3185 results about "Goal node" patented technology

The invention provides a network attack and defense decision support method and system based on artificial intelligence, and relates to the technical field of network attack and defense, and the method comprises the steps: obtaining network topology information and equipment configuration information, constructing a network environment digital twinborn model, collecting network flow data and security logs, and extracting features through a deep learning model. Generating a network security situation assessment report, analyzing the attack graph by using a graph neural network, identifying a target node and generating an initial defense strategy set, generating a plurality of candidate defense schemes by using a multi-target optimization algorithm based on a validity assessment result of a defense strategy, and performing security and feasibility analysis on the candidate defense schemes, the method comprises the steps of selecting an optimal defense scheme, refining the optimal defense scheme into specific implementation steps, generating alternative schemes for coping with different attack conditions, presenting the optimal defense scheme and the alternative schemes through a visual interface, and providing a decision basis and a risk assessment report.

The invention discloses a database query method and device, electronic equipment and a nonvolatile storage medium. The method comprises the steps that a knowledge graph corresponding to a to-be-queried database is determined, and the knowledge graph is used for representing a logic structure and an incidence relation of data in the to-be-queried database; a similarity score between the user question text and each graph node in the knowledge graph is determined, a target node is determined in each graph node of the knowledge graph according to the similarity score, and the similarity score is used for representing a correlation degree between the graph node and the user question text; and generating database mode information corresponding to the to-be-queried database according to the target node, and generating a structured query language statement corresponding to the user question text by adopting a large language model according to the database mode information. According to the method and the device, the technical problem of poor accuracy of generating the query statement of the database by utilizing the large language model due to the fact that the field information in the database is numerous and the relationship is complex is solved.

Disclosed are an advanced persistent threat (APT) detection method and system based on a continuous-time dynamic heterogeneous graph network (CDHGN). The method includes: selecting network interaction event data, extracting entities from the network interaction event data as source nodes and target nodes, extracting an interaction event occurring between a source node and a target node as an edge, and determining a type and an attribute of a node, a type and an attribute of the edge, and a moment at which an interaction event occurs, to obtain a continuous-time dynamic heterogeneous graph; converting each type of edge in the continuous-time dynamic heterogeneous graph into a vector by a CDHGN encoder, to obtain an embedding representation by a CDHGN decoder to obtain a detection result of whether each type of edge is an abnormal edge, to intercept an APT attack.

The invention relates to the technical field of network routing, and discloses a dynamic load balancing method and system for network routing, and the method comprises the steps: carrying out the periodic sampling and feature extraction of a processing load of a network node and a transmission load of a network link, and obtaining a load state vector and a network load degree; constructing a load balancing decision matrix, and performing comparison operation on the load balancing decision matrix and the multi-stage load threshold to obtain a load optimization instruction sequence; identifying a high-load source node set and a low-load target node set based on the load optimization instruction sequence, and performing load prediction modeling to obtain a task migration scheme; performing path optimization of reinforcement learning based on the task migration scheme to obtain a multi-routing path set; and carrying out load partition clustering and routing strategy distribution on the multi-routing path set to obtain a regional routing mapping table, and the method reduces the network delay, improves the resource utilization rate and realizes dynamic load balance.

The invention relates to the technical field of computer deep learning, in particular to a deep learning task hybrid deployment method and system. The method comprises the steps that S1, a user submits a task through a Kubernetes native interface; s2, a task scheduling optimizer analyzes tasks according to resource requirements and service levels and allocates the tasks to proper nodes; s3, the SLO manager tracks real-time data of various performance indexes of the system and compares the real-time data with a preset service grade target; s4, the Koordlet component executes a specific resource allocation operation on the target node; and S5, the traffic safety monitor monitors the data streams among the nodes in real time. According to the method, a mixed deployment strategy for different types of resources is provided by analyzing the periodical rule of deep learning task resource use, dynamic resource sharing of online and offline tasks is achieved, and meanwhile it is ensured that the system can still run stably and efficiently under the high-load condition.

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 discloses an automatic penetration test method, system and device based on deep reinforcement learning, and a medium, and the method comprises the steps: carrying out the vulnerability scanning and baseline verification of a device through employing a vulnerability scanning and baseline verification tool, and obtaining the vulnerability information of a tested system; generating an attack graph according to the obtained vulnerability information, determining a vulnerability utilization probability according to the vulnerability information, and assigning values to edges of the attack graph to obtain a Bayesian attack graph; designing an attacker target node reward value on the generated Bayesian attack graph, simulating a possible attack behavior of an attacker, and calculating an attack strategy of the attacker by using a deep reinforcement learning method; simulating potential attack behaviors of an attacker in the attack graph according to an attack strategy, and generating a penetration attack path which is most likely to be adopted by the attacker; and performing an automatic penetration test to generate a penetration test report. According to the invention, the penetration test scheme can be intelligently generated according to the vulnerability of the system, and the precision and efficiency of network security evaluation are greatly improved.

The invention relates to the technical field of communication networks, and discloses a distributed network resource optimization scheduling method and system under a load balancing strategy. The method comprises the following steps: if first service request information sent by a client is received, determining a health state weight, a response time weight and a bandwidth weight; determining a first candidate node; if the number of the first candidate nodes is at least two, calculating a health detection score of each first candidate node; and calculating the expected revenue of each first candidate node, taking the first candidate node with the highest expected revenue as a first target node, and distributing the first service request information to the first target node. According to the scheme, the weight is dynamically adjusted according to the current load and resource conditions, the allocation strategy can be automatically adjusted in the face of different load conditions, dynamic load balancing is achieved, and the overload problem is avoided. And the node most suitable for the current load is preferentially selected, so that the service quality and the user satisfaction can be improved.

