2066 results about "Goal node" patented technology

The invention discloses an intelligent resource scheduling method and system based on a dynamic data consanguinity atlas, and relates to the technical field of resource scheduling, the method comprises the following steps: collecting execution logs and flow metadata of tasks in a computing platform in real time, and constructing a dynamic directed weighted consanguinity atlas; calculating a blood relationship influence coefficient of each node in the dynamic directed weighted blood relationship map; obtaining a to-be-scheduled task, and calculating a comprehensive priority score based on the dependency weight of the dynamic blood relationship map of the to-be-scheduled task, the real-time load state of the target node and the scheduling execution time delay; performing priority ranking on the to-be-scheduled tasks based on the comprehensive priority score, and allocating cluster resources; and predicting the load trend of the target node, and triggering a migration decision when detecting that the predicted load of the target node exceeds a threshold value and the weight ratio of the key consanguinity tasks borne by the target node exceeds a preset threshold value. Through a dynamic consanguinity map and an intelligent scheduling algorithm, high efficiency and fairness of resource allocation are realized, the cluster utilization rate is improved, and task delay is reduced.

The invention discloses an agent platform resource management method and device based on a cloud native architecture and a medium, and the method comprises the steps: packaging an agent application into an independent container instance based on a containerization technology, and deploying the container instance to a target node; acquiring task demand information of the intelligent agent in real time, and generating a dynamic scheduling scheme by combining the resource state data and through a multi-target optimization algorithm so as to allocate the task to a target container instance; according to a matching function of the capability vector of the intelligent agent and the task demand vector, calculating the integrating degree of the intelligent agent and the task so as to generate a collaborative decision-making result and issue the collaborative decision-making result to the target intelligent agent; the resource utilization rate and the task execution state of the intelligent agent are monitored, an elastic telescoping mechanism or task rescheduling is triggered according to feedback data monitored in real time, and a resource allocation strategy is dynamically adjusted.

The invention relates to the technical field of distributed computing, and discloses a distributed computing power scheduling method and system based on dynamic load balancing. The method comprises the following steps: acquiring real-time resource state data of a plurality of nodes in the distributed system and demand characteristics of a task to be allocated, generating a dynamic task allocation strategy, allocating the task to a target node for execution, monitoring a resource state of the target node in real time, and triggering task reallocation when the resource state deviates from a preset threshold value. The system comprises a monitoring acquisition module, a task analysis module, a strategy generation module, a scheduling execution module and a resource abstraction module. According to the method, tasks and node resources can be accurately matched, the resource utilization rate is improved, the real-time performance and reliability of the tasks are guaranteed, the overall performance of a distributed system is optimized through heterogeneous resource unified scheduling and a resource reserving and recycling mechanism, and the method is suitable for various distributed computing scenes.

The invention relates to a network attack tracing method and device based on a threat graph, equipment and a medium. The method comprises the steps of obtaining network security log data from multiple security data sources, and performing standardization processing to obtain a structured network security event data set; extracting threat entities and behavior relationships among the threat entities from the structured network security event data set to obtain an entity set and a relationship set; constructing a threat map according to the entity set and the relationship set, and performing time data slicing according to the timestamp to obtain a map snapshot and map metadata; based on a predefined attack chain template, identifying an attack chain sub-graph conforming to an attack behavior structure in the threat graph to obtain an attack chain set and a path reachability matrix; and according to the attack chain set and the path reachability matrix, attack path inversion is carried out by taking the target node as an end point, and an attack traceability path and a graph evolution process display result are obtained. By adopting the method, the network attack path can be identified and the attack source can be traced.

Systems and methods are disclosed for condition identification. One or more processors may receive a member data object with indicators and dimensions, access a member-specific graph network with nodes representing attributes and weighted edges indicating associations, modify the nodes and edges based on the member data object, generate a multi-modal graph database by combining the modified member-specific graph network and a disease graph network, apply the multi-modal graph database to an attention-based graph neural network (GNN) that identifies associations between nodes by dynamically allocating attention weights to edges, generate an embedding data object with node identifiers and vectors representing features and relationships, select a target node associated with condition data, apply the embedding data object to a classification layer that outputs predicted conditions for the target node, and generate the probability of predicted conditions appearing in the target node.

