317 results about "Dependency graph" patented technology

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.

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 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 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 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 an intelligent decision data inquiry system and method based on a generative large model, and belongs to the technical field of artificial intelligence. Natural language inquiry input by a user is received, and an inquiry intention and a target field are extracted; extracting structured and unstructured cost data from the real estate project database, and constructing a candidate field set; performing semantic matching by utilizing the generative large model to generate an intention field mapping matrix; constructing a field dependency graph based on a mapping result, and extracting a minimum closed-loop field set; performing credibility evaluation on the fields, and constructing a weighted field set; generating a dynamic query template, outputting a natural language answer, and attaching a field tracing path; if the field is missing or the credibility is insufficient, automatically generating a reverse query until a credibility requirement is met; according to the method, automatic association and accurate question and answer between complex semantic query and heterogeneous cost data are realized, and the method has good interpretability, traceability and intelligent interaction capability.

The invention discloses an edge computing node collaborative task unloading method for guaranteeing a low-delay service, and relates to the field of edge computing, each edge computing node generates a collaborative view comprising the edge computing node and a neighbor edge computing node through a local LSTM prediction model and federated learning, and the collaborative view comprises a predicted resource state and a predicted network state; according to the invention, the local LSTM prediction model and federated learning are combined to generate the collaborative view, so that accurate prediction and global information sharing of edge node resources and network states are realized, and the accuracy of decision making is improved; the tasks are analyzed into a dependency graph with key path marks, so that priority scheduling of the key tasks is ensured, and the overall task time delay is reduced; based on a weighted voting consensus mechanism of node credibility and resource adequacy, the efficiency and reliability of decision consensus among nodes are improved; the task unloading efficiency and the service quality of the low-delay service are effectively improved, and the stability and the reliability of the edge computing system are enhanced.

The invention provides a test case generation method and device, equipment, a storage medium and a program product, and relates to the technical field of software testing. The method comprises the following steps: constructing a dependency graph comprising nodes represented by API functions and edges representing a dependency relationship between two connected API functions according to an API specification document; determining a target scene rule matched with the received test task, and performing random walk on the dependency graph according to a scene preference weight provided by the target scene rule and used for representing the degree of correlation between the API function and the target scene rule to obtain an API call sequence; and generating a test case for testing the GPU driver according to the API call sequence. According to the method, the target scene rule matched with the test task is determined, and the scene preference weight which is provided by the target scene rule and represents the correlation degree of the API function and the target scene is utilized to guide random walk on the dependency graph constructed based on the API specification document, so that the generated API calling sequence better meets the requirements of the test scene.

The invention provides an intelligent test data generation and verification method and system based on a hierarchical architecture, and relates to the technical field of data generation, and the method comprises the steps: constructing a cross-layer constraint dependency graph by analyzing each layer of data mode and constraint and conversion logic of a hierarchical architecture software system; generating candidate test data meeting the constraint based on the dependency graph in a back-stepping manner, and verifying the candidate test data through projection; and iteratively optimizing the dependency graph and the test data by monitoring the difference between the actual execution and the prediction form. According to the method, cross-layer data consistency verification is realized, the test coverage rate and the defect detection efficiency are improved, and the quality risk of a software system is reduced.

The invention relates to the technical field of operation and maintenance monitoring, and discloses a multi-service system automatic centralized monitoring management method and system. According to the method, monitoring data streams of a multi-service system are captured in real time through a distributed data acquisition agent, and noise filtering and format standardization processing are carried out to generate a standardized monitoring data sequence; automatically identifying monitoring elements in the service system by adopting a pattern recognition algorithm based on the normalized sequence, and packaging each element into a monitoring entity; dynamically generating a monitoring strategy rule according to the characteristics of the monitoring entity and the association relationship, and storing the monitoring strategy rule to a monitoring strategy knowledge graph; analyzing a logic relationship of strategy rules in the knowledge graph, and constructing a monitoring strategy dependency graph by using a graph calculation engine; performing structural integrity verification and performance optimization on the dependency graph to ensure the performability of the dependency graph; deploying a monitoring executor to a target business system according to the optimized dependency graph to start an automatic monitoring process; and collecting data returned by the monitoring actuator in real time to generate a monitoring analysis report.

