1430 results about "Goal node" patented technology

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.

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 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 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 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 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.

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 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.

The invention discloses an online education resource recommendation method and system based on a knowledge graph, and relates to the field of online education resource recommendation. The method comprises the following steps: S1, mapping an interaction record of a learner to a knowledge graph node space to generate an interaction matrix; s2, on the basis of the interaction matrix, calculating the mastery degree of each node in combination with interaction strength and knowledge graph path dependence; s3, correcting the mastery degree in combination with the forgetting curve to generate a corrected mastery degree; s4, calculating the energy mastering degree of the target node by adopting a path energy attenuation model; s5, calculating enhanced correlation based on the features of the resource and the target node and the space-time state; s6, filtering resources having conflicts with mastered knowledge to obtain security correlation; and S7, calculating a recommendation priority score in combination with the energy mastery degree and the safety correlation. Through personalized recommendation and cognitive conflict filtering, the accuracy and correlation of educational resource recommendation are improved, so that the learning efficiency is optimized, and the cognitive burden of learners is relieved.

The invention provides a hierarchical deep reinforcement learning routing protocol method based on a multi-link ad hoc network, relates to the technical field of communication routing, and solves the problem that the calculation efficiency in the current multi-link ad hoc network is degraded. The method comprises the following steps: firstly, constructing and dynamically updating a global network view for all nodes in a network, and further constructing a route selection model which is divided into a high-level controller and a low-level controller; a high-level controller plans a route from a global perspective and balances a global load, and a low-level controller selects a target node according to a planning result of the high-level controller and distributes an optimal communication link for the target node in combination with link state information; training and optimization of the network of each controller are carried out independently, external rewards are introduced to a high-level controller, and internal rewards are introduced to a low-level controller; the external reward is used for optimizing the overall performance of the high-level controller; the internal reward is used as an index value of the low-level controller for feeding back the link state in real time, and collaborative optimization of the high-level controller and the low-level controller is achieved.

The invention relates to the technical field of edge computing, in particular to an operator edge computing node optimization method and system, and the method comprises the steps: obtaining local load features and cross-node association features of edge nodes in real time, and constructing a dynamic load vector and an association vector respectively; based on a preset time sequence prediction model, taking the load vector and the association vector as input, and obtaining a predicted load trend in a future preset time period; generating an optimal service migration strategy through a preset reinforcement learning model according to the predicted load trend in combination with the dynamic load vector, the association vector and the task attribute; wherein the migration strategy comprises a migration target node and a migration resource allocation scheme; wherein the task attributes comprise task priorities; and on the basis of the migration strategy, a resource pre-allocation scheme of the target node is generated in combination with a preset resource constraint condition, and the method has the advantages of improving service response, resource utilization rate, task success rate and the like in a high dynamic scene.

The invention discloses a container-based cross-data center computing power resource scheduling method, which relates to the field of resource scheduling, and comprises the following steps: firstly, requiring each physical GPU node to report a sharable fine-grained GPU resource report; then, for a to-be-scheduled task submitted by a user, pre-estimated resource consumption data of the to-be-scheduled task is loaded or generated from the task portrait database; most importantly, the scheduler does not simply screen available nodes any more, but carries out detailed resource allocation scoring and selection on each physical GPU node in a feasible node list according to portrayed task requirements, and determines an optimal target node by calculating an adaptive score. According to the intelligent matching mechanism, the node agent program and the GPU API gateway module are combined to configure isolation parameters for the container and redirect GPU API calling, and refined and efficient scheduling of cross-data-center computing power resources is achieved.

The invention provides a computing power center resource centralized self-adaptive control scheduling system and method, and the method comprises the steps: S1, a user submits a computing power training task through an API, and sends the task to a DAG task composer, and forms a task list to be scheduled; s2, the intelligent monitoring analysis module is used for continuously collecting node data and calculating real-time CS and HS scores of each node; s3, analyzing the dependency relationship type data by a task composer, and calling a dynamic programming algorithm in an adaptive scheduler to make a decision; s4, the adaptive scheduler performs dynamic task distribution according to the task list of the DAG task orchestrator and the intelligent monitoring analysis module, and S5, the distributed runtime and environment management module is used for uploading the prepared Docker mirror image to the distributed storage; and S6, through the unified communication adaptation layer, the mirror image is pulled from the distributed storage on the target node and the container is started, the user does not need to concern the difference of the operating system in the whole process, and efficient and intelligent adaptive scheduling is realized.