The invention relates to the technical field of industrial control, in particular to a robot scheduling method and system based on cooperative control, and the method comprises the steps: obtaining a task proportion and target node information in a task adjustment scheme, judging an optimal task distribution path and an optimal execution sequence according to a task priority order and a node adaptation rule, and carrying out the task scheduling according to the optimal task distribution path and the optimal execution sequence; determining an inspection task distribution blueprint; according to the routing inspection task distribution blueprint, the tasks are distributed in real time by adopting a low-delay scheduling framework, and a dynamic distribution map and real-time distribution update data of the tasks among the nodes are obtained according to node response delay and communication overhead among the nodes; the latest load state of each node is obtained through a dynamic distribution map and real-time distribution update data, and whether a potential calculation pressure overload risk exists or not is judged according to a distribution uniformity index and distribution anomaly detection. The invention aims to solve the problems of large performance difference, unbalanced load and low task scheduling efficiency in a multi-robot collaborative inspection process in a narrow space.

The invention relates to the technical field of space governance, and discloses a multi-agent space collaborative governance method which comprises the following steps: processing multi-source heterogeneous data such as satellite images and sensor readings, establishing cross-type semantic association through a geographic space data embedding technology, generating a unified structured text after optimizing the satellite images through vLLM, and synchronizing the unified structured text to a central database; an agent role portrait is dynamically generated by the central server large language model based on a preset Prompt template, and generation does not depend on a fixed rule; then, selecting a target node in the edge-center architecture, disassembling a total task into sub-tasks, establishing semantic mapping, calculating a matching probability, and performing optimal distribution by a reward borrowing function; generating a governance scheme in a perception layer-decision layer-execution layer framework, and outputting a coded operation instruction; based on an execution feedback updating strategy, a multi-level mechanism is set, roles are automatically redistributed, and the governance continuity is guaranteed. According to the invention, the overall efficiency and reliability of space governance can be improved in the face of dynamic scenes or emergency situations.

The invention discloses an edge computing node dynamic load balancing method based on a multi-agent system, and relates to the technical field of node load balancing. According to the edge computing node dynamic load balancing method based on the multi-agent system, the current computing power communication data and the operation environment data of each edge computing node are collected, the operation load index of each node is analyzed, and whether an overload node exists or not is judged; if an overload node exists, a plurality of nodes adjacent to the overload node are obtained, a task migration candidate set is screened out, finally, a target node is determined from the candidate set, and task migration operation is executed; the operation load index of the edge computing node is dynamically quantified by collecting and comprehensively analyzing multiple parameters such as the CPU utilization rate, the memory occupancy rate, the communication delay, the temperature rise rate, the I / O blocking rate, the power consumption fluctuation rate, the cache write-in waiting time and the vibration disturbance amplitude in real time. Compared with a traditional single index or fixed threshold mode, the method can identify the node overload state more accurately and timely.

Provided in the present disclosure are an information processing method, an electronic device, and a storage medium, which are applied to the technical field of artificial intelligence. The method comprises: determining information to be processed corresponding to an intelligent agent; and inputting the information to be processed and node sequence information into a large model, wherein the node sequence information is used for indicating a plurality of nodes included in a task flow and edges between the nodes, each node represents an action instruction for the intelligent agent, and each edge represents a transition between the nodes, such that the large model determines, on the basis of the information to be processed, a target node to be executed and controls the intelligent agent to execute the action instruction corresponding to the target node. The present disclosure enables the planning of a complex flow and controllability of an execution process, allowing the intelligent agent to complete tasks more efficiently and accurately. Furthermore, the present disclosure can support the customization of different task flows for different scenarios, thereby meeting usage requirements in the different scenarios and improving user experience.

The invention provides a distributed data calculation engine scheduling and resource dynamic optimization method and system, and relates to the technical field of distributed data, and the method comprises the steps: receiving a distributed calculation task, determining a resource demand parameter, constructing a multi-level state space based on historical execution data, and carrying out the iterative optimization of a dynamic reward mechanism, and selecting a target node group and constructing a network topology structure, analyzing and generating a data transmission path and a bandwidth allocation scheme through a graph neural network, executing task allocation and carrying out resource load monitoring. According to the invention, the utilization rate of computing resources is improved, the data transmission delay is reduced, and intelligent resource scheduling and optimization in a distributed computing environment are realized.