The invention relates to the technical field of software engineering and automatic testing, in particular to an E2E scene test optimization method and system based on target man-machine cooperation. According to the scheme, an optimization target is manually set, an initial test case and a business process are input, and a test resource library is constructed; the system automatically analyzes a service path, generates a use case dependency graph and a key path, and determines a core scene through manual verification; a candidate optimization strategy is generated by the system, and a target strategy is determined through manual screening according to resources and priorities; the system carries out simplification, merging and scheduling optimization on the use cases, and deploys and executes the use cases after manual sampling verification of coverage; the system collects execution data in real time and generates an evaluation report, and whether a use case is supplemented or a strategy is adjusted is determined after manual analysis; and finally updating the resource library and the strategy model, establishing a dynamic feedback mechanism, and triggering continuous optimization during system iteration. According to the method, the E2E test efficiency and the software delivery quality are remarkably improved through man-machine cooperation and closed-loop optimization.

The large-model-based data security risk automatic research and judgment processing system comprises a fusion research and judgment module which is used for carrying out comprehensive research and judgment on the risk level, attack intention and potential influence of a security event based on security data in combination with deep learning and a large-scale language model, and transmitting a research and judgment conclusion to an automatic processing module; the business process disassembling and analyzing module is used for automatically discovering and modeling based on the security data through process mining and flow analysis, perceiving a business process and a dependency graph associated with a security event in real time, evaluating potential influences of different disposal measures on business continuity, and transmitting business influence evaluation to the automatic disposal module; and the automatic disposal module is used for selecting and executing an optimal risk disposal strategy from the security strategy library by adopting a distributed architecture of an AI intelligent agent according to the research and judgment conclusion and the business influence evaluation. The accuracy of alarm study and judgment is improved, 'alarm fatigue 'is relieved, and the method has high elasticity, high fault tolerance and strong cooperative capability.

The invention relates to the technical field of prefabricated part modeling, in particular to an interaction system for prefabricated part steel bar parameter driving design and a modularization implementation method. Incrementally updating the engine; the model application module is used for acting the change patch sequence on the three-dimensional reinforcement model in a transaction mode and driving display refreshing; the system further comprises a rule base module and an interpretable verification module. The rule base module stores a steel bar arrangement rule and / or a standard verification rule in a constraint declaration form; the interpretable verification module is used for performing compliance verification on the three-dimensional reinforcement model or the derived quantity thereof based on the evidence data field, and outputting a violation item list; the interactive interface comprises parameter panels and a violation interactive area, and the parameter panels are displayed in groups according to component type parameters, geometric parameters, steel bar parameters and specification options and support interactive modification. According to the method, the script dependency is reduced, the dependency graph increment patch is updated in real time, the automatic specification matching and positioning are realized, and the low-cost expansion is governed by the plug-in.

The invention relates to the technical field of software testing, and discloses an agent-based multi-dimensional test sample automatic generation method and system.The agent-based multi-dimensional test sample automatic generation method comprises the steps that code changes are captured, interface definitions are extracted, and multi-source data are fused for consistency verification; constructing a multi-dimensional dependency graph and analyzing the change influence to obtain an associated interface list; generating a multi-dimensional test scene; performing parameter traceability and consistency derivation, and initializing test data and environment to obtain a complete test data set; generating a use case code, and performing hierarchical optimization and executable verification; test arrangement optimization and automatic execution are carried out, and faults are monitored and diagnosed in real time; carrying out multi-dimensional analysis and defect identification, dynamically evaluating the value of a test case and optimizing a test strategy to obtain a comprehensive test report; according to the invention, full-process automation of test case generation is realized, the test efficiency and quality are improved, and the test maintenance cost is reduced.

