1065 results about "Web resource" patented technology

The invention provides a dynamic routing selection method and system, electronic equipment and a storage medium, and aims to solve the problem that a routing strategy is difficult to adapt to a dynamically changing network, the method comprises the following steps: a terminal layer collects the state of a terminal and network data, and performs lightweight feature extraction; the edge node layer receives the data of the terminal layer, carries out space-time-semantic feature aggregation, and generates a region-level resource scheduling and routing decision strategy based on fragmented reinforcement learning; the central cloud service layer gathers whole network data, generates a global optimization strategy and issues the global optimization strategy; the edge node layer fuses global optimization and a region-level strategy, and executes dynamic routing selection; and security and privacy protection are provided through the trusted chain layer. According to the invention, adaptive path selection can be realized, the network resource utilization rate is improved, and the network stability is improved.

The embodiment of the invention relates to the technical field of artificial intelligence, and provides a linkage method and system of an apartment network and intelligent equipment, and the method comprises the steps: collecting network operation data and equipment operation data of a target apartment in real time; based on the equipment operation data, determining currently achievable candidate service scenes of the target apartment; fusing the network operation data and the equipment operation data into a joint operation map of the target apartment through the space-time diagram attention network; a multi-agent depth deterministic strategy gradient algorithm is adopted to carry out network resource allocation and equipment control decision making on the joint operation map, scene adaptive optimization is realized in combination with candidate service scenes, and a linkage strategy of a target apartment is obtained; and generating a decision instruction based on the linkage strategy, and adopting the decision instruction to realize remote control of the network equipment and the intelligent equipment. Through deep fusion of the space-time diagram attention network and multi-agent reinforcement learning, dynamic collaborative management and control of the network and equipment are realized, and the resource utilization rate and the service response speed are improved.

The invention discloses an air-space-ground caching and resource optimization method and system based on a hierarchical intelligent agent, and aims to construct a deep reinforcement learning architecture in which a high-layer DQN and a low-layer DDPG are coordinated for high dynamics and information uncertainty of an air-space-ground integrated network. The high-level intelligent agent generates a period-level content cache and access control strategy based on global states including a cache state, a task request, a node resource and the like, and the low-level intelligent agent executes time slot-level resource allocation, task unloading rate control and UAV deployment optimization under the constraint of the high-level strategy. The system triggers low-level optimization through a double-stage reward mechanism and constraint verification, evaluates a strategy effect based on time slot income and full-period income, and combines state perception and experience playback technologies to realize collaborative optimization of high and low-level decisions. A simulation result shows that compared with a traditional method, the method has remarkable advantages in the aspects of reducing content acquisition delay, reducing return communication overhead, improving task processing success rate and the like, and the space-air-ground MEC network resource utilization rate and user experience are effectively improved.

The invention discloses a TSN scheduling optimization method and device based on flow sensing autonomous learning, equipment and a medium. The method comprises the following steps: deploying a lightweight flow detection module in a switch or a router, and after a controller receives a data request, automatically identifying a newly arrived unknown service flow by using the lightweight flow detection module, and judging whether the newly arrived unknown service flow is a periodic TT flow or an unpredictable burst flow; the controller performs classification management on the identified service flow types, collects topological information and flow requirements of the whole network and issues the topological information and the flow requirements to the terminal nodes through a network management interface; the controller constructs an intelligent queue scheduling task based on the collected network topology information and traffic demand and converts the task into a Markov decision process MDP, network resources, queue states and priorities are used as a state space, a scheduling strategy is used as an action space, a reward function is designed in combination with throughput and delay indexes, and an intelligent queue scheduling task is obtained. Driving a dynamic environment through real-time data and training a DRL model; an enhanced queue scheduling mechanism Pro-CQF is adopted, different priority labels are configured according to classified flow types, and then mixed flow scheduling is carried out; the controller periodically collects time delay, packet loss rate and end-to-end transmission delay indexes and feeds back the indexes to the DRL model, and a scheduling strategy is updated online. According to the method, the traffic sensing and scheduling efficiency is greatly improved in a network environment in which multiple service flows coexist and end-side equipment functions are different, and the reliability and the expandability of the TSN in industrial Internet of Things, edge computing and other high-real-time application scenes are remarkably enhanced.