The invention discloses a Text-to-SQL (Structured Query Language) generation method and device, a medium and a product, and relates to the field of geographic information technology application, the method comprises the following steps: analyzing a natural language query text input by a user to obtain a source field set and a target field set; determining corresponding nodes of each field in the source field set and the target field set in the river-lake long-system database mode knowledge graph to obtain a source node set and a target node set; the nodes in the source node set serve as starting points, the nodes in the target node set serve as terminal points, the knowledge graph is processed through the shortest path algorithm, and a path set is obtained; decomposing the natural language query text into a plurality of sub-problems by adopting a large language model and a thinking chain technology, and obtaining a dependency relationship among the sub-problems; according to the dependency relationship and the target field set, a public table expression is obtained, and then the SQL statement is obtained, and the accuracy of database query language generation under the river and lake long-term system scene can be improved.

The embodiment of the invention discloses a distributed data transfer method and device based on fault prediction and a medium, belongs to the technical field of data migration, and solves the problem that when a distributed system breaks down, the timeliness of task completion is seriously influenced. Comprising the following steps: performing health degree evaluation and stable operation duration prediction on nodes in a distributed system through a preset fault prediction algorithm to obtain a node fault prediction result; wherein the fault prediction result at least comprises a node in which a fault is predicted to occur, fault occurrence time and a to-be-transferred task corresponding to the node in which the fault is predicted to occur; performing priority analysis on the to-be-transferred tasks based on the time sequence diagram neural network to obtain task priorities; determining a target node based on the multi-dimensional features corresponding to the nodes and the association relationship between the nodes; and transferring the task to be transferred to a target node through a preset multi-stage progressive transfer strategy according to the node fault prediction result and the task priority.

The invention relates to a multi-mobile-robot path planning method based on swarm intelligence, and belongs to the technical field of robot path planning. The method comprises the following steps: setting the size of a map, starting and target positions and colors of a robot, creating a preset grid map, and initializing an ant colony algorithm, a genetic algorithm and a pheromone system; iteratively searching paths for the mobile robots with different starting points at the same time through an ant colony algorithm; a genetic algorithm is used for optimizing the path, conflict detection and processing are carried out after the path is optimized, and it is ensured that the final path is free of conflicts; and when the maximum number of iterations is reached, outputting the shortest path that each robot arrives at the target node and no collision exists between the robots. According to the path planning method, unnecessary turning can be reduced, the convergence speed of path searching is obviously improved, robot conflicts can be avoided, and the actual requirements of multi-robot path planning are met.

The invention relates to a computing power resource dynamic scheduling method, device and equipment based on deep reinforcement learning and a medium thereof, and the method comprises the steps: constructing a joint state vector through real-time fusion of a network layer channel state and computing layer node load data, and driving a strategy network to generate transmission parameters and resource allocation actions of cooperative control; the code modulation parameters of the wireless transmission module and the computing resource proportion of the target node are synchronously configured in the execution layer, and dynamic task scheduling in the channel decay environment is achieved; a multi-target reward mechanism is designed to couple transmission bit error rate penalty, resource utilization efficiency and task timeliness evaluation indexes, and a reinforcement learning agent is guided to balance communication stability and computing power demand conflicts; according to the method, strategy network parameters are optimized through time difference errors, closed-loop feedback is formed in combination with channel state prediction and node load updating, the problems of network and calculation layer splitting decision, insufficient dynamic adaptability and multi-target optimization imbalance in the prior art are effectively solved, and the task scheduling success rate in the time-varying wireless environment is improved.

The invention relates to a dynamic routing and flow balancing method and system for a regionalized network topology, and the method comprises the steps: dividing a satellite network into a plurality of partitions, and distributing a core node for traffic scheduling and routing calculation for each partition; each core satellite node responds to each traffic demand, and bandwidth and traffic distribution in each partition are adjusted through a multi-anchor-segment routing method and a preset linear programming model; and each satellite node dynamically adjusts the route of each traffic demand through a probability forwarding scheduling method based on geometric topology according to the relative position of the satellite node and the target node. According to the method, low delay, high bandwidth utilization rate, load balance and efficient fault recovery of the satellite network are realized through network partitioning, a multi-anchor-segment routing method and probability forwarding based on geometric topology.

