207 results about "Directed graph" patented technology

The invention discloses a method for realizing a logic breakpoint debugging function of an industrial configuration control system, which relates to the technical field of industrial automation control, and comprises the following steps of: constructing a fault prediction model, performing multi-scale time sequence modeling and spatial correlation analysis on spatial-temporal characteristic tensors, and generating a high-probability fault pile point set; constructing an equipment logic association graph, performing ontology reasoning to obtain an association diagnosis characteristic graph of high-risk logic nodes, and generating a directional monitoring instruction set in combination with the high-probability fault pile point set; and performing space-time scene reconstruction and root cause path analysis on the high-confidence breakpoint trigger signal and the five-dimensional section snapshot to generate an interactive fault diagnosis report. According to the method, the equipment-logic association graph is constructed, graph traversal and ontology reasoning are carried out, the dynamic association relationship between equipment physical data and control logic execution data is modeled into a weighted directed graph structure, node risk values are quantized by utilizing a graph diffusion algorithm, and visual tracking of a fault propagation path is realized.

The invention discloses a water supply network global water quality prediction method based on a double flow-graph convolutional network, and belongs to the field of urban water supply. According to the method, a pipe network model of a directed graph is constructed, information flows in the upstream direction and the downstream direction are extracted respectively, feature learning is carried out through a parallel graph convolution module, a random mask mechanism is introduced during training to simulate sensor missing, and accurate prediction of the water quality concentration under the sparse monitoring condition is achieved. According to the double flow-graph convolution water quality prediction model provided by the invention, the practicability and coverage capability of the water quality prediction model under the condition of sparse monitoring data are remarkably improved; a double flow-graph convolution structure and a dynamic mask training mechanism are adopted, so that the robustness of the complexity of a pipe network is effectively enhanced; the constructed water quality prediction model has the characteristic of high response speed, can realize real-time prediction of water quality in combination with historical monitoring data and network structure information, is suitable for various scenes such as water quality monitoring, abnormal early warning and intelligent regulation and control, and is beneficial to improving the operation efficiency and management level of a water supply system.

The invention discloses a heterogeneous unmanned cluster collaborative hunting method, device and equipment and a storage medium, and relates to the technical field of agent automatic control, and the method comprises the steps: determining a heterogeneous unmanned cluster, environment information and constraint conditions of a multi-agent collaborative hunting system, and constructing a physical model of the multi-agent collaborative hunting system; at the beginning of each decision cycle, modeling a task allocation problem of multiple agents into a'maximum flow-minimum cost 'graph theory problem, constructing a task allocation directed graph, determining an individual capture target corresponding to each agent, and based on a PPO framework, taking the maximum return of the corresponding individual capture target completed by each agent as an individual target, and performing task allocation on each agent; and with the maximum return of all captured targets completed by the heterogeneous unmanned cluster as a global target, an intelligent agent is guided to learn an optimal hunting strategy. According to the method, the problem of obvious insufficiency in the aspect of processing heterogeneous capability constraints in the prior art is solved.

The invention relates to a river network grading system and method based on a graph theory and confluence cumulant, and belongs to the technical field of water conservancy projects and geographic information. Comprising the following steps: acquiring original DEM data and water system data, and converting the water system data to a preset projection coordinate system; extracting a primary river network based on the DEM data, and screening the primary river network in combination with the length of the river network and the area of the drainage basin to obtain a simplified river network; river network flow calculation: carrying out flow assignment on each river network element in the simplified river network based on the confluence cumulant; graph theory modeling: abstracting the simplified river network into a directed graph model; and river network grading: reverse tracing is carried out by taking the water outlet as a starting point, and the hierarchical relationship between the trunk and the branch is determined by combining the in-degree / out-degree and the edge weight of the node, so that river network grading is realized. According to the method, river network grading is upgraded from morphological description to hydrological function description, and the method is suitable for high-precision river network refined analysis, intelligent water conservancy emergency management and other scenes.

The invention discloses a new energy multi-mode fault diagnosis method based on dynamic graph convolution and transfer learning. According to the method, multiple types of sensors are deployed to collect operation, environment and historical data of new energy equipment, and a multi-modal data set is generated through preprocessing; the method comprises the following steps: defining an equipment component as a graph node, constructing a directed graph containing a time-varying edge weight by utilizing dynamic time warping and an attention mechanism, and updating a graph structure on line; a dual-branch dynamic graph convolutional network is adopted to extract multi-modal data features, an adversarial transfer learning algorithm and a meta-learning algorithm are combined, distribution of a source domain and a target domain is aligned, and a special diagnosis model is generated by using small samples. And fault classification is carried out, a fault component is positioned in combination with a graph node attention weight, and a fault evolution index is constructed by fusing a fault prediction probability and an attention weight change rate. The method effectively fuses multi-modal data, adapts to complex working conditions, improves small sample diagnosis precision, realizes fault accurate positioning and evolution prediction, and has important significance for guaranteeing stable operation of new energy equipment.