The invention discloses a resource awareness and task migration method and system for industrial edge nodes. The method comprises the following steps: constructing an edge node resource dynamic monitoring system, and sensing, calculating, storing and network resource states in real time; establishing a node health degree evaluation model, and predicting a potential fault risk; designing an intelligent task migration decision-making mechanism based on a resource state; the guarantee of data consistency and service continuity in the task migration process is realized; and constructing a distributed task scheduling optimization framework. The system comprises a resource monitoring module, a health assessment module, a migration decision module, a data synchronization module and a scheduling optimization module. According to the method, the problem of unstable task execution caused by dynamic resource change in an industrial edge computing environment is solved, and intelligent resource management and efficient task migration of the edge nodes are realized.

The invention provides a front-end resource dynamic preloading method and system based on user behavior intention prediction, and relates to the technical field of computers, and the method comprises the steps: collecting continuous behavior data of a user in real time in a client browser, carrying out the feature extraction of the collected behavior data, and generating a behavior feature vector; on the basis of an intention prediction model deployed at a client, according to the behavior feature vector, performing real-time prediction on the click intention probability of each interactive element in the page; when the predicted click intention probability exceeds a preset threshold value, triggering a resource preloading instruction; and according to the resource preloading instruction, dynamically creating a browser preloading label, and starting background downloading of a target resource so as to perform dynamic preloading of a front-end resource. According to the method and the device, the resource utilization rate and the page loading efficiency are improved, so that the waste of a server and network resources is reduced while the user experience is improved.

Techniques for displaying a graphical user interface of a software development system are described. An initial description of an initial structure of a software system is obtained, the initial description including an identification of interconnections amongst provider network resources that form the initial structure. A modified description of a modified structure of the software system is obtained, the modified structure based on at least a change to the initial structure of the software system described in the initial description. A difference between the initial description and the modified description is determined. An electronic device is caused to display a graphical user interface including a visual depiction of the initial structure of the software system and an indication of the difference, the visual depiction based at least in part on the initial description.

The invention belongs to the field of natural language processing and artificial intelligence, and particularly relates to a long text writing generation method and system based on multi-agent cooperation. The method comprises the following steps: receiving a text theme input by a user, completing complex theme decoupling, and outputting an outline directory; the method comprises the following steps: collecting keywords according to a text topic and an outline catalog, obtaining a retrieval result by retrieving the Internet, a domain database and network resources, and preprocessing to obtain a reference with a uniform format; performing systematic evaluation on the reference literature from the field integrating degree and the logic relevance, and performing grading processing on the reference literature to obtain a high-quality reference literature; based on the text theme and the high-quality reference, long text content is generated according to a set logic structure and writing specifications. According to the method, the fragmented expression problem is effectively avoided, the academic value and reading experience of the text are remarkably improved, the inherent defects of a traditional long text generation technology are practically overcome, and a high-efficiency solution is provided for text creation in the professional field.

There are provided methods, systems and non-transitory storage mediums for providing personalized item recommendations to a user in real-time. Data related to items on a web resource and as user data comprising profiles, preferences, and past interactions with items of a plurality of users are received. Personalized strategies and constraints for a second set of items are received. An objective function representing optimization goals is also received. Using an inference engine, user behavior of a plurality users is inferred based on the received data. In real-time, user interactions of a user with the web resource is received, and personalized item recommendations are generated by a personalized recommendation engine for the user based on the user interaction, inferred user behavior, offer strategies, and the objective function. The personalized item recommendations, along with discount types and time periods, are transmitted to the user's device in real-time.

The invention discloses a fine-grained slice isolation method in an air-sea cross-domain network, which belongs to the technical field of network communication, and comprises the following steps: firstly collecting resource data to construct a cross-domain heterogeneous resource map; selecting an optimal slice deployment scheme through K shortest path screening and node matching decision; then realizing slice hard isolation by means of a resource data isolation technology, a network resource isolation technology and a wireless link resource isolation technology; and finally, monitoring the operation state, and triggering dynamic adjustment of the scheme when the operation state is abnormal. The system comprises three modules, and seamless switching of slices is guaranteed. Through a deterministic optimization algorithm and a multi-level isolation mechanism, accurate and efficient allocation and strict isolation of heterogeneous resources are realized, and the QoS guarantee capability of services in a high-dynamic cross-domain network and the system stability are remarkably improved.

