139 results about "Resource network" patented technology

The invention relates to the technical field of supply chain management, and discloses an intelligent supply chain optimization management and control method. The method comprises the following steps: acquiring full-link real-time operation data of a supply chain through a distributed data acquisition module; extracting topological feature data by using a graph neural network; inputting the multi-target collaborative optimization model, and generating supply chain strategy optimization parameters by adopting a mixed integer linear programming framework and a dynamic constraint relaxation mechanism; constructing an adaptive resource scheduling model, realizing node dynamic scheduling by using an improved particle swarm algorithm, and outputting optimal configuration data; and establishing a layered supply chain management and control model which comprises a strategic planning layer, a dynamic coordination layer and an execution control layer, and realizing full-link intelligent optimization management and control. The invention also relates to risk event processing, strategic planning layer resource network planning and the like. The system can comprehensively collect data, optimize resource configuration, improve response speed, reduce cost, effectively control risks and improve the overall competitiveness of a supply chain.

The invention relates to the technical field of electric power communication, in particular to an electric power communication resource topological optimization method and system based on a graph database. The specific implementation process comprises the following steps: constructing an electric power communication resource topological graph comprising communication exchange nodes, data transmission links and attribute information through a graph database; analyzing and quantifying the received business type into a routing constraint condition according to a business service level protocol; performing dynamic weighting calculation on each candidate communication path meeting the routing constraint condition, evaluating comprehensive performance indexes and outputting an optimal communication path; and converting the optimal communication path into a topology configuration instruction capable of guiding network equipment to carry out data deployment, and carrying out dynamic optimization on the power communication resource topology. According to the method, the service quality requirements of different services are quantified into dynamically adjusted routing constraints and weights, so that reasonable allocation of power communication resources and effective optimization of network topology are realized, and the working efficiency of a power communication resource network is improved.

The invention discloses an AI-based meter reading data management and resource scheduling optimization method and system, and relates to the technical field of intelligent management of meter reading data. The method comprises the steps that meter reading data of a meter is obtained, and the meter is provided with a corresponding two-dimensional code metal identification piece; meter reading data are managed according to geographic areas and user types, a multi-level data feature system is formed, and the user types comprise resident types and industrial types; carrying out trend modeling on the multi-level data characteristic system by adopting an AI model so as to determine an energy consumption trend in a future period; dynamically adjusting a meter reading task according to an energy consumption trend and a network load condition so as to dynamically schedule human resources, data resources, network resources and hardware resources; wherein the power grid topology position code is used for binding an association relationship between the meter reading task and the power grid physical conduction path. According to the invention, full-link optimization from meter reading data acquisition to resource scheduling is realized.

Methods, systems, and devices for wireless communications are described. A network entity may transmit a set of resources allocated to a user equipment (UE) to the UE via a reconfigurable intelligent surface (RIS). The network entity may transmit an indication of a weighting matrix for controlling reflective elements of the RIS during transmission occasions. The RIS controller may apply different portions of the weighting matrix to the reflective elements for the transmission occasions. The network entity may transmit one or more transmission configuration indicator (TCI) states to the UE for each portion of the weighting matrix. The network entity and UE may communicate based on cyclically applying the different TCI states.

The invention provides a smart city emergency response method and system combined with artificial intelligence, and the method comprises the steps: firstly carrying out the situation awareness of a city emergency event, generating an event situation map containing spatial-temporal evolution characteristics and influence levels, calling a pre-trained strategy migration model to match historical cases, and outputting a preliminary response strategy set; and dynamically adapting and adjusting the preliminary response strategy set based on the situation map real-time updating characteristics, generating an adaptive response strategy set, carrying out cooperative scheduling analysis on the adaptive response strategy set and the urban emergency resource network, generating a target cooperative scheme matched with resource supply and strategy requirements, and outputting an emergency response instruction containing an execution priority according to the target cooperative scheme. And the information is sent to a corresponding node to trigger a multi-main-body linkage response, so that the scientificity and the high efficiency of the emergency response of the smart city are improved.