The invention provides a power distribution control cabinet fault response method and device, and relates to the technical field of electrical automation control, and the key points of the technical scheme are that a weighted directed graph model representing the relation of internal components of a power distribution control cabinet is constructed in advance; mapping component fault information predicted by the digital twin system to attributes of corresponding nodes in the weighted directed graph model; identifying a fault path or a fault cluster satisfying a predetermined condition; for each identified fault path or fault cluster, calculating a composite risk index according to the prediction probability of the nodes included in the fault path or fault cluster, the weight of the connection edge and the severity of the influence on the system; and generating early warning information according to the composite risk index. According to the power distribution control cabinet fault response method and device provided by the invention, a potential fault propagation path or a fault aggregation area can be identified based on physical, electrical and influence relationships among components, and a composite risk formed by a plurality of potential fault combinations is quantified, so that a preventive maintenance decision is guided.

The invention belongs to the technical field of flow instruments, and provides a dynamic compensation and error correction system of a high-precision flow instrument, which comprises a multi-dimensional data acquisition and preprocessing module, a module capable of being additionally provided with a sensor, a module for establishing a data stream with a timestamp and processing data, a module for fluid network modeling and priori knowledge construction, and a data processing module. A distributed collaborative optimization and parameter calibration module capable of constructing a directed graph model and generating initial compensation parameters; a network association reasoning and dynamic weight distribution module capable of adding a global penalty term to optimize parameters on the basis of a traditional error function; a working condition adaptation calibration and cross-domain parameter migration module capable of constructing an association graph, detecting abnormity and distributing weights; real-object-free calibration and parameter migration can be realized; according to the system, through multi-module cooperation, the problem that traditional single-table compensation is local and is not global is solved, and high precision and real-time performance of industrial-grade cooperative metering are supported.

The invention discloses a text generation method and device based on a large model, equipment and a storage medium, and relates to the technical field of natural language processing, and the method comprises the steps: carrying out the semantic analysis of a preset field related document based on an encoder of an initial large model, and generating a target concept set and a target embedding vector; constructing an initial weighted directed graph according to the target concept set and the target embedded vector, and expanding the initial weighted directed graph to obtain a target weighted directed graph; determining a target sub-graph meeting a preset optimal condition from the target weighted directed graph by utilizing a preset optimization algorithm, and obtaining a target structured text part set based on the target sub-graph; and performing fine tuning on the initial large model to obtain a fine-tuned large model, and converting the target structured text part set into a target text meeting a preset field text specification by using the fine-tuned large model. Content innovation can be realized while strict logic is ensured and requirements of professional fields are met.

The invention relates to a dynamic construction method and system for an integrated energy large model knowledge base. The method comprises the steps that a directed graph is constructed according to physical equipment or energy nodes on a target integrated energy system; performing knowledge association and filling on nodes and edges in the directed graph to form a node static knowledge set; accessing multi-source real-time data, and performing anomaly detection and state analysis on the multi-source real-time data to obtain a corresponding event; mapping the event into a directed graph, and updating the directed graph and the node static knowledge set to obtain an updated graph database and an updated vector knowledge base; and constructing an input prompt to the comprehensive energy field large model based on the query statement and the corresponding knowledge set to output an answer. According to the method, a dynamic knowledge base tightly coupling a physical world and an information world is constructed, the systematization degree, the cross-system analysis capability and the real-time performance of comprehensive energy system knowledge are remarkably improved, and powerful support is provided for deep application of a large model in scenes such as fault diagnosis and operation optimization.

The invention provides a power grid fault propagation path identification method and system based on a graph neural network, and relates to the technical field of power grid fault identification, and the method comprises the steps: obtaining a power grid topological structure and fault observation data, constructing a direction perception graph neural network, achieving the one-way message passing based on potential energy difference, and obtaining a node potential energy value and embedded representation; edges are screened to generate a directed graph, and candidate paths are generated in the potential energy gradient direction; and verifying the satisfaction degree of the graph neural network evaluation path to the physical constraint through an invariant, and finally selecting the path with the minimum topological entropy. According to the invention, the accuracy and reliability of fault propagation path identification are improved.