The invention relates to an intelligent scheduling method and device for communication network resources, equipment and a medium. The method comprises the following steps: performing IP packet header analysis on a service data packet to obtain an initial service priority identifier, and performing adjustment in combination with a preset priority rule, an equipment alarm state and a network congestion rate to generate a dynamic service label; based on the dynamic service label, the real-time link state and the energy consumption coefficient, generating a resource allocation strategy and a path scoring weight parameter through a preset deep reinforcement learning network; and calculating a path score based on the resource allocation strategy and the path score weight parameter, generating a path mapping table, generating a port suppression instruction based on the dynamic service label and the resource allocation strategy, and extracting a base station power regulation instruction. According to the method, the dynamic adaptability, the resource utilization rate and the energy consumption management and control capability of communication network resource scheduling are improved by means of dynamically adjusting the service priority, comprehensively modeling a resource allocation strategy and the like, and the transmission guarantee level of high-priority services is enhanced.

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 discloses a financial intelligent backbone network flow scheduling method and system based on SRv6, and relates to the technical field of network scheduling, and the method comprises the following steps: S1, carrying out the recognition of the ingress service flow, and dividing the ingress service flow into predefined service types; s2, matching a corresponding SRv6 traffic tunnel model for each service type; step S3, distributing differentiated QoS strategies for different types of service flows, and executing corresponding queue scheduling; s4, aiming at the SLA requirements of different service types, constructing a corresponding optimization objective function to carry out path calculation; s5, when it is detected that the link utilization rate exceeds a preset threshold value, an intelligent tuning process is automatically triggered; and S6, issuing the optimal SRv6 strategy obtained by calculation to entrance equipment for execution. According to the invention, an intelligent and efficient financial backbone network architecture is constructed, and a refined traffic scheduling strategy is realized, so that the utilization rate of network resources and the operation and maintenance efficiency are improved.

The invention relates to the technical field of communication, in particular to a dynamic service scheduling method for a 5G new call based on intelligent semantics, which realizes efficient scheduling of 5G call services through three core steps of multi-operator fusion access, dynamic routing distribution and intelligent voice interaction. According to the method, a unified SIP trunk line integration platform is constructed, and network resources of three major operators are integrated; respectively designing routing strategies for low-concurrency and high-concurrency scenes from the dimensions of network quality, cost rate, compliance, link load and the like, and dynamically optimizing path selection through a weight algorithm; and intelligent analysis and interaction of voice semantics are realized by combining ASR, TTS and NLP engines. According to the method, the problems of cross-operator routing optimization, high-concurrency processing and compliance management are solved, and the method is suitable for scenes with high requirements on stability and compliance, such as finance and government affairs.

Systems, methods, and apparatuses for providing optimization of network resources. The system is operable to monitor the electromagnetic environment, analyze the electromagnetic environment, and extract environmental awareness of the electromagnetic environment. The system extracts the environmental awareness of the electromagnetic environment by including customer goals. The system is operable to use the environmental awareness with the customer goals and / or user defined policies and rules to extract actionable information to help the customer optimize the network resources.

The invention relates to the technical field of network communication, in particular to a network resource entry method and system based on a topology self-discovery technology, and the method comprises the following steps: a detection source device generates a detection data packet containing a device identifier, a TTL value and an accessed device list, and sends the detection data packet to an adjacent device hop by hop; after the intermediate equipment receives the probe packet, if the intermediate equipment does not exist in the accessed list, adding equipment information and a connection relationship and continuing forwarding until the TTL value is exhausted or no available port exists; the method has the beneficial effects that hop-by-hop collection of network equipment information is realized by designing an intelligent detection data packet, a dynamic network topology is constructed in combination with a graph theory algorithm, deep resource attributes are extracted by utilizing a machine learning technology, and finally automatic input and real-time updating of network resources are realized. According to the method, through detection-construction-extraction-entry-visualization five-dimensional architecture, the problems of low efficiency and poor real-time performance of traditional manual entry are solved.

The invention discloses a computing power network resource scheduling method and system, and the method comprises the steps: responding to a service request, carrying out the coding of a network state through an edge attention graph neural network, and enabling the network to integrate an edge feature vector into attention weight calculation in message transmission, so as to sense a link state; based on a coding result, a scheduling decision of joint optimization calculation and network resources is generated through a self-adaptive multi-target reinforcement learning strategy network, and the reward function weight can be dynamically adjusted according to the network state; decisions are executed based on a distributed multi-agent architecture, and all agents realize global coordination through a message coordination mechanism. According to the invention, efficient, adaptive and extensible scheduling of multiple targets in the dynamic computing power network is realized.