The invention belongs to the technical field of computers, and particularly relates to a data center server resource allocation method and system based on dynamic load balancing, and the method comprises the steps: collecting a physical resource state, instance constraint and network flow through a multi-dimensional probe, constructing a dynamic weighted load scoring model, and introducing a migration penalty term to correct node load evaluation; solving maximum weight matching of the bipartite graph with constraint by combining a Hungary algorithm, and realizing global optimal mapping of the migration instance and a target node; a memory, a CPU and network resources are pre-configured before migration, a delay recovery mechanism is triggered after migration, and the service quality is guaranteed. The system comprises a resource acquisition module, a score generation module, a matching solution module, a pre-configuration module, a self-adaptive period adjustment module and the like. The cluster resource utilization rate is obviously improved by 28%, hotspots are reduced by 76%, the SLA default rate is reduced by 92%, meanwhile, energy is saved by 15%, and collaborative optimization of efficiency and service quality is achieved.

The invention provides an agent-based user interaction method and device, equipment, a medium and a product, which can be applied to the technical field of artificial intelligence and the field of financial science and technology. The method comprises the steps of updating a scene sub-graph in a preset knowledge graph according to current input information of a target user for a preset agent; the scene sub-graph is used for storing historical interaction information of the target user and the preset intelligent agent; according to the semantic entities in the updated scene sub-graph and the incidence relation between the different semantic entities, updating a semantic entity sub-graph in a preset knowledge graph; performing semantic entity clustering on the updated semantic entity sub-graph, and updating a community sub-graph in a preset knowledge graph according to a semantic entity clustering result; searching a target node related to the current input information in a preset knowledge graph; determining memory prompt information according to the retrieved target node; and for the current input information and the memory prompt information, generating feedback information based on a preset agent.

The invention provides a building main body construction quality tracing method and system, and relates to the technical field of building construction management, and the method comprises the steps: dividing region blocks based on a pixel matrix of a construction site image, carrying out the matching of each region block with a preset control node coordinate in a building information model, recognizing and positioning a target node, generating node image data; based on a target node associated with the node image data, collecting construction environment parameters and physical state parameters of the target node, and generating a multi-dimensional parameter set based on the timestamp index; performing parameter fitting calculation on the multi-dimensional parameter set to generate a parameterized label sequence for representing the construction progress and the quality state; encrypting the parameterized tag sequence and storing the parameterized tag sequence into a distributed account book to form encrypted evidence storage data; and based on the encrypted evidence storage data, quality responsibility tracing and construction record verification are carried out, and a quality tracing verification result is generated. And comprehensive and accurate real-time monitoring on the construction progress and the quality state can be realized.

The invention relates to a resource elastic scaling decision-making method, system and device and a medium. The method comprises the following steps: collecting real-time operation data of a security service node, and performing multi-dimensional security index analysis according to the real-time operation data to obtain a portrait data packet; predicting the security service weight value to obtain a prediction result, performing dynamic error compensation on the prediction result to generate a corrected weight prediction value, and generating a control instruction based on the corrected weight prediction value and the active session state; and when the instruction is a migration instruction, analyzing a session state snapshot of the instruction, calling a preset kernel state locking function to lock a memory session block of a source node, obtaining incremental state change data to generate a migration snapshot packet, and performing block verification injection operation on a target node. According to the method, by integrating multi-dimensional safety index analysis, prediction error compensation and stateful transition verification mechanisms, the accuracy and response efficiency of resource elastic scaling decision making are improved, and the continuity of stateful service transition and the consistency of safety strategies are enhanced.

The invention discloses a medical question-answering system based on a time sequence knowledge graph and a question-answering method thereof, and the system comprises a TKG construction module which is used for constructing the time sequence knowledge graph in the medical field and comprises entities, relationships and timestamp information; the hierarchical graph neural network module fused with position coding comprises a sub-graph layer and a global graph layer, the sub-graph layer is used for capturing a structural dependency relationship of concurrent facts under the same timestamp, and the global graph layer is used for capturing time correlation between cross-timestamp entities; the LLM collaborative reasoning module adopts RAG retrieval and combines the reasoning result of the TKG with an external medical knowledge base to generate an answer; the multi-mode interaction module integrates voice recognition and synthesis and supports voice questions and answers; according to the scheme, the position codes are fused into the message propagation process of the relation perception graph convolutional neural network, the distinguishing capacity of the target node for the neighbor nodes is greatly enhanced, and therefore the expression capacity of embedding of the target node is enhanced.