The invention relates to the technical field of process orchestration, and discloses an automatic orchestration system and implementation method based on event driving and context management, and the system comprises a model orchestration module which receives an external request through an OpenAPI, carries out the model packaging based on namespace attributes, and abstracts the workflow execution capability into a configurable model entity; the workflow engine module is used for providing a dynamic arrangement and execution mechanism based on a directed graph structure and carrying out unified context management; the parameter configuration module is used for constructing a global data constraint and management mechanism of a parameter-result double-layer model, and establishing a data mapping relation between an execution layer and a display layer of the process; and the data source management module uniformly manages and configures the external service capability units which can be called by the nodes in the workflow. According to the method, asynchronous, self-driven and distributed parallel execution of the workflow nodes can be realized, the operation efficiency in a high-concurrency scene is improved, and organic unification of performance, stability and expansibility is realized.

The invention discloses a power system carbon emission dynamic traceability and evaluation method, system and device based on life cycle and topological entropy fusion and a medium, and belongs to the technical field of power system low-carbon operation and carbon emission evaluation, and the method comprises the steps: collecting carbon emission data of different stages; establishing a stage set, calculating the emission of each stage, and solving a life cycle carbon entropy; abstracting a power system into a weighted directed graph, calculating a branch carbon flow rate based on the weighted directed graph, and calculating a graph theory carbon entropy based on node inflow; introducing a weight parameter, fusing the life cycle carbon entropy and the graph theory carbon entropy, and defining a fusion entropy; under a multi-period operation condition, calculating a time evolution sequence based on the fusion entropy, and defining a sensitivity coefficient; and generating a visual carbon flow map containing node carbon potential distribution, a branch carbon flow rate, a carbon entropy evolution curve and a responsibility allocation matrix. According to the method, dynamic conduction and node-level tracking of carbon emission along the electric power flow are realized, and the complexity and balance of carbon emission space distribution are quantified through an entropy theory.

The invention discloses a biomass pyrolytic reaction network prediction method based on a graph theory and quantum chemistry, which comprises the following steps: analyzing the molecular structure of a biomass initial reactant, abstracting the molecular structure into a directed graph molecular model, marking a potential conversion path after molecular mechanics optimization, generating a unique identifier by using an atom numbering rule and a Hash algorithm, and predicting the biomass pyrolytic reaction network. Functional groups are extracted and packaged into sub-graph modules, chemical bond parameters are obtained in combination with quantum chemistry calculation and are converted into fracture probabilities, basic reaction building blocks are predefined to traverse and mark breakable bonds, fragments are recombined and then de-weighted through Hash, and then reaction activation energy weights are given to side chemical bond fracture reactions; an energy optimal path is screened by means of a priority queue search algorithm, accurate analysis of a chemical bond fracture path and intermediate product conversion in the biomass pyrolysis reaction network is finally achieved, reaction path and product distribution are efficiently predicted, and the calculation efficiency and precision of a complex system are balanced.

The invention discloses a multi-type resource participation power grid frequency modulation method based on a consistency algorithm, and the method comprises the steps: firstly constructing an improved frequency response model fusing distributed optical storage equipment, hydrogen storage and energy storage equipment and a DC flexible load, identifying system parameters, solving a multi-resource comprehensive adjustment coefficient, constructing a directed graph model, employing a grouping consistency algorithm, and obtaining a multi-type resource participation power grid frequency modulation model; the optimal adjustment coefficient of each resource is solved through distributed optimization of an augmented Lagrange function and a consistency index, and primary frequency modulation of a multi-type resource collaborative response system is driven. According to the multi-type resource participation power grid frequency modulation method based on the consistency algorithm, by driving a multi-type resource collaborative response system to perform primary frequency modulation, collaborative regulation and control of light storage, hydrogen storage and flexible load are realized, economy and operation constraints are considered through distributed optimization, and communication complexity is reduced through a clustering iteration mechanism; and the frequency stability of the high-proportion new energy power grid is obviously improved.