The invention discloses a multi-modal data retrieval method and device, a storage medium and computer equipment. The method comprises the following steps: collecting multi-modal original data; on the basis of description information of metadata of original data, all metadata belonging to the same associated items and logic relations among all the metadata are obtained, a metadata chain is constructed, a distributed graph database is constructed on the basis of the metadata chain, the metadata chain is expressed in the distributed graph database in the form of a graph, the graph comprises nodes and edges, the nodes represent the metadata, and the edges represent the metadata. The edge represents a logical relationship between the metadata; and when a data retrieval instruction is received, traversing each node in the metadata chain along the logical relationship of the metadata chain in the distributed graph database, obtaining a target node matched with a data retrieval requirement corresponding to the data retrieval instruction, and returning original data corresponding to metadata represented by the target node. Multi-modal data dynamic association retrieval can be realized, and cross-modal information mining efficiency and accuracy are improved.

The invention relates to a task and communication combined path planning method for a multi-base unmanned system. The method comprises the following steps: mapping task distribution into a target node layer, and constructing a communication link into a transmission link layer to form a task and communication double graph; establishing a dynamic coupling edge between the graphs to represent the influence of a task execution behavior on the communication quality; an interactive influence propagation model is introduced in the time dimension, and the feedback effect of communication link fluctuation on the task progress is simulated; providing time-space dynamic cost evaluation input for path planning; defining a task feasible domain and a communication strong connectivity domain in a time-space domain based on the double-layer graph output by the technology; constructing a joint feasibility domain (JFE), only allowing path planning to be expanded in the domain, and processing a splitting problem of communication repair after the path planning; performing multi-target compression on the path, and balancing task path time consumption, node connection stability and path redundancy consumption indexes; a graph homotopy simplification mechanism is introduced, and redundant edge paths in the JFE are compressed.

The invention provides a one-way isolation network intelligent data analysis method and device based on a large model and a medium, and belongs to the technical field of data processing and network security. Matching an accessible node area list according to a preset organization structure level; the system receives and analyzes the instruction to determine a query intention and an associated data range; the system converts a natural language query instruction into a cross-node SQL statement through a large model module, and verifies the legality of the SQL statement based on user permission; and the system unidirectionally transmits the encrypted SQL instruction to a target node area through a hardware optical shutter, triggers the node area to execute local statistical operation and returns a structured result. Dynamic data pulling in a one-way network environment is achieved through a hardware optical shutter and data pump technology, natural language query is converted into SQL in real time in combination with distributed query optimized by a large model, and the problems that data statistics is lagged and user operation intervention is needed in a traditional isolation network are solved.

The present application discloses a database query method and apparatus, an electronic device, and a non-volatile storage medium. The method comprises: determining a knowledge graph corresponding to a database to be queried, wherein the knowledge graph is used for representing a logical structure and an association relationship of data in said database; determining similarity scores between user question text and graph nodes in the knowledge graph, and determining a target node from among the graph nodes of the knowledge graph on the basis of the similarity scores, wherein the similarity scores are used for representing the degree of association between the graph nodes and the user question text; and on the basis of the target node, generating database schema information corresponding to said database, and using a large language model to generate, on the basis of the database schema information, a structured query language statement corresponding to the user question text.

The invention discloses a distributed computing task non-perception migration method and system under optical fiber network interruption. The distributed computing task non-perception migration method comprises the following steps: constructing a distributed task migration system and an output system architecture; collecting network state indexes, constructing a network topological graph, and analyzing the state of an optical fiber link for fault prediction; obtaining a calculation task operation state, establishing a sub-task mapping relation, constructing a directed dependency graph, and generating a state snapshot; analyzing resource requirements, reserving standby resources, and deploying a distributed cache system; analyzing a fault influence range, extracting an influenced calculation sub-graph, and selecting a migration target node; in a target node preloading environment, reconstructing an execution context, and redirecting a communication path; and setting a data change capture mechanism, synchronizing incremental data and executing consistency verification. According to the method, non-perception migration of the computing tasks is realized, task continuity and data consistency are guaranteed, and the reliability of the distributed computing system in an optical fiber network fault scene is improved.

