498 results about "Dependency graph" patented technology

The invention relates to the technical field of computing power scheduling, and discloses a heterogeneous resource computing power intelligent scheduling method and system. According to the method, real-time state monitoring is conducted on heterogeneous computing resources, and resource state parameters such as the computing unit utilization rate and the memory occupancy rate are obtained; task attributes and user request parameters of the task queue are collected, historical task data are processed based on the genetic algorithm optimization model to execute task demand prediction, and predicted demand parameters are generated. A dependency graph containing resource unit nodes and communication link roadsides is constructed through a resource topology analysis tool, predicted demand parameters are input into a scheduling priority classifier trained by a graph neural network, and an actual scheduling priority is identified. And executing resource conflict prediction based on the priority, inputting task feature vectors into a conflict resolution module of a fuzzy logic decision maker, outputting actual conflict resolution parameters, and finally integrating to generate a scheduling scheme containing a resource allocation sequence and an execution time table.

The invention belongs to the field of natural language processing, and provides a task complexity driven graph semantic multi-agent collaborative decision-making method and system.The task complexity driven graph semantic multi-agent collaborative decision-making method comprises the steps that a task text is obtained and subjected to semantic coding to obtain a task semantic vector, evaluation is conducted based on the task semantic vector to obtain a complexity vector, and a task complexity score of the complexity vector is calculated; the task semantic vector and the complexity vector are fused to obtain a task representation vector, an agent capability relation graph is constructed, the participation probability of each agent node is obtained according to the task representation vector and the agent capability relation graph, and a dynamic agent combination scheme is formed; and performing task decomposition according to the agent combination scheme, constructing a sub-task dependency graph, scheduling the execution sequence of the sub-tasks through topological sorting, realizing cooperative execution of the agents, and generating a task result. According to the method, precise matching and efficient cooperation of the agent combination are realized, and the capability of processing complex tasks and the resource utilization efficiency of the multi-agent system are remarkably improved.

The invention relates to the technical field of automatic generation, in particular to a knowledge graph driven teaching material automatic generation method and system. The method comprises the following steps: receiving teaching target input, analyzing the teaching target input into a multi-layer structure based on a preset teaching intention meta-model, and mapping the multi-layer structure to a knowledge graph node; generating an optimal teaching path through a rule engine in combination with the node topology position and the teaching intention level; after the topological fingerprint of the target textbook system is recognized, compatibility analysis is executed, and if a structural gap exists, a transition module is automatically inserted to reconstruct a path; calling the bound multi-modal content unit according to the reconstruction path node, and completing modal coordination according to a cross-modal dependency graph; and finally, performing structured rendering on the integrated content, and outputting a textbook body adaptive to the terminal. The method has the advantages of teaching intention deep understanding, multi-modal content consistency control and cross-textbook system adaptation, and the intelligence level and generalization ability of textbook generation are improved.

A method is provided for optimizing regression testing in a software development environment. The method includes analyzing source code to create a structural dependency graph that maps dependencies between code elements; identifying changes in the source code between a current version and a previous version; mapping the identified changes onto the structural dependency graph to determine affected code elements; selecting a subset of regression tests based on the affected code elements identified in the dependency graph; and executing the selected subset of regression tests to validate the changes in the source code.

A system for translating source code in a first programming language to a target language is provided. The system is configured to receive source code for converting to target code; determine an abstract syntax tree from the source code; determine program specifications from the source code; determine a dependency graph from the source code; determine a plurality of chunks based at least in part on the abstract syntax tree, the program specifications, and the dependency graph; determine a plurality of converted chunks based at least in part on the plurality of chunks and a deep learning model, the deep learning model converting the plurality of chunks from the language of the source code to the language of the target code; post-process the plurality of converted chunks to obtain intermediate code; and provide the intermediate code as the target code.