The technology disclosed relates to a method, system, and non-transitory computer-readable media that detects malicious communication between a command and control (C2) cloud resource on a cloud application and malware on an infected host, using a network security system. The network security system reroutes the cloud traffic to the network security system. The incoming requests of the cloud traffic are directed to a cloud application in the plurality of cloud applications, and wherein the cloud application has a plurality of resources. The network security system analyzes the incoming requests, determines that the incoming requests are targeted at one or more malicious resources in the plurality of resources. Also, the network security system prevents transmission of the incoming requests to the malicious resources, by making the malicious resources unavailable for receiving future incoming requests, while keeping other resources in the plurality of resources available for receiving the future incoming requests.

A distributed system and method for compressing and restoring data across edge computing devices and cloud infrastructure is disclosed. The system dynamically adjusts compression based on available computing resources, network conditions, and now energy constraints. Edge devices monitor power consumption and battery levels, optimizing compression parameters to extend battery life while maintaining data quality. A workload scheduler prioritizes tasks based on energy availability, offloading intensive processing to cloud infrastructure when necessary. The system utilizes an energy-aware coordination layer to balance workloads across multiple devices, ensuring efficient data flow and long-term operational stability. Homomorphic operations allow secure distributed processing on compressed data, while an adaptive neural upsampler enhances reconstructed outputs. By integrating energy optimization, the system improves performance and longevity of edge devices in power-limited environments.

The invention provides a hybrid resource network port management method and a port management system, and the method comprises the steps: determining the number of available continuous port segments, the length of each available continuous port segment and the total number of available ports in a logic port pool of hybrid resources, and further combining the constraint length of the continuous port segments, measuring the discrete degree of available ports in the hybrid resources to obtain a port fragmentation index, and judging whether the hybrid resources meet a preset integration condition according to the port fragmentation index, if so, determining a to-be-integrated port section in the logic port pool, migrating services in the to-be-integrated port section to an available continuous port section, and if not, migrating services in the to-be-integrated port section to the available continuous port section. According to the method and the device, available ports in the ports are combined, and a continuous port interval obtained after combination is written into a logic port pool, so that port integration is realized, idle ports can be sorted out through service migration when the ports of hybrid resources are distributed dispersedly and discontinuously, and the problems that new connection is refused and the requirement of a large port cannot be met due to port fragmentation are avoided; and network performance is improved.

Disclosed are systems and techniques for wireless joint communications and sensing. For example, a network device can receive, from a network entity, at least one configuration signal indicating at least one configuration comprising a resource for a physical uplink control channel (PUCCH) and for sensing. The network device can output, for transmission to the network entity, the PUCCH based on the at least one configuration comprising the resource. The network device can transmit a sensing signal based on the at least one configuration comprising the resource.

The invention belongs to the technical field of computing power networks, and discloses a self-adaptive heterogeneous computing power network and an operation method thereof. The network comprises a computing network infrastructure layer which is used as the bottommost layer of the adaptive heterogeneous computing power network and comprises computing power resources and network resources; the resource abstraction layer is used for abstractly packaging computing power resources and network services provided by the computing network infrastructure layer; the computing network integrated layer is used for preprocessing and joining resource objects; the computing network brain layer is used for realizing collaborative scheduling of resources of the whole network; and the heterogeneous computing power network service base layer and the user service layer are used for integrating bottom computing power resources, network resources, intelligent arrangement and whole-network collaborative scheduling services into standardized services. According to the method, the existing heterogeneous computing power resources are constructed into the self-adaptive heterogeneous computing power network, the computing power bottleneck of a single computing power resource is broken through, meanwhile, a unified and more powerful computing power service is provided for a user, and the user does not need to care about the specificity of each computing power resource.

The invention provides a method and system for realizing transparent acceleration of AI load storage on a cloud native platform based on RDMA, and belongs to the technical field of cloud native. A network acceleration component is realized based on a dynamic resource allocation DRA mechanism, the network acceleration component comprises network acceleration plug-ins and network acceleration custom resources, the network acceleration plug-ins are containerized and deployed on each working node, a first storage component is deployed on at least one working node in a cloud native platform, and a second storage component is deployed on at least one working node in the cloud native platform. A first storage component uses a first protocol to provide a storage service for an AI application, and the scheme comprises the following steps: in a scheduling process of a service container, in response to that the service container is scheduled to a first working node, a network acceleration plug-in on the first working node generates a network acceleration configuration file based on a network acceleration custom resource, the first protocol is used for indicating that the service container is injected into a protocol stack of shared memory communication SMC-R based on RDMA when the service container is started, so that the service container can use the SMC-R to bypass the first protocol, and network acceleration based on RDMA is provided for the first storage component.