The present application relates to the technical field of network fault diagnosis, and discloses a network fault diagnosis method, an electronic device, a storage medium, and a program product. The method comprises: acquiring fault diagnosis trigger information, the fault diagnosis trigger information comprising a resource name and a fault identifier; determining resource instance information corresponding to the resource name, a target node matching the fault identifier in a fault knowledge graph, and at least one potential cause node having a causal relationship with the target node; and invoking an executable diagnosis interface corresponding to each potential cause node to execute target resource instance information corresponding to each potential cause node, and when it is verified that a potential cause corresponding to the potential cause node is established, and the node type of the potential cause node is a preset root cause node, acquiring a fault diagnosis result corresponding to the potential cause node. In this way, the accuracy and credibility of fault diagnosis can be improved, and complex and accurate fault diagnosis tasks can be implemented.

The invention provides an unmanned aerial vehicle ad hoc network transmission and calculation integrated resource scheduling method based on task driving, and the method comprises the steps: building a multi-dimensional resource pool model which comprises the communication bandwidth, calculation resources and storage resources of an unmanned aerial vehicle, and collecting the resource state vector of each unmanned aerial vehicle node in real time; a dynamic topology sensing network is constructed, link duration is predicted through relative motion speed between unmanned aerial vehicle nodes, and a network structure chart with weights is generated; constructing a decision model based on a fusion architecture of a preset message passing neural network and a deep reinforcement learning network, and inputting the network topology features of the network structure chart and the resource state vector into the decision model; and outputting an optimal scheduling strategy including target node selection and multi-hop path planning through the decision model, and maximizing system benefits while meeting constraints of tasks on communication and computing resource quality. The problems that existing unmanned aerial vehicle networking communication is high in time delay, low in reliability and difficult to calculate and maximize utilization of resources are solved.

The invention relates to the technical field of distributed processing, and provides a data processing method based on a distributed network, and the method comprises the steps: collecting a dynamic feature set and a real-time resource index set of an input data flow in real time; inputting the dynamic feature set and the real-time resource index set into a pre-trained autonomous optimization model, and generating a joint optimized data fragmentation strategy matrix and a task allocation weight vector; splitting the to-be-processed task into a plurality of sub-tasks with an execution order relationship according to the data dependency graph, and dynamically mapping each sub-task to a specified storage fragment of the target node according to the weight vector; in the subtask execution process, each node continuously exchanges execution state data, and dynamically adjusts the task execution path weight of adjacent nodes according to the progress deviation rate and the resource margin in the state data so as to form a closed loop feedback optimization link; and according to the continuously updated real-time resource index set and the progress deviation rate, triggering a fragment migration transaction and a fault recovery assembly line, and carrying out strategy iteration updating.

The invention discloses a heterogeneous computing power resource dynamic cooperative scheduling method and system and a computer device. The method comprises the steps of obtaining multi-dimensional feature information of a to-be-scheduled task, current state information of each node in a heterogeneous computing power resource pool and scheduling environment state information; training a time sequence model by adopting historical associated data, inputting the multi-dimensional feature information, the current state information and the scheduling environment state information into the trained model to obtain associated trend information in a future preset time window, and then combining the multi-dimensional feature information and the current state information of all the nodes to obtain the scheduling environment state information. A multi-objective optimization function fusing task delay, cost consumption and energy consumption is constructed, the function is optimized based on a preset strategy, a target decision strategy is obtained, a scheduling instruction set is generated after analysis, allocation information and target nodes are determined according to the scheduling instruction set, and tasks are issued. According to the method, dynamic collaborative scheduling of heterogeneous computing power resources can be realized, and the overall utilization efficiency of the resources is accurately, efficiently and effectively improved.