The invention relates to the technical field of micro-service operation and maintenance, and discloses a full-link intelligent fault simulation and assessment defense method in a micro-service scene. The method comprises the following steps: constructing a dynamic dependency graph based on a service registration center and real-time communication traffic; performing matching backtracking on historical faults according to the atlas, and generating a fault injection point list and a propagation path set sensed by the atlas; in the isolated environment, driving the programmable agent to perform multi-dimensional fault injection, and synchronously acquiring full-amount system response signals; performing multi-dimensional deviation calculation on the signal and the base line to form an observation record containing deviation intensity and propagation rate; and training a graph neural network model with time sequence dependence understanding capability based on the record, and carrying out fault mode identification and evolution prediction on an online real-time link, and outputting a risk assessment report. According to the method, the authenticity of fault simulation and the initiative of risk prediction are improved.

The invention discloses a multi-agent task arrangement method and system, and relates to the technical field of artificial intelligence. In the method, firstly, a task demand is obtained, key information and intention of the task demand are extracted, and task semantics are obtained; secondly, based on the task semantics and the business standard process template, obtaining a business standard process template matched with the task demand; then, according to the matched business standard process template and task semantics, a task context is constructed, and a domain specific language DSL is generated through reasoning according to the task context; thirdly, the DSL is analyzed, a task dependency graph is constructed according to the DSL analysis result, and the task dependency graph is converted into BPMN process model data; and finally, sub-task scheduling and execution of the sub-agents are carried out according to the BPMN process model data. According to the method provided by the invention, the dependence on professional developers or process engineers can be reduced, the response and planning time of complex tasks can be shortened, the result predictability can be improved, and the reasoning cost can be obviously reduced.

The invention discloses a data auditing method based on big data, and relates to the technical field of big data, and the method comprises the steps: obtaining multi-modal data in a big data platform form, extracting features of the multi-modal data to generate a feature set, creating a rule base based on the feature set, and constructing a cross-form dependency graph according to the feature set and the rule base; cascade auditing is executed through a cross-form dependency map, when auditing fails, an affected field is reversely positioned according to the connection direction of the map, and incremental state rollback and re-auditing are executed on the affected field; when it is detected that the high-frequency data is changed, an anti-rollback storm mechanism is started, a cascade load index is obtained, and a rollback instruction set is generated through cascade blocking based on the index; and executing distributed fault-tolerant control, constructing a node collaborative protection system through cascade loads, and outputting a fault-tolerant operation instruction.

The invention relates to a signal equipment fault diagnosis rule automatic generation method, system and equipment, and the method comprises the steps: a demand cleaning processing process: carrying out the semantic analysis of an original fault diagnosis demand of signal equipment, and generating a structured output containing a demand pseudo code and a dependency graph based on a recognition result of the demand; the demand searching process is used for constructing a demand vector and a knowledge graph; performing similarity search on the vectorized query demand to obtain similar demand nodes for a user to select a root demand, and performing deep search in the knowledge graph based on the root demand to generate a fault tree structure and fault tree data; and an intelligent rule automatic generation process: generating prompt words based on the fault tree data construction rule, and generating a signal equipment fault diagnosis rule code by using a large language model. Compared with the prior art, the method has the advantages that the signal equipment fault diagnosis rule is automatically generated, and the development efficiency is improved.

The invention discloses an agent task scheduling planning method, and relates to the technical field of agent scheduling. The method comprises the steps of analyzing a task instruction to generate atomic tasks capable of being independently executed, constructing a subtask dependency graph, and defining association constraints between the tasks; determining the real-time resource occupancy state of the intelligent agent to obtain a resource state tensor, completing resource-task association anchoring and dependency priority ranking in combination with the sub-task dependency graph, and generating a task priority sequence with resource constraint; performing dynamic capability matching and predictive load balancing calculation on the sequence through a task-agent adaptation model, and determining a target execution agent of each atomic task; and based on the target execution agent and the subtask dependency graph, performing time sequence scheduling arrangement and conflict resolution, and generating a collaborative execution scheme. The method improves the reasonability and efficiency of agent task scheduling, reduces resource conflicts and execution timeout risks, and is suitable for agent cluster collaborative scheduling in a complex scene.