The invention discloses a dynamic intelligent maintenance path planning method and system for a generator set, relates to the technical field of intelligent maintenance of power generation equipment, and solves the problems of insufficient dynamic quantification of potential safety hazards and low response efficiency of sudden working conditions in conventional maintenance planning of the generator set. The method comprises the steps that a constrained directed graph model is constructed based on a three-dimensional topological relation of equipment, nodes represent a to-be-overhauled part, and edges represent a feasible moving path; a temperature risk sensing mechanism is introduced, and the safety coefficient in the path edge weight is dynamically adjusted; and designing a hybrid optimization algorithm to solve the maintenance sequence with the lowest total cost. Project verification shows that the high-temperature operation risk can be effectively reduced, the construction period is shortened, and the operation and maintenance intelligence level of the generator set is remarkably improved.

The embodiment of the invention relates to the technical field of information processing, and discloses a reinforcement learning training method based on graphical user interface simulation, a system construction method, a system, equipment and a product, and the method comprises the steps: modeling a graphical user interface navigation graph into a directed graph, enabling nodes of the directed graph to represent the interface state of a graphical user interface, and enabling the nodes of the directed graph to represent the interface state of the graphical user interface; the edges of the directed graph are represented as state transition between the graphical user interfaces, and a navigation graph structure is generated by controlling topological parameters of the directed graph; rendering a graphical user interface according to the navigation map structure; driving the intelligent agent to perform graphical user interface operation by using the task target, and obtaining an operation action of the intelligent agent; evaluating the operation action and the task completion degree of the intelligent agent; and calculating a reward through the operation action and the task completion degree of the intelligent agent, and feeding back the reward to the intelligent agent. And an ideal experimental environment is provided for a methodology of constructing an intelligent agent based on a graphical user interface from zero.

The invention relates to the technical field of artificial intelligence, and particularly provides a multi-level main line diagram memory method for a large model. The method comprises the following steps: designing a memory graph, and organizing task semantic information in a dialogue by a directed graph structure; constructing a multi-level main line strategy; automatically creating nodes and updating the graph; constructing a dependency edge and an acceleration strategy; generating a structured prompt and context injection mechanism; according to the method, the key problems of memory decline, main line offset and context consistency deficiency of the large language model in complex multi-round dialogue tasks are solved, and the performance and reliability of the large language model are improved.

The invention discloses a smart course personalized resource label classification and recommendation system based on teaching practical experience, and relates to the technical field of smart education, and the system comprises a knowledge graph construction module, a resource labeling module, a user capability modeling module, a recommendation core module, a system interaction module and a parameter optimization module. The working process of the system is as follows: extracting a knowledge point set from a course standard, defining a relationship type between knowledge points, constructing a weighted directed graph, and calculating a shortest path distance between the knowledge points; marking a knowledge point set which is directly explained, a knowledge point set which needs to be mastered in advance, cognitive difficulty and a resource type for resources in the resource library; initializing a user knowledge state matrix, updating the mastery degree according to a test result, and calculating a current capability threshold value of the user; preliminarily screening a candidate resource set, calculating a knowledge migration intensity score, and grouping and sequencing according to resource types to generate a final recommendation list; and adjusting core parameters of a knowledge migration intensity algorithm according to user behavior data generated by a recommendation result.

The invention provides a fault root cause dynamic prediction method and system based on probability propagation reasoning, and relates to the technical field of fault positioning prediction, and the method comprises the steps: constructing a directed graph structure, associating each node with a dynamic fault probability, and constructing a fault state expression system; based on the fault state expression system, calculating and dynamically updating an edge weight, and constructing a dynamic propagation graph model; generating fault occurrence probabilities of the monitoring data, and fusing the fault occurrence probabilities to obtain aligned unified probability data; on the basis of a dynamic propagation graph model, on-graph probability propagation reasoning is carried out by using unified probability data, a final root cause is determined, and rapid fault positioning is realized; an intelligent self-healing and strategy optimization algorithm based on reinforcement learning is introduced, a repair strategy is automatically generated and executed, a decision is continuously optimized through online learning, and a self-evolution safety operation and maintenance system is formed. According to the invention, accurate and rapid prediction and positioning of system faults are realized.

The invention is suitable for the field of multi-modal data analysis and target identification, and provides a multi-modal time series data reasoning method based on a thinking chain, a three-layer reasoning state architecture is designed, hierarchical abstraction and time alignment of multi-modal data are realized through a gating circulation unit and a time modulation mechanism, and the multi-modal time series data reasoning efficiency is improved. Converting the multi-modal features into a unified time sequence semantic representation; designing a multi-step reasoning controller, and dynamically focusing key information and iteratively optimizing a reasoning state by adopting a modal exclusive attention mechanism and a gating circulation unit; in order to explicitly capture a cross-modal and cross-time complex dependency relationship, multi-modal time sequence dependent graph structure modeling is carried out, multi-modal time sequence data is modeled into a directed graph, and structured information transmission is realized through a graph neural network; according to the method, deep understanding and accurate prediction of military target behaviors are realized, the reasoning process can be visually explained through state evolution, attention weight, graph structure dependency and the like, and reliable technical support is provided for intelligent decision making in a complex scene.