The invention relates to the technical field of power distribution network optimization, in particular to a dynamic optimization method and system for a power distribution network, and the method comprises the steps: collecting the operation data of each node of the power distribution network in real time through an intelligent electric meter; carrying out load flow calculation according to the node power balance equation set to obtain a branch power flow diagram; marking the branch with the active power exceeding the power threshold as a power abnormal branch; constructing a power distribution network resource network topology model; optimizing interconnection switch states of the candidate connection branches by adopting a genetic algorithm, and generating opening and closing combination configuration of interconnection switches; and the simulation load flow calculation is executed again, and if the stability condition is met, opening and closing combination configuration is executed. By introducing real-time load flow calculation, an abnormal path can be quickly identified when the power is abnormal. According to the method, a genetic algorithm is utilized, and an initial population design based on weighted sorting is combined, so that an optimal opening and closing combination with low power loss and low switching cost is realized, and dynamic and accurate state evaluation and optimization decision are realized.

The invention relates to the technical field of computer science and industrial engineering, in particular to a tool workshop scheduling method, which comprises the following steps of: acquiring production characteristics of a current tool workshop, generating an optimal batch scheduling strategy according to the production characteristics of the current tool workshop based on a preset evolutionary strategy, and automatically designing an algorithm by utilizing a preset large model, and calculating the minimum completion time and the maximum completion time of the optimal batch scheduling strategy, and completing tool workshop scheduling according to the minimum completion time and the maximum completion time based on the optimal batch scheduling strategy. Therefore, the problems of high workshop scheduling complexity and the like caused by strong coupling of batch division and working procedure sorting, strict constraint between working procedures and high resource networking degree in workshop scheduling are solved, an automatic algorithm design framework based on a large model is introduced, the expert algorithm design time is shortened, and the workshop scheduling efficiency is improved. Therefore, the efficiency and feasibility of aircraft tooling production scheduling are improved.

The invention discloses an entity chain store management system based on the Internet of Things, and the system comprises an Internet of Things sensing module, an intelligent analysis and decision support module, a dynamic resource scheduling module, a customer service optimization module, a safety monitoring and emergency response module, and an innovation marketing module. The Internet of Things sensing module is used for comprehensively sensing data of temperature, humidity and illumination in a chain store, positions and stock of commodities and browsing tracks and purchase records of customers. According to the entity chain store management system based on the Internet of Things, the dynamic resource scheduling module captures subtle changes of the stock quantity, the personnel state and the equipment parameters of each chain store in real time through high-frequency data acquisition once every five minutes, the limitation of data lag in traditional static scheduling is broken through, a virtual mapping model is constructed based on 1000 resource nodes, and the real-time performance of the system is improved. A local resource state of a single store is converted into a global visual digital resource network, so that a scheduling decision can cover resource pools of all chain stores.

The invention provides a transformer substation cabinet intelligent resource allocation and scheduling management method based on big data, and relates to the technical field of transformer substation cabinet resource allocation and scheduling management. The method comprises the following steps: acquiring historical operation data of a substation cabinet, and constructing a cabinet resource network topology model; and establishing a cabinet resource three-dimensional evaluation model. And generating a dynamic optimal resource allocation pool. And generating an optimal resource scheduling route sequence. And generating a dynamically adjusted resource scheduling instruction. According to the method, the cabinet operation state data and the load dynamic change information are obtained in real time, the resource scheduling instruction for dynamic adjustment is generated according to the resource scheduling route deviation and the cabinet load deviation, good dynamic adaptability is achieved, various changes in actual operation can be quickly responded, and the resource scheduling efficiency is improved. The intelligent level and the overall operation efficiency of transformer substation cabinet resource management are further improved, and powerful support is provided for stable and reliable operation of a power system.