The invention discloses an AI model distribution and deployment system and method oriented to cloud edge collaboration, belongs to the technical field of artificial intelligence and edge computing crossing, and aims to solve the problems of low AI model distribution efficiency and poor deployment adaptability in a cloud edge collaboration scene. The method comprises the steps of collecting multi-dimensional attribute data of edge nodes and converting the data into multi-level capability tags, and triggering tag differentiation updating according to data change amplitude; constructing a model demand hierarchical description framework by relying on capability labels, converting distribution demands into quantitative query conditions, and adjusting and screening a target edge node set through a three-level progressive matching mechanism in combination with dynamic weight; coding data blocks corresponding to redundancy are generated based on the network state of a target node, adaptive cache nodes are screened by using a distributed cache mechanism, the data blocks are pre-stored, and a cache network is established and transmitted to the target node; according to the invention, efficient distribution and accurate deployment of the AI model in the cloud edge collaborative environment are realized, and the model transmission reliability and the node adaptability are improved.

The invention discloses an electromechanical equipment distributed control system based on the Internet of Things, which belongs to the technical field of distributed control, and specifically comprises the following steps: splitting a control task into task units, registration dependencies, data entries and task levels at an edge gateway, and generating a candidate scheduling range according to registration items; a calibration heartbeat with a reference segment is issued, and a driving node executes and returns a measurement record in an idle window for calibrating hardware fingerprints and link overhead and dynamically updating matching parameters; when computing power saturation is triggered, splitting a low-level task unit into suspensible fragments, setting a de-duplication execution identifier, opening a state interface, and completing migration check; selecting a target control node and a transmission path according to a calibration result, issuing fragment and state interface information, driving a target node to continue fragments according to an execution template, and guiding a source node to release occupied resources in sequence; and checking and comparing the execution result, and when the rule is triggered, recovering to the previous stable state according to the mapping rollback table.

The invention discloses a dominant path identification method and system in a multi-source scene based on a perturbation response model, and relates to the technical field of computers. According to the method, the influence of the link state change on the steady state of the system is dynamically analyzed based on a perturbation response mode instead of depending on a static index, the method is closer to a real interaction mechanism of the network, and the mechanism modeling precision is improved. By traversing the link state, the actual contribution of each link to the response of the target node is quantified, and the complex interaction of weak connection and multi-link coordination is prevented from being ignored. Through the process that the system is evolved to the steady state again after disturbance, flow distribution and competition at the position of a shared node are naturally modeled instead of depending on local optimal addressing, the global reasonability of dominant path recognition is improved, and the problem of multi-source target resource competition is solved. The method is suitable for various networks, and has a core support effect on construction of a propagation risk early warning model based on network science, implementation of a key node intervention strategy and optimization of an intelligent governance mechanism of a social information system.

The invention discloses a heterogeneous resource scheduling method and device of a cloud data center, a medium and a product, and relates to the technical field of cloud computing, and the method comprises the following steps: receiving a scheduling task of an application instance containing source architecture resource information; screening the processing nodes according to the computing power demand of the application instance and the specification reference values of the processing nodes, and determining candidate nodes according to the screening result; constructing an application performance portrait of the application instance according to the performance data of the application instance; according to the application performance portrait and the scheduling task, equivalent resource configuration corresponding to deployment of the application instance on the candidate node is determined, a score is determined based on the equivalent resource configuration and the specification reference value, and a target node is determined based on the score; and modifying the resource request of the application instance based on the equivalent resource configuration to obtain a modified resource request, and deploying the application instance to the target node, so that the target node performs resource configuration based on the modified resource request to complete heterogeneous resource scheduling. And equivalence quantization and intelligent scheduling of heterogeneous resources are realized.

The invention discloses a heterogeneous GPU resource management scheduling method, a computer device, a medium and a product. The method comprises the following steps: acquiring computing power resources of each node, including a GPU model, a GPU video memory, a GPU number, a computing power segmentation scheme and a GPU use condition; automatically segmenting the heterogeneous GPU of each node according to the computing power segmentation scheme of each node to obtain resource segmentation information; obtaining an expected computing power and an expected video memory of a to-be-executed task; screening out target nodes meeting the expected computing power and the expected video memory according to a node analysis strategy; and according to the sub-resource analysis strategy, screening out sub-resources meeting the expected computing power and the expected video memory, recording the sub-resources as target sub-resources, and allocating the to-be-executed task to the target sub-resources. According to the method, GPU resource fragmentation is effectively reduced through global and node resource conjoint analysis scheduling, meanwhile, pooling management, dynamic configuration and intelligent scheduling of heterogeneous computing equipment can be achieved, and the resource utilization rate is effectively increased.