The invention provides an application environment and version management method and system suitable for a low-code platform, and relates to the technical field of platform management.The application environment and version management method comprises the steps that a directed dependency graph is constructed by obtaining change records, and a dependency loop is detected and intelligently decomposed through a depth-first traversal algorithm; and packaging the node clusters into micro-services based on a function relevancy model to form an acyclic directed dependency graph, constructing a version backtracking index and a difference analysis matrix to determine a change influence range, executing incremental deployment according to a topological sequence, and recording deployment information. According to the method, the problem of version management and environment dependence in a low-code platform is solved, and the application deployment efficiency and the system stability are improved.

The invention belongs to the technical field of artificial intelligence and multimedia processing, and discloses a multi-modal content conflict resolution method based on modal time sequence dependence modeling, which comprises the following steps of: constructing a modal representation vector by unifying time, space, semantics and priority information of modeling modal contents; and further structuring and quantifying complex dependency and conflict relationships among different modal contents by utilizing a modal time sequence dependency graph, and constructing a context relationship by combining a graph neural network, so that the system can identify conflicts of the contents in time, space and semantic levels, and realizes self-adaptive content reordering and display configuration based on conflict scores. According to the method, the limitation of traditional single-dimension conflict detection is overcome, the accuracy rate and the coverage range of conflict identification are improved, the method can be widely applied to multi-modal content-intensive scenes such as intelligent media generation, AI broadcasting, virtual navigation and XR interaction, information shielding and semantic redundancy are reduced, and the interaction experience of users and the system content quality are enhanced.

The invention provides a syntactic structure tree-based code change influence intelligent analysis method and system, and the method comprises the steps: extracting a control flow, a data flow and a call flow according to an AST generated through the analysis of an original code and a changed code, and constructing a static dependency graph SDG containing a multi-dimensional dependency relationship; the method comprises the steps that a base layer is used for comparing AST differences before and after code change and generating grammatical structure change feature vectors; the service layer matches a corresponding service label for the changed AST node by using the service domain template; the change labeling layer constructs a cross-version dependency chain for the change AST nodes and generates historical feature vectors; and calculating a risk value of changing the AST node by utilizing a feature calculation result and combining with a multi-dimensional influence diffusion algorithm, and automatically generating a decision suggestion. According to the method, multiple dimensions of a control flow, a data flow and a call flow are deepened, risk prediction is optimized by utilizing natural language processing and historical change data, and the influence brought by code change is assisted to be better understood and managed.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a root cause analysis method and device based on a space-time dependency graph, equipment and a medium. Comprising the steps of constructing a space-time dependency graph, generating a diagnosis path blueprint, identifying a fault source and a root cause entity type, generating a graph query statement, executing query and performing cause and effect verification, and outputting a root cause analysis report. And the causal reasoning and root cause positioning of the system state change are realized by fusing the graph structure information and the natural language processing capability. Through cooperative processing of a language model and a graph data structure, fault symptom information and system structured state data are deeply fused, a path is generated in the graph structure, and a causal relationship is verified, so that the ability of understanding a complex system state evolution chain is improved, and accurate identification and diagnosis of root causes are realized. And the accuracy and the automation level of root cause analysis are obviously enhanced.

The present invention relates to systems and methods for proactive real-time anomaly detection in cross-environment RPC (Remote Procedure Call) communications within computing systems. Utilizing an Intelligent GraphRPC Method, this invention integrates advanced graph analysis techniques to enhance fault detection and workflow management. The method features a dual-graph approach, employing both real-time and aggregated dependency graphs, which allows for continuous monitoring and analysis of RPC interactions to detect and prevent unauthorized or misconfigured RPC calls between staging and production environments. An ingestion pipeline further supports the system by aggregating and archiving call graph data, providing beneficial insights into service dependencies and potential security risks. This proactive anomaly detection system is designed to seamlessly integrate into existing monitoring and alerting frameworks, providing a robust solution to safeguard data integrity and operational stability, thereby minimizing losses and reputational damage due to data breaches and system disruptions.