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 discloses a question and answer method and device based on a large language model and a nonvolatile storage medium. The method comprises the following steps: receiving question text information input into a large language model; determining multiple pieces of entity information in the question text information, and determining association information between the entity information; determining a target node including the entity information and an associated edge corresponding to the target node in the knowledge graph; updating the associated edge, and performing extension processing on the target node according to the updated associated edge; converting the question text information into text feature vectors, and determining target knowledge entries in a vector knowledge base, wherein the similarity between the target knowledge entries and the text feature vectors meets a preset condition; and determining answer text information corresponding to the question text information according to the extended knowledge sub-graph and the target knowledge entry. According to the method and the device, the technical problem that the accuracy of the answer information of the large language model is insufficient due to the historical forgetting condition of the large language model in multiple rounds of long context dialogues is solved.

The invention discloses an intelligent private cloud resource scheduling management system based on a hyper-converged architecture, particularly relates to the technical field of virtual resource scheduling management, and is used for solving the problem of overall utilization rate reduction caused by cascade resource oscillation and fragmentation due to fault recovery in the prior art. Candidate buffer nodes are screened based on the resource occupancy state, a migration priority queue is generated in combination with the node resource isomerism matching degree and the real-time load, and an independent buffer resource pool is reserved at a target node; key resources are directionally reserved through resource locking labels, task dependence periods are associated, and task conflict risks possibly triggered after the resources are released are dynamically predicted; adjusting a label effective period to cover a task execution window based on a conflict identification result; and removing the label in the preset time window, releasing the resources to the buffer pool, synchronously triggering global resource integration and candidate node list updating, and realizing efficient recovery and dynamic redistribution of fragmented resources, thereby inhibiting multi-node resource fluctuation.

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 provides a QoS routing optimization method and system, a computer and a readable storage medium, and the method comprises the steps: S1, obtaining a real-time network performance data set which comprises link bandwidth, delay and packet loss rate; s2, modeling a network topology into a graph structure based on the real-time network performance data set, and generating a network state feature matrix; s3, constructing a state space and an action space according to the network state feature matrix, wherein the action space comprises a plurality of candidate paths from a source node to a target node; s4, selecting a candidate path from the action space through a main network, evaluating the performance value of the candidate path by adopting a target network, updating the main network according to the performance value, and generating an empirical data set; and S5, optimizing a path selection strategy according to the empirical data set, and updating a routing table. According to the method and the device, the optimal path meeting the QoS requirement can be found for the data flow in different service environments in the dynamic network environment.

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 invention provides a wind power prediction method and system based on multiple time scales, and relates to the technical field of wind power prediction, and the method comprises the steps: carrying out the preprocessing of wind power plant data, and obtaining historical time series data; performing multi-time-scale decomposition on the historical time sequence data, inputting a multi-scale time decomposition result into a preset wind power prediction model, constructing a multi-scale graph structure by a multi-scale graph structure unit according to the multi-scale time decomposition result, outputting a multi-hop neighbor set of a target node in the multi-scale graph structure, and outputting a multi-hop neighbor set of the target node in the multi-scale graph structure; the adaptive graph convolution unit constructs an adaptive adjacency matrix according to the multi-hop neighbor set, updates node representation by using the adaptive adjacency matrix, and outputs node representation features; the cross-scale fusion unit performs cross-scale fusion on the node representation features through a multi-head attention mechanism to obtain a cross-scale fusion result; and the prediction unit receives the cross-scale fusion result and outputs a wind power prediction result. The wind power prediction precision of the wind power plant can be effectively 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.

A method for selecting a recommended node in a behavior deviation model includes: determining a candidate path starting from a delegation node to a target node based on a preset network topology; determining candidate recommended nodes passing through each candidate path, and acquiring a first behavior deviation corresponding to the target node at each candidate node; reading a second behavior deviation of each candidate recommended node from a central node in the network topology; calculating an average deviation value for the target node based on second behavior deviations and first behavior deviations; determining a candidate path with the smallest average deviation value as a target candidate path; and in response to the average deviation value of the target candidate path being less than or equal to a preset warning value, determining the candidate recommended node on the target candidate path as a recommended node.

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 relates to the technical field of block chains, and discloses a block chain node fault recovery method and a block chain system.The block chain node fault recovery method comprises the following steps that S1, the fault state of a block chain node is detected through a fault detection and notification module, whether a target node needs to be recovered or not is judged, and a recovery request is broadcasted; s2, through a recovery strategy generation module, generating initial probability distribution based on the recovery demand of the target node and the bandwidth and load state of the normal node, and determining a data distribution strategy of the normal node; and S3, according to the response state of the normal node, the probability distribution is adjusted through a dynamic adjustment and optimization module. According to the method, the problems of low node fault recovery efficiency, non-uniform task distribution, poor transmission reliability and long verification time consumption in the prior art are solved by dynamically adjusting probability distribution optimization, data blocking and multi-path parallel transmission, redundant coding combined consistency verification and a hierarchical priority strategy; and the efficiency and adaptability of data recovery are greatly improved.