A distributed system and method for compressing and restoring data across edge computing devices and cloud infrastructure is disclosed. The system dynamically adjusts compression based on available computing resources, network conditions, and now energy constraints. Edge devices monitor power consumption and battery levels, optimizing compression parameters to extend battery life while maintaining data quality. A workload scheduler prioritizes tasks based on energy availability, offloading intensive processing to cloud infrastructure when necessary. The system utilizes an energy-aware coordination layer to balance workloads across multiple devices, ensuring efficient data flow and long-term operational stability. Homomorphic operations allow secure distributed processing on compressed data, while an adaptive neural upsampler enhances reconstructed outputs. By integrating energy optimization, the system improves performance and longevity of edge devices in power-limited environments.

The present disclosure relates to a method performed by a network node (101) for handling Configured Grant-Small Data Transmissions, CG-SDT, resources in a communications system (100). The network node (101) provides a configuration for common CG-SDT resources to a User Equipment, UE (105). The common CG-SDT resources are common to multiple UEs (105). The network node (101) provides information indicating which SDT resource the UE (105) should use. The SDT resource is the common CG-SDT resources or other Mobile Originated-Small Data Transmissions, MO-SDT, resources.

The invention relates to the technical field of enterprise resource management, in particular to an enterprise management optimization method and system based on big data, and the method comprises the following steps: capturing data of employee skills, a project execution process and customer interaction according to enterprise resource planning in an enterprise, analyzing the captured data, and carrying out the multi-dimensional information classification processing; and constructing an enterprise resource comprehensive data set. According to the method, the flow and utilization efficiency of the resources is improved by establishing the graphic database to integrate the enterprise resources and converting the data elements into the network nodes and the connecting edges, and the resource flow and utilization efficiency is improved by identifying and analyzing the association strength of the nodes and the weights of the edges in the resource network. Through detailed flow path analysis, an enterprise can formulate a scientific resource allocation strategy according to market requirements and current resource situations, resource allocation and use are optimized after dynamic resource allocation is implemented, the strategy is adjusted in time by comparing and analyzing resource efficiency and cost effectiveness before and after optimization, and the market response speed and competitiveness of the enterprise are remarkably improved.

The invention discloses a data-intensive workflow dynamic task allocation method and system oriented to cloud-edge cooperative computing, and relates to the technical field of cloud computing and edge computing collaboration.The method comprises the steps that workflow and cloud-edge resources are modeled in a unified mode, and a comprehensive cost function containing time, economy and energy consumption is defined; analyzing the workflow to obtain a conflict task set and a maximum data path set; in combination with resources and network states monitored in real time, candidate nodes are filtered according to data privacy constraints, edges or cloud nodes with the minimum comprehensive cost are selected for tasks for allocation, and sorting is performed according to priorities during multiple tasks; the system comprises a resource monitoring module, a workflow analysis and preprocessing module, a cost evaluation module and a dynamic scheduler module, the modules cooperate to realize dynamic allocation, the problems of resource heterogeneity, network dynamic and the like under cloud edge cooperation are solved, multi-objective optimization and privacy protection are balanced, and the workflow execution efficiency is improved.

The invention discloses a smart power grid and new energy collaborative distributed energy storage optimization regulation and control system, and relates to the technical field of smart power grid and new energy collaborative control. The system comprises a central processing module, a data acquisition and monitoring module, an intelligent scheduling and optimization module and a new energy automobile collaborative scheduling module. A multi-dimensional prediction model (covering weather, equipment state and user behavior data) is constructed based on machine learning, a distributed energy storage charging and discharging strategy is optimized in combination with a genetic algorithm and dynamic programming, power grid frequency modulation, voltage regulation and standby capacity requirements are coordinated, regional energy storage resources are integrated through a virtual power plant management platform, cross-space-time deployment is realized, and the power grid energy storage capacity management system is optimized. The new energy consumption capability is remarkably improved, the fluctuation risk of the power grid is relieved, the innovatively designed vehicle network interaction center supports orderly charging and reverse discharging cooperation of the electric vehicle, the peak regulation potential of a vehicle battery is aggregated, and a flexible and adjustable mobile energy storage resource network is formed.

The invention relates to the technical field of industrial data processing and resource scheduling, and discloses a garment intelligent manufacturing process dynamic optimization management system based on a large model, comprising an unstructured load waveform semantic analysis module used for receiving a load demand data stream and analyzing to generate a discrete load pulse sequence, calculating a semantic cognitive entropy representing the certainty of the load request; the