The invention discloses a multi-agent dynamic diagnosis method and system for a vehicle-mounted software system, and relates to the technical field of software diagnosis. Collecting multi-source operation data of the vehicle-mounted software in real time; performing document analysis and skill processing on the internal technical document of the original equipment manufacturer to obtain an executable skill set; constructing a dynamic dependency graph based on the multi-source operation data and the executable skill set; obtaining a current fault prompt, calling a multi-skill collaborative agent, substituting multi-source operation data and the fault prompt into the dynamic dependency graph, arranging action layer operation in an executable skill set through the agent, executing log analysis, dependency chain verification, abnormal link identification and fault scene restoration, and outputting abnormal associated data; based on the dynamic dependency graph, the abnormal associated data, the rule layer information of the executable skill set and the historical fault cases, root cause reasoning is executed to obtain an executable troubleshooting scheme, and the fault diagnosis accuracy and efficiency of the vehicle-mounted software system are improved.

The embodiment of the invention discloses a network task migration method and device, electronic equipment and a medium, and relates to the technical field of task scheduling, and one specific embodiment of the method comprises the following steps: determining first migration data of a first task migration request and second migration data of a second task migration request; determining a first migration path set of the first task migration request and a second migration path set of the second task migration request based on the first migration data and the second migration data; determining that a bandwidth migration conflict relationship exists between the first task migration request and the second task migration request under the condition that an overlapped path exists in the first migration path set and the second migration path set; and based on the bandwidth migration conflict relationship, constructing a migration dependency graph to perform task migration request scheduling according to the migration dependency graph. Efficient and low-conflict network task migration scheduling is realized, and the flexibility, the stability and the resource utilization rate of network task migration are remarkably improved.

The invention discloses an electronic design multi-agent system with graph structure control and feedback iteration capabilities and an operation method thereof. The electronic design multi-agent system comprises a natural language analysis module, a decision planning agent, a functional module agent set, a schematic diagram construction agent, an assembly agent, a simulation verification agent and a feedback iteration module. The system receives a circuit design requirement described by a natural language, automatically constructs a sub-task dependency graph, distributes parallel tasks, retrieves matched components and generates a local schematic diagram through a multi-agent cooperation mechanism, and assembles the local schematic diagram into a complete schematic diagram. The system automatically triggers task re-planning, device re-matching and graph structure optimization through simulation verification and error feedback to form closed-loop iterative control. According to the method, automatic conversion from the natural language to the schematic diagram conforming to the electrical specification is achieved, the problems that a traditional EDA tool is complex in operation and existing AI aided design lacks systematicness and self-optimization capacity are solved, the design threshold is remarkably lowered, and the design efficiency and quality are improved.

The invention provides an employee work assessment recommendation method and system based on a collaborative filtering algorithm, and relates to the technical field of employee work assessment, and the method comprises the steps: extracting a task dependency relationship and time consumption data from a project management system, and constructing an employee task dependency graph; secondly, collecting and analyzing work interaction information containing document operation and task marking features, and identifying task switching critical points to generate quantitative indexes; constructing a similar employee group and extracting an efficient working mode by means of a collaborative filtering algorithm after establishing an evaluation data set through space-time alignment of the two; by taking the task dependency graph as an environment state and the efficient mode as a reference, generating a task sequence optimization scheme by using a reinforcement learning model; according to the scheme, the staff group composition is adjusted according to the scheme execution effect, the optimization model is fed back, finally, a comprehensive evaluation result is generated based on the scheme and actual task sequence matching degree and the quantitative index stability, the task sequence optimization and physiological state adjustment ability can be accurately evaluated, and the work assessment scientificity and the task execution efficiency are improved.

The invention relates to a task processing method and device, equipment, a medium and a product. The method comprises the following steps: receiving a code change request, wherein the code change request carries code change information and a code change type; generating a task dependency graph of the code change request based on the code change information and the code change type; analyzing a dependency relationship among the tasks in the task dependency graph, and generating an execution strategy corresponding to each task; and for each task, determining a target agent according to an execution strategy corresponding to the task under the condition that the task is determined to meet an execution condition based on the execution strategy corresponding to the task, and executing the task by using the target agent. By adopting the method, the task processing efficiency can be improved through a dynamic task arrangement mechanism in a complex multi-task and multi-environment parallel construction scene.