The invention belongs to the technical field of cloud edge collaborative service deployment, and relates to a complex cloud edge collaborative service deployment method based on multi-agent reinforcement learning. The method comprises the following steps: S1, constructing a dual-agent collaborative deployment framework; s2, an environment module collects resource state data of a cloud side node and topological features of a physical link, and generates preprocessing features; s3, inputting the preprocessing features into a node agent network, training and outputting a target node index, and setting an output result as an action of a node agent; s4, the link agent obtains real-time topological characteristics from a physical link between the source node and the target node, inputs the topological characteristics to the link agent network, and outputs optimal path selection and frequency slot block combination; s5, the evaluation module calculates a collaborative score; and S6, the node agent and the link agent update network parameters according to the instant reward signal. According to the invention, collaboration of cloud and edge computing resources and link resources is realized, and high-quality service provision with low delay and high resource availability is completed.

A system and method for scalable generations of synthetic <logical form, utterance> pairs for training a semantic parser is disclosed. A semantic parser is trained on pairs of <logical form, utterance>. An ontology graph is constructed and derived from a plurality of enterprise documents and provides relationships among the concepts or classes of an organization. One or more paths are traversed among a plurality of source and destination node pairs, facilitating comprehensive semantic representation. Attributed query subgraphs are generated of source nodes, destination nodes, and hidden nodes. Each path is recognized among a variety of possible paths between source and destination nodes. Each path in the ontology query subgraph is validated by considering a plurality of predicates and a knowledge graph generates a natural language utterance. The utterances are refined and rephrased using a large language model, enhancing their coherence and linguistic quality.

Aspects of the disclosure include methods for leveraging retrieval augmented generation (RAG) over a graph neural network (GNN) for edge building and the generation of reason-aware graph recommendations. A method can include constructing a graph neural network from an input graph having a plurality of nodes and one or more edges. The graph neural network includes one or more internal layers, each internal layer having one or more node vectors encoding a K-hop neighborhood for a target node of the plurality of nodes. RAG data including non-graph contextual data is retrieved for each of the plurality of nodes and transformed into embeddings using a large language model encoder. The RAG embeddings are encoded into node vectors of the graph neural network. The graph neural network generates a representation for the target node that is transformed by a feed forward neural network tower into an output vector.

The invention discloses a power distribution topology dynamic reconstruction risk assessment method, system and device and a medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring real-time state data of an intelligent component to construct an initial topological structure, and identifying a changed target node by sending a detection signal; and then carrying out dynamic reconstruction on the topological structure in the three logic layers. And performing electrical characteristic verification on the reconstructed topological structure. And after the electrical characteristic verification is passed, determining a connection relationship of upstream and downstream power distribution cabinets and a component association link based on the target node, and calculating a risk level in combination with historical operation parameters. And formulating a temporary coping strategy according to the risk level, and generating a risk assessment report containing the topological influence range, the risk level and the coping strategy. Real-time monitoring and risk early warning of topological change of the power distribution system are realized in the whole process. By implementing the technical scheme provided by the invention, the risk assessment accuracy of the dynamic reconstruction of the power distribution topology can be improved.

The invention provides a computing power resource scheduling method and system based on cloud network integration. Selecting a computing power task to be scheduled as a current scheduling task, and generating a node resource adaptation matrix based on the real-time load data, the cloud network topological relation between the nodes and the dynamic bandwidth data; then calculating the lowest scheduling cost of each node according to the matrix, and determining a dynamic adjustment factor when a resource allocation conflict occurs in the current scheduling task; based on the lowest scheduling cost, the computing power demand scale and the data transmission estimated overhead, calculating the final scheduling overhead for scheduling the current scheduling task to each node; and finally, allocating tasks to a target node according to the final scheduling overhead, updating a running task queue, if a conflict occurs, calling a dynamic adjustment factor to execute resource reallocation, and updating the queue after the reallocation succeeds. And circulating the process until all tasks are scheduled. According to the scheme, optimal scheduling of computing power resources in a cloud network convergence environment can be realized.