The invention discloses a cross-scene user traffic monitoring behavior analysis method and system based on deep learning. The method comprises the following steps: collecting standard traffic data; establishing a multi-modal depth feature extraction network, extracting time sequence features of a statistical level from packet header information of standard traffic data by using a CNN convolutional neural network, obtaining semantic features from traffic payload by using a Bi-LSTM bidirectional long short-term memory network, and obtaining associated features of network resources based on a GNN graph neural network; fusing the time sequence features, the semantic features and the associated features by using a cross-modal attention mechanism to obtain fused features; and inputting the fusion feature into a behavior recognition model based on a multi-head self-attention mechanism, and outputting a user behavior tag which at least comprises application use, behavior intention and behavior abnormality score. The manual analysis cost is reduced, and the monitoring analysis efficiency and accuracy are improved.

A system comprises a memory communicatively coupled to at least one processor. The at least one processor is configured to receive a request for data sharding operations from a user device, determine a node comprising multiple network resources configured to create a response to the request, receive sharding feedback from the node, execute the machine learning algorithm to determine whether the performance of the network resources match a predefined performance threshold, generate a proposed sharding strategy to increase the network resources at the node in response to determining that the performance of the network resources does not match the predefined performance threshold, provide the proposed sharding strategy to the node for implementation, and generate multiple sharding commands configured to control routing of data between the first node and the user device in response to receiving the access command; and route the request to the node using the sharding commands.

The application discloses a financial intelligent backbone network traffic scheduling method and system based on SRv6, and relates to the technical field of network scheduling.The method comprises the following steps: step S1, identifying and dividing the entry service traffic into predefined service types; step S2, matching the corresponding SRv6 traffic tunnel model for each service type; step S3, allocating differentiated QoS strategies for different types of service flows, and performing corresponding queue scheduling; step S4, constructing the corresponding optimization objective function for path calculation according to the SLA requirements of different service types; step S5, automatically triggering the intelligent optimization process when detecting that the link utilization rate exceeds the preset threshold; and step S6, issuing the optimal SRv6 strategy calculated to the entry device for execution.The application builds an intelligent and efficient financial backbone network architecture, and realizes fine traffic scheduling strategies, thereby improving the network resource utilization rate and operation and maintenance efficiency.

The invention discloses a distributed file management system and method, and belongs to the technical field of computer software, the system adopts a tree hierarchical structure to organize files, and the system comprises an application layer used for receiving file operation requests from a plurality of independent clients, and each request carries a unique application identifier; the file system layer comprises a configuration module, an operation module, a metadata management module, a day module, a timed task module and a gateway module, and the request is routed to a corresponding logic storage partition in the data layer according to an application identifier; the data layer comprises a database cluster and an object storage cluster, and performs persistent storage and management of logic isolation on metadata and file data according to application identifiers; and the infrastructure layer provides an extensible server and network resources. Based on a distributed architecture, load balancing and efficient access are realized through data storage partitioning, a metadata management and index technology and a client load balancing technology, and the expandability, fault tolerance and performance of the system are improved.

The embodiment of the invention relates to the technical field of computers, in particular to a distributed network architecture construction method and system for port environment monitoring, and the method comprises the steps: dividing a plurality of monitoring sub-regions according to the port region environment monitoring requirements, and deploying a heterogeneous monitoring node set in each sub-region. And performing real-time operation state sensing processing on the node set to generate a target network topology structure. And executing routing protocol generation and data scheduling strategy formulation based on the topological structure to obtain a self-adaptive routing table and a data transmission scheduling rule. According to the tables and the rules, distributed collaborative optimization processing is performed on the nodes, network resource allocation schemes and node task load adjustment instructions are generated, finally, port area distributed network architecture reconstruction is performed through the schemes and the instructions, and the efficiency and the accuracy of port environment monitoring are improved.

Systems, methods, and apparatuses for providing optimization of network resources. The system is operable to monitor the electromagnetic environment, analyze the electromagnetic environment, and extract environmental awareness of the electromagnetic environment. The system extracts the environmental awareness of the electromagnetic environment by including customer goals. The system is operable to use the environmental awareness with the customer goals and / or user defined policies and rules to extract actionable information to help the customer optimize the network resources.

The invention discloses a rail transit comprehensive dispatching emergency response method and system based on the Internet of Things, and relates to the technical field of the Internet of Things. The method comprises the following steps: acquiring a rail transit comprehensive scheduling instruction queue through a rail transit Internet of Things terminal; sending the instruction queue to an emergency analysis engine, performing emergency scoring, and obtaining a scoring index queue; an RS-485 subnet layer is built by a plurality of paths of RS-485 interfaces arranged by the gateway; the mapping relation between the instruction queue and the RS-485 subnets is established according to the scoring index queue, and the gateway responds to the instruction queue on the RS-485 subnets in a grading mode on the basis of the mapping relation. The technical problems that the emergency response is delayed and the communication efficiency is low due to the fact that an existing rail transit dispatching system cannot intelligently recognize the emergency degree of an instruction are solved, and the technical effects of improving the emergency response efficiency and optimizing network resource utilization through emergency scoring and RS-485 subnet grading response are achieved.