The invention discloses a micro-service system fault positioning method and system based on multi-modal data fusion. The method comprises the following steps: converting data into a uniform abnormal event format; constructing a micro-service instance dependency graph, and performing data enhancement; extracting three modal features respectively corresponding to the log, the measurement index and the call chain tracking; the method comprises the following steps: firstly, calculating an initial fusion weight of each modal, then calculating an interaction weight, carrying out weighted average on each modal feature, weighting a result after local fusion again, and generating a global feature vector; and inputting the global feature vector into a classifier, calculating the probability that the micro-service instance is the root cause of the fault, and outputting a sorting list. According to the method, a more comprehensive system view is provided, the limitation of a single data source is avoided, the training data is effectively expanded, the robustness and generalization ability of the model in a marked data scarce scene are improved, the contribution degree of each mode can be adaptively adjusted according to a fault scene, and the fused features are more discriminative.

The invention relates to a deep learning task resource allocation method and device, equipment and a medium. The method comprises the following steps: respectively analyzing a computational graph structure and a historical resource monitoring log corresponding to a deep learning task, and generating a tensor dependency graph and a resource use time sequence matrix; performing time-varying demand prediction on the basis of the matrix, generating a time-phased resource constraint table, performing memory allocation processing on the basis of a tensor dependency graph, and generating a tensor memory partitioning scheme and an inter-partition communication cost matrix; and performing static resource pre-allocation based on the time-phased resource constraint table and the tensor memory partitioning scheme, generating pre-allocated resource configuration, and performing resource scheduling and outputting real-time resource configuration through a deep reinforcement learning model according to the time-phased resource constraint table, the inter-partition communication cost matrix and the pre-allocated resource configuration. According to the method, by means of dynamic resource allocation, cross-partition communication cost optimization, reinforcement learning optimization and the like, the resource utilization rate and task execution efficiency of a deep learning task are remarkably improved.

The invention provides a vulnerability discovery method and system based on symbol-semantic hybrid reasoning, and belongs to the technical field of software security. The method comprises the steps that a target program is analyzed, and an event sequence is extracted; converting the event into a symbol with a time sequence label and confidence, and constructing a symbol dependency graph; reasoning based on the dependency graph, calling a large language model to generate a semantic reasoning action when the certainty is insufficient, and updating the state after symbol verification; new rules are extracted from the inference chain passing verification through reinforcement learning, and self-evolution is achieved; and outputting a vulnerability report containing the reasoning path and the confidence coefficient. The system correspondingly comprises a time sequence probability symbol module, a symbol-semantic bidirectional coupling reasoning module, a reinforcement learning self-evolution module and a report generation module. According to the method, the preciseness of symbol logic and the generalization ability of a semantic model are fused, the accuracy, interpretability and adaptive ability of vulnerability detection are effectively improved, and the method is suitable for security audit and code review of a complex software system.

The invention relates to a context delay switching method and system based on an embedded operating system, and belongs to the technical field of embedded operating systems. According to the method, a dependency graph is constructed, a dependency state is analyzed, judgment and adjustment are carried out, conflict-free tasks are screened, sorting is carried out, and switching is executed; according to the method, the limitation that traditional static scheduling cannot adapt to task dependency relationship changes during operation is solved by constructing the dependency relationship graph and collecting and analyzing the dependency strength and the execution state between the tasks in real time, and in a complex embedded scene, the correlation degree between the tasks is quantified through a dependency strength formula, and the real-time state analysis is combined, so that the real-time state analysis is realized. The task switching sequence can be dynamically adjusted, it is ensured that key tasks are executed according to the dependency sequence, execution blocking or data errors caused by the fact that front tasks are not completed are avoided, switching of the tasks which do not meet dependency can be flexibly suspended, conflict-free tasks are executed, and therefore the real-time response capacity and the operation stability are improved.

A method for managing an event includes: obtaining a dependency graph including a plurality of nodes representing operational elements of the event and a plurality of edges representing relationships between the operational elements; generating, for each of the plurality of nodes, a risk vector and a readiness vector based on an analytic routine; and performing a graph updating routine, wherein the graph updating routine includes: applying a modification to a first set of nodes of the plurality of nodes; identifying a subgraph of the dependency graph based on the modification, wherein the subgraph includes a second set of nodes of the plurality of nodes; generating a modified dependency graph by updating, for each node of the second set of nodes, the risk vector and the readiness vector; and displaying the modified dependency graph.