The invention provides an intelligent task scheduling and optimizing method and system, a medium and equipment, and the method comprises the steps: analyzing task metadata through a database storage process, generating a task dependency graph according to an obtained task dependency matrix, and converting the task dependency graph into a visual interaction interface; identifying a key link with longest time consumption and a bottleneck task on the key link through the created time prediction model, and generating a multi-dimensional optimization suggestion based on an identification result; automatically adjusting a task scheduling script according to the multi-dimensional optimization suggestion and generating a standardized configuration file, dynamically allocating resources according to the standardized configuration file, and monitoring an execution state of a task through a fault-tolerant mechanism; and performing intelligent early warning according to the task log data collected in real time and the system performance index, and generating a multi-dimensional analysis report. The system is an intelligent scheduling system integrating dependency analysis, link optimization and execution monitoring, and automatic analysis and optimization of a complex task network are achieved.

The invention provides a method and equipment for constructing a text entity recognition model based on large model data enhancement. The method comprises the following steps: firstly, constructing an initial model comprising a preprocessing unit, a syntax dependency analysis unit, a data correction enhancement unit, a context coding unit, a syntax enhancement unit, an expression fusion unit and a sequence decoding processing unit; preprocessing the training sample to obtain a preprocessed text, and establishing a preliminary dependency graph through syntactic analysis; correcting and enhancing the text and the dependency graph by the large language model to obtain a text sequence and a dependency graph for subsequent use; encoding the text sequence to obtain an initial representation vector, and enhancing the vector in combination with the dependency graph; after fusion, decoding and outputting a prediction label containing a lexical entity label; and calculating loss by using a function containing conditional random field structure loss, judging convergence, and if not, updating parameters and continuing training until a target model is obtained. According to the method, data are optimized and expanded by means of a large language model, syntactic dependency enhancement features are combined, and the recognition capability of the complex text named entities is improved.

The invention relates to the technical field of quantum computing, and discloses a quantum circuit mapping method and system based on deep reinforcement learning, according to the method, an original logic quantum circuit is analyzed and simplified into a ZX-graph, then the ZX-graph is converted into a quantum circuit dependency graph, the computing complexity is remarkably reduced, and the quantum circuit mapping efficiency is improved. A large-scale quantum circuit mapping task can be processed within reasonable time, a Markov decision process environment is constructed through a quantum circuit dependency graph and quantum chip parameters, a deep reinforcement learning agent is trained by using the Markov decision process environment, and an optimal mapping strategy optimization model is obtained. Through the optimal mapping strategy optimization model, the corresponding optimal quantum bit mapping scheme can be output to the current to-be-mapped logic line, so that the method can be adapted to quantum chips of various topological structures, and the generalization ability and adaptability of the mapping scheme are improved.

The invention provides a code quality adaptive evaluation and optimization method, system and device and a medium, and belongs to the technical field of computers. The method comprises the following steps: performing redundant information cleaning and standardization processing on an original code; selecting an adaptive parser to convert the processed code into an abstract syntax tree, and optimizing the abstract syntax tree; performing embedding processing on each node of the abstract syntax tree by using a pre-trained deep learning model to generate a node semantic vector, and performing aggregation, feature extraction and vector normalization operation to obtain a semantic vector representing code semantic features; extracting a dependency relationship from the abstract syntax tree to construct a program dependency graph, extracting node features by using a neural network model, and generating a dependency relationship analysis result; based on the semantic vector and the dependency analysis result, identifying a code potential problem by using an optimization algorithm and generating an optimization suggestion; codes are automatically modified according to optimization suggestions, the optimization effect is re-evaluated, and related models and algorithms are iteratively optimized according to feedback information.

The invention relates to the technical field of industrial data processing, in particular to a model-driven FlinkSQL (Structured Query Language) dynamic optimization method, which comprises the following steps of: generating candidate SQL (Structured Query Language); carrying out parallel acquisition and noise reduction processing; constructing a causal dependency graph and embedding the graph; constructing a spatio-temporal feature vector and performing steady-state judgment; performing disturbance sandbox verification and stability judgment; a fault-tolerant strategy is injected, and DML is compiled and produced; and deploying tasks and monitoring and updating in real time. Physical signals of industrial equipment are stripped through a wavelet noise reduction technology, then operation indexes of the Flink platform are subjected to structured expression in combination with causal dependency graph embedding, quantitative classification is performed on current operation and ideal steady state, robustness is verified through multiple cycles, adaptive parallelism and a fusing strategy are injected into SQL, and the method has the advantages of being high in robustness and high in reliability. And the reference base point is automatically updated according to the monitoring result, so that the problems of low SQL execution efficiency, insufficient stability and difficulty in adapting to complex industrial scenes caused by neglecting environment change and system load fluctuation during operation are effectively solved.