The invention discloses a resource allocation and configuration method and device and electronic equipment. The method comprises the following steps: converting network resources in a private network into programmable objects, and constructing a resource label model according to attribute information of the programmable objects and the network resources; receiving service demand information, extracting a service intention in the service demand information, and converting the service intention into a standardized intermediate instruction; generating a resource allocation scheme according to the standardized intermediate instruction and the network resource use state information, and evaluating the resource allocation scheme in a digital twin environment of the private network; and under the condition that the evaluation result of the resource allocation scheme in the digital twin environment meets a preset requirement, converting the resource allocation scheme into a control instruction, and sending the control instruction to the network resource. The technical problems of low network resource allocation efficiency and slow service response speed caused by non-uniform resource description and high service demand analysis difficulty in a multi-manufacturer heterogeneous network environment are solved.

The invention discloses a network resource allocation method, electronic equipment and a storage medium, and the method comprises the steps: taking a current excitation parameter issued by a global decision model in a cloud platform as a weighted item of a reward function of a domain-level decision model; the decision model in a single domain is controlled to realize the optimal local network resource and consider the optimal global network performance of the cross-domain network at the same time; according to the global decision model, quantizing the global income index after each candidate operation is executed so as to determine a global resource allocation strategy; and finally, the network resource allocation state of each data center is adjusted through the network controller deployed in each data center, and the domain-level decision model is deployed in a plurality of data centers in different regions. Through the method, the technical problem that cross-domain network resources in a cloud environment cannot be reasonably allocated in related technologies is solved, and the technical effect of intelligently and cooperatively allocating local network resources and global network resources in a domain is realized.

Systems, methods, and apparatuses for providing optimization of network resources. The system is operable to monitor the electromagnetic environment, analyze the electromagnetic environment, and extract environmental awareness of the electromagnetic environment. The system extracts the environmental awareness of the electromagnetic environment by including customer goals. The system is operable to use the environmental awareness with the customer goals and / or user defined policies and rules to extract actionable information to help the customer optimize the network resources.

The invention relates to a resource allocation method and device, equipment, a storage medium and a computer program product. The method comprises: receiving a resource allocation request of a target service; when the isolation level is a preset isolation level, acquiring network resource state information of the current network, and inputting the priority, the resource demand and the network resource state information corresponding to each network slice into a resource allocation model to obtain a target resource allocation result; the resource allocation model is used for executing a resource allocation strategy taking minimization of resource waste as an optimization target and satisfying service level protocol parameters corresponding to a plurality of network slices as constraint conditions; and carrying out resource allocation on each network slice based on the target resource allocation result. According to the content, the resource waste of each network slice can be minimized, so that the resource utilization rate is improved.

A system and method for privilege assurance protection of computer networks that remedies the deficiencies of the current directory service structure. The system uses a software agent to collect and store snapshots of all network resources on a computer network by identifying network domains, searching the directory service of each domain for network resources, and periodically querying the network resources for changes. The software agent communicates with a backend server which provides searching, querying, storage, administrative and other functionality to the agent via remote procedure calls.

The invention provides a method for constructing a container cloud agent based on a MiniMax-Text-01 large model, a medium and equipment, belongs to the technical field of automatic operation and maintenance, and can perform fault detection, flow prediction and resource elastic expansion and contraction according to real monitoring data. The intelligent agent constructed by the invention can automatically pre-judge and give an optimal scheme for capacity expansion or migration when finding that the load of the server is abnormal, so that the frequency and the duration of manual intervention are reduced. In a multi-cloud and hybrid cloud architecture, an intelligent agent can cooperatively manage calculation, storage and network resources on a heterogeneous platform, and cross-cloud load balancing and cost optimization are realized. In addition, the intelligent agent can help a data analysis and machine learning team to better utilize a cloud GPU / TPU cluster and automatically select a proper computing power and storage scheme, so that the training and prediction efficiency of mass data is remarkably improved. In the scene of Internet of Things and edge computing, the intelligent agent can link the cloud and the edge nodes, intelligently dispatch the computing power and bandwidth of each node, and find and correct abnormal data streams in time.