The disclosed technology includes generating, based on a configuration file, a dependency graph representing a production environment. The dependency graph may contain nodes and edges. The structure of the dependency graph is based on relationships identified between the applications of the production environment. Historical incident data that includes historical data related to past production environment disruptions may be obtained, parsed, and used to generate weights for the nodes or edges of the dependency graph. The generated weights may be assigned to the dependency graph. A potential update to the production environment may be obtained. An impact score for the potential update may be generated by utilizing the weights of the dependency graph. An alert may be transmitted to a computing device when the impact score is above a threshold value.

The invention discloses a domain knowledge content generation method and system based on retrieval enhancement generation, and belongs to the technical field of natural language processing and knowledge generation. Performing fuzzy semantic retrieval in the structured knowledge base to obtain semantic matching entries and calculate semantic relevancy; constructing a semantic fusion representation vector to form an enhanced representation set; performing context alignment with the query vector, and extracting a causal path fragment with the maximum weight; inputting the causal path and the query vector into a large language model, and executing a condition generation operation; analyzing and judging whether the generated content has term jump or logic chain scission or not based on the term dependency graph, if so, adjusting a context window and regenerating, and finally outputting professional content meeting dependency consistency; according to the method, the accuracy and logic consistency of professional field text generation are effectively improved, and the method is suitable for knowledge generation tasks in complex semantic scenes.

This invention discloses a method and system for predicting faults in conferencing equipment with end-to-end awareness. The method includes: constructing a device dependency graph, comprising a main graph and at least two subgraphs; real-time acquisition of multi-dimensional operational status time-series data of each device node, and extraction of degradation feature sets; fusion embedding encoding of each device node using a link fault coding model to obtain a fusion embedding vector, the model being constructed based on a graph attention network and a causal temporal convolutional network; and, based on the fusion embedding vector, prediction of the fault probability and remaining lifespan of each device node using a multi-head task predictor, and determination of the warning level. This invention solves the technical problem of existing technologies being unable to accurately predict cross-link cascading faults based on quantitatively modeling the coupling dependencies between devices by using hierarchical spatial reasoning of the device dependency graph and adaptive fusion of multi-modal temporal degradation features.

The invention relates to the technical field of automatic generation of low-code components, and discloses an automatic component generation method for a low-code platform. The method comprises the following steps: acquiring and standardizing an original interaction event stream of a user on a canvas to form a user operation chain; and scanning and marking a composite operation unit conforming to a predefined structure on the operation chain, and carrying out dynamic weight distribution according to the spatial layout and the time decay attribute of the composite operation unit. And combining the similar units with similar weights to form candidate component logic blocks. And extracting an interface element dependency relationship of the logic block to construct a dependency graph, and calculating cohesiveness measurement of the logic block based on graph node characteristics. Bridging operations connecting different logic blocks are identified and analyzed, and coupling metrics therebetween are calculated. And fusing cohesiveness and coupling measurement to generate a componentized evaluation matrix. And analyzing the matrix and driving a code template engine, and automatically synthesizing a final visual component. According to the invention, intelligent generation from user interaction to high-quality, high-cohesion and low-coupling components is realized.

The invention provides a collaborative development process data synchronization and notification method. According to the method, an automatic process is triggered by monitoring a test-submitted event message queue, development data is positioned based on module identification, key operation features (such as database change and task start and stop) are analyzed and extracted by using natural language, and feature extraction precision is improved by combining a dynamic keyword library and cross-modal fusion; the timeliness and reliability of risk reminding are ensured through template message generation and multi-channel delivery; a risk propagation range is further predicted by means of a dependency graph model, and a closed-loop early warning mechanism is formed. According to the method, the problem of delay caused by information lagging and insufficient collaboration in the development process is solved, the risk of manual operation is reduced, and the efficiency and controllability of project management are improved.