Simulation and validation of neural network systems is provided. In various embodiments, a description of an artificial neural network is read. A directed graph is constructed comprising a plurality of edges and a plurality of nodes, each of the plurality of edges corresponding to a queue and each of the plurality of nodes corresponding to a computing function of the neural network system. A graph state is updated over a plurality of time steps according to the description of the neural network, the graph state being defined by the contents of each of the plurality of queues. Each of a plurality of assertions is tested at each of the plurality of time steps, each of the plurality of assertions being a function of a subset of the graph state. Invalidity of the neural network system is indicated for each violation of one of the plurality of assertions.

Embodiments of the present application provide a task scheduling method and device, a target directed graph to be detected is obtained; a change type of the target directed graph is matched based on task change information and a preset acyclic constraint condition, to obtain a first detection result; if the first detection result represents that the change type does not match the preset acyclic constraint condition, then the task nodes to be detected are detected based on the task change information and a preset cyclic detection method, to obtain a second detection result representing whether the target directed graph is a directed acyclic graph; in the directed graph detection process, first, whether the change type of the target directed graph conforms to the preset acyclic constraint condition is identified, if it conforms, then it can be directly determined that the target directed graph is a directed acyclic graph; if it does not conform, then part of the task nodes in the target directed graph are detected based on the preset cyclic detection method, to identify whether the target directed graph has a cycle, which can improve the detection efficiency of the directed graph, to ensure that the directed graph used in task scheduling is acyclic.

The invention relates to the crossing field of water management and artificial intelligence. The method disclosed by the invention solves the problems of difficulty in coordination of multi-subject benefit conflicts, insufficient utilization of a basin spatial topological structure, separation of physical constraints and decision-making processes, limitation of adaptive ability and the like in the prior art. The method is characterized by comprising the steps of abstracting a drainage basin into a directed graph structure; generating a node embedding vector by using a physically guided graph attention network, and explicitly introducing a physical constraint factor in an attention mechanism; configuring an intelligent agent for each sub-basin to perform distributed strategy learning; cooperative training is carried out by adopting a multi-agent depth deterministic strategy gradient algorithm, fair distribution is guided by a global reward function in a Nash product form, and physical constraints such as water balance and the like are ensured in combination with a local reward function containing a nonlinear physical penalty term. According to the method, distributed, physically consistent and self-adaptive optimal configuration of drainage basin water resources is realized, and the method is mainly used for improving fairness, efficiency and feasibility of water resource distribution.

The invention provides a circuit energy efficiency automatic optimization method. The method comprises the following steps: firstly, modeling a circuit gate-level netlist into a directed graph; extracting a key path by using a static time sequence analysis tool, and extracting a context sensing key sub-graph containing the path and a first-order neighborhood node of the path; thirdly, feature coding is conducted on the key sub-graphs through a graph neural network, and state vectors representing the local state of the circuit are generated; the state vector is input to a reinforcement learning policy network to decide a drive capability adjustment action for a particular standard cell. And the system modifies the netlist according to the action, calls the static time sequence analysis tool again to evaluate the modified performance, power consumption and area indexes, and generates a reward signal to iteratively optimize the strategy network. By constructing the closed-loop optimization process, the problems of low manual optimization efficiency and unbalanced power consumption and area balance are solved, automatic and intelligent collaborative optimization of circuit energy efficiency is realized, and the design quality and efficiency are remarkably improved.

The invention provides a knowledge graph cleaning and quality evaluation system, which relates to the technical field of data processing, and comprises a data processing module for obtaining standardized data; the initial alignment module is used for obtaining isomorphic entity clusters and heterogeneous entities; the final alignment module is used for constructing a heterogeneous knowledge graph based on heterogeneous entities and training a graph neural network model by using a dual loss function to obtain a heterogeneous entity cluster; the knowledge completion module is used for performing completion operation on the predicted embedded vector and the initial triple to obtain a final triple; and the quality evaluation module is used for calculating the directed graph model and evaluating the knowledge graph by taking a calculation result as an evaluation index. According to the method, one-stop processing of knowledge cleaning, completion and quality evaluation can be realized, and compared with a traditional method, the method has the advantages that the knowledge cleaning efficiency and the quality control level are improved, excellent migration adaptation capability is shown in multiple scenes, and the method can be flexibly applied to different business fields.

