight line segments related to an outer frame of a drawing frame area in the architectural drawing in all straight line segments, and determining the position of the drawing frame area based on the plurality of target straight line segments; based on the position of the drawing frame area, the drawing label area in the architectural drawing is recognized from the drawing frame area, and compared with an existing manual drawing frame marking method, the workload can be reduced, and the recognition efficiency is improved.

The invention relates to a management method for a ternary polymer lithium battery of shared charging equipment with high cycle index. The method comprises the following steps: carrying out graph theory modeling on a battery management system in the shared charging equipment, and constructing a topological graph model; depth-first search is carried out, a charging and discharging path is identified, and a composite impedance value is calculated; calculating a dynamic voltage spectrum and impedance characteristics to obtain time-domain current distribution; according to the time domain current distribution, performing multi-factor coupling loss calculation on each ternary polymer lithium battery unit to obtain dynamic loss distribution data; performing battery state real-time evaluation based on the dynamic loss distribution data, and reconstructing a nonlinear charging and discharging characteristic curve; and performing multi-time-scale cooperative scheduling based on the nonlinear charging and discharging characteristic curve to obtain a battery charging and discharging control strategy. According to the method, the nonlinear characteristics of the battery can be accurately modeled, the battery state is evaluated in real time, and intelligent scheduling is performed based on a dynamic prediction result to maximize the service life of the battery and the overall efficiency of the system.

The invention provides a node clustering method based on communication network connectivity analysis, and belongs to the technical field of network analysis. The method comprises the following steps: constructing a graph model according to input nodes and a connection relationship thereof, wherein the construction of a graph supports a single network or multi-network relationship; traversing each node in the graph by using a depth-first search algorithm, recording an access path, and classifying the nodes with a communication relationship into the same communication component; and outputting a set of all connected components. According to the method, when the graph is constructed, topology analysis of various network conditions in the undirected graph is supported by dynamically judging the nodes and the neighbor relation of the nodes. The method has the advantages of low calculation complexity and high expansibility, is suitable for the fields of network communication, equipment connectivity check, complex command and control system information transmission modeling and the like, and provides an efficient and accurate solution for connectivity analysis.

The invention relates to a power distribution network topology abnormity intelligent identification method comprising the following steps: S1, constructing a topology structure of a power distribution network, and forming a connection relation graph between nodes; s2, constructing an association relationship between a measurement object file and measurement data of each node of the power distribution network; s3, on the basis of the topological structure of the power distribution network, traversing the graph through a depth-first search method, obtaining the structure and characteristics of the power distribution network in combination with a centrality analysis and community discovery method, and identifying important nodes and community structures; s4, carrying out detection based on the anomaly detection model, carrying out anomaly detection on the topological data of the power distribution network, and obtaining an abnormal node; s5, based on an exception type identification model, acquiring an exception type of the exception node; and S6, in combination with the important nodes and the community structure, evaluating the severity level of the abnormity according to the influence degree of the abnormal nodes and the positions of the abnormal nodes in the network, obtaining the severity level of the abnormity, and performing visualization. The operation safety and the management efficiency of the power distribution network can be effectively improved.

The invention provides a multi-strategy caching and automatic data generation method and system based on a Node environment, and relates to the technical field of data processing, and the method comprises the following steps: receiving a client request, obtaining a data generation template, constructing a data dependency graph to analyze a field dependency relationship, and determining a field generation sequence by adopting a recursive generation strategy and depth-first search; data validity verification is executed, a data generation thread pool is created by adopting an adaptive load balancing mechanism based on the data dependency graph, and asynchronous processing is realized through double buffer queues, so that the data generation efficiency is improved, the data logic integrity is ensured, and the system resource consumption is reduced.

The invention discloses a traceability method and system based on power enterprise production information, and the method comprises the steps: carrying out the content recognition of a plurality of production information data matched with a target power enterprise, and obtaining initial information data; processing the initial information data based on a constructed large language model and a classification model in sequence to obtain an information data set corresponding to different information dimensions; traversing the information data sets based on a depth-first search algorithm to obtain entity information and attribute information corresponding to each information data set, and determining a first relationship result between the information data sets corresponding to different information dimensions; comparing the first relation result with a second relation result obtained by knowledge graph identification to obtain target relation result data; and inputting the target relation result data into a traceability model for processing to obtain each traceability information log of the target power enterprise. According to the method provided by the embodiment of the invention, effective traceability of the production information of the power enterprise is realized.