The invention discloses a test method, device and equipment and a computer readable storage medium, which are applied to the technical field of computers, and comprise the following steps: obtaining multi-source data of a distributed storage cluster, the multi-source data comprising hardware state data, service associated operation data and business dependence data; constructing a relation dependency graph among the hardware nodes, the service nodes and the service nodes based on the multi-source data; adjusting the chaos test script according to the real-time state of the distributed storage cluster and the dynamic adjustment strategy to obtain a target chaos test script; and executing the target chaos test script, determining a fault range and a recovery mechanism based on the relation dependency graph, and outputting a test report. According to the method, comprehensive and accurate presentation of the dependency relationship of each node is realized, and the chaos test script can adapt to the dynamic change of the cluster, so that the test scene is more practical, the invalid test is avoided, the effectiveness, reliability and efficiency of the cluster chaos test are remarkably improved, and the high availability and service continuity of the cluster are powerfully guaranteed.

The invention discloses a project process management system fusing text analysis and progress tracking, and the system comprises the following modules: a dual-channel guide fusion module which is used for extracting a context semantic vector and a structure intention vector from a preprocessed text sequence, and generating a fusion representation for task element recognition; the self-adaptive weight module is used for carrying out adjustable aggregation and comprehensive association degree calculation on the multi-source semantic features; the edge weight self-adaptive sparsification processing module is used for performing edge weight screening and structure optimization on an edge set in the initial inter-task association graph and outputting a sparsified task dependency graph; the cross-layer state sensing module is used for adjusting a node style and an edge state to reflect a global execution trend; and the path rearrangement module is used for triggering key path analysis and structure reconstruction logic to form a new path structure. According to the invention, semantic analysis and graph modeling are fused, and an intelligent linkage project process management system is constructed.

The invention relates to a risk behavior early warning method and device based on adaptive time sequence slicing, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: collecting user operation behavior data aiming at each service in a service system, and obtaining user information and service characteristics of each service in the service system; generating a service activeness index; adjusting the length of a time interval between time sequence slices of the user operation behavior data; according to the adjusted time sequence slices, integrating the user operation behavior data, generating a user behavior sequence, and constructing a user behavior dependency graph; generating a normal behavior pattern of the user according to the time sequence slice intervals of different granularities, the user behavior sequence and the user behavior dependency graph; and according to the normal behavior mode, carrying out risk behavior early warning on the user actual operation behavior of each service in the service system. By adopting the method, the risk behavior prediction accuracy can be improved.

The invention discloses an intelligent agent system and a control method thereof, and relates to the technical field of natural language processing. The system comprises an execution layer, a planning layer and an auditing layer, the planning layer identifies and extracts innovation task meta-information, constructs a task tree to complete operations such as hierarchical clustering, and determines a task execution path; the execution layer calculates the semantic similarity between the content of the knowledge graph and the user innovation question, determines the optimal question and answer, extracts heuristic information, and decomposes the heuristic information into sub-questions to construct a dependency graph; generating an answer set based on the graph solving sub-problems, and integrating answers by using a genetic algorithm to obtain an overall solution; and the auditing layer performs multi-dimensional scoring on the scheme, and determines a structured scheme report and optimization suggestions for users to use according to a scoring result. The method has the capabilities of structured reasoning, problem recursive decomposition and scheme closed-loop optimization, can generate a multi-dimensional evaluation result, and efficiently realizes systematic modeling of a clear path for analogy heuristic information support problem solution.

The invention belongs to the technical field of artificial intelligence, and provides a monthly settlement task intelligent scheduling method and system based on reinforcement learning, and the technical scheme is that a time sequence state vector is constructed and obtained based on obtained monthly settlement task multi-dimensional state feature data; encoding the time sequence state vector to obtain time sequence features, constructing a task dependency graph, extracting global features of the task dependency graph, fusing the time sequence features and the global features of the task dependency graph to obtain fusion features, and generating action probability distribution based on the fusion features; based on the action probability distribution, introducing a hard constraint and a soft constraint, constructing a multi-target reward function, optimizing the reinforcement learning agent based on a near-end strategy optimization algorithm, and outputting an action strategy based on the optimized reinforcement learning agent; and converting the optimized reinforcement learning agent output action strategy into a specific monthly settlement task scheduling instruction. And dynamic, self-adaptive and global optimization scheduling of the complex financial monthly settlement process is realized.

The invention relates to the technical field of constructional engineering informatization and data security, and particularly discloses a BIM and block chain-based whole-process engineering cost data collaborative management system, which comprises a data analysis and dependency graph construction module, which is used for analyzing BIM model component attributes and association relationships and constructing a directed dependency graph; the loop dependency detection and resolution module is used for identifying and resolving a loop dependency structure in the graph; the topological sorting processing module is used for generating an ordered calculation sequence of the components; the cryptographic abstract calculation module is used for generating a unique abstract value of each component in sequence; the global abstract generation module is used for aggregating and generating a global cryptographic abstract representing the overall logic state of the model; and the block chain evidence storage module is used for combining the global abstract and the related metadata to perform uplink evidence storage.

Architectures and techniques are described that can generate or receive a dependency graph of a microservices platform. The dependency graph can be constructed based on run time operation of microservices such that the nodes of the graph can represent microservices and the edges can represent the interactions during run time. The dependency graph along with an anomaly pattern can be embedded into an embedding space, and based on an examination of the embedding space, it can be determined whether the anomaly pattern exists in the dependency graph.

The invention discloses a multi-dimensional data asynchronous calculation method and system based on a dynamic dependency graph, and relates to the field of data processing. The method comprises the following steps: constructing a metadata, logic and instance three-layer separation storage model; analyzing the reference relationship to construct a directed acyclic graph, and generating a calculation priority; monitoring data change, and executing asynchronous serialization calculation through a message queue based on priority; the associated document is automatically updated based on anchor mapping. According to the method, the calculation deadlock and the performance bottleneck of large-scale data in the Web environment are solved, logic decoupling and dynamic expansion are realized, the final consistency of the data is guaranteed, and the high-concurrency throughput and the stability are remarkably improved.

The invention discloses a method and a system for constructing a multi-agent collaborative dynamic industrial knowledge graph. The method comprises the following steps: constructing a federal mode registration center to register and manage a cross-domain mode dependency relationship; based on the dependency relationship, accurately pushing the mode change proposal submitted by the subsystem to the influenced subsystem; the influenced subsystem simulates in the sandbox and generates a quantized cross-domain influence evaluation report; the federal mode registration center collects the report and performs automatic conflict detection and multi-round negotiation type digestion; and after the change proposal is passed or the conflict is solved, performing synchronous versioning deployment. By establishing the cross-domain mode dependency graph, sandbox simulation evaluation and a multi-round negotiation resolution mechanism, a structured solution process is provided for cross-system mode conflicts, global transparency, influence controllability and conflict solvability of the industrial knowledge graph in the dynamic construction process are ensured, and the dynamic construction efficiency of the industrial knowledge graph is improved. An isolated knowledge graph is converted into a unified knowledge system which works cooperatively and is high in adaptability.

The invention discloses a verification platform code checking method and device, electronic equipment and a storage medium, and relates to the technical field of chip verification, and the method comprises the steps: obtaining a to-be-checked and verified platform code, and determining a target checking rule configuration file based on the to-be-checked and verified platform code; analyzing the to-be-checked and verified platform code to obtain an abstract syntax tree corresponding to the to-be-checked and verified platform code; based on the abstract syntax tree, generating a dependency graph of components in the to-be-checked and verified platform code; and based on the target check rule configuration file, the abstract syntax tree and the dependency graph, performing static check on the to-be-checked and verified platform code to obtain an error check report of the to-be-checked and verified platform code. The technical problem of low inspection efficiency caused by lack of customized inspection processes and inspection rules in related technologies is solved, and the technical effect of improving the inspection efficiency is achieved.