A computing system including one or more processing devices configured to receive a computational workflow specification. The computational workflow specification includes a plurality of workflow stages. The plurality of workflow stages include one or more unfilled workflow stages that each include a respective workflow stage objective, a respective workflow stage exit criterion, and one or more fillable fields. The computational workflow specification further includes a directed graph structure in which the plurality of workflow stages are arranged. Based at least in part on the computational workflow specification, the one or more processing devices are further configured to compute a filled computational workflow including one or more filled workflow stages. Computing the filled computational workflow includes computing respective filled values of the one or more fillable fields at least in part at a generative language model. The one or more processing devices are further configured to execute the filled computational workflow.

The application discloses a kind of chart representation method and its application supporting observation transmission and computing resource joint scheduling, mainly solve the problem that the prior art uses observation and transmission independent scheduling, transmission and computing independent scheduling, leading to the long time delay of ground observation task completion.Its implementation scheme includes: given the satellite network directed graph including multiple satellite nodes and the connection edge between node.According to its construction observation subgraph and two communication subgraphs;Observation subgraph is connected through observation satellite node between first communication subgraph;First communication subgraph is connected through computing satellite node between second communication subgraph, constitute graph representation model, so that observation, transmission and computing can be characterized in the same graph, support multi-dimensional resource joint scheduling.The application can map observation, transmission and computing joint scheduling problem from complex mathematical programming problem to routing problem in graph, reduce the complexity of problem solving, guarantee low latency demand, can be used for satellite network ground observation task.

The embodiment of the invention provides a method, device and equipment for determining transaction risk assessment attributes of an object, and the method comprises the steps: obtaining transaction data of a transaction object, and constructing a directed graph; determining a first low-order feature according to a first edge weight corresponding to the node in the first sub-graph and a second edge weight corresponding to the node in the directed graph, and determining a second low-order feature according to the total number of closed loops and the number of sub closed loops; determining neighbor node aggregation features according to the directed graph and the basic features; determining a high-order feature based on the initial node feature, the neighbor node initial feature and the neighbor node aggregation feature; and inputting the first low-order feature, the second low-order feature, the high-order feature and the basic feature into a transaction risk assessment attribute determination model to determine a transaction risk assessment attribute. According to the technical scheme of the embodiment of the invention, the transaction risk assessment attribute of the transaction object is determined based on the constructed directed graph and the multi-dimensional features, the risk account is effectively identified and assessed, and the accuracy of transaction assessment is improved.

The embodiment of the invention provides a real-time dynamic path planning method and device, equipment and a storage medium, and relates to the technical field of path planning. According to the method, after feature vectors of a plurality of nodes are obtained, new graph structure data can be constructed according to the feature vectors. And calculating an access path sequence based on the new graph structure data, and performing closed-loop structure detection on the access path sequence to determine a directed closed loop. And performing pruning operation on the directed closed loop according to the node out-degree of the directed closed loop to obtain an optimal planning path. According to the method, a directed edge structure is dynamically generated by introducing a feature vector similarity relationship between nodes, and a closed-loop detection and pruning mechanism is added to realize dynamic construction of a directed graph based on node feature similarity driving, so that invalid cycles are accurately identified and avoided, and the path planning efficiency is improved. According to the method, the graph structure can be dynamically adjusted according to the environment change, so that the continuity and the stability of the path planning process are improved.

The invention discloses a file code similarity analysis method, which is based on a directed graph isomorphism theory and comprises the following steps of: A, extracting characteristics of an open source code file, namely a Call Graph (Call Graph for short), and establishing a file sample library; b, extracting a function call relation graph of the file to be analyzed; c, executing the standardization work of the graph, and preprocessing the graph according to the related definition of the tree structure; d, extracting the maximum common subgraph of the function call relation graph of the file to be analyzed and the sample library file; and E, calculating a relationship between the maximum common subgraph and the function call relationship graph to obtain a similar result, and completing file code similarity analysis. According to the method, the similarity of the file codes is analyzed by depending on the function call relation graph, the internal characteristics and higher-level logic characteristics of the file codes are considered, and higher accuracy and efficiency are achieved. The method is suitable for occasions such as source code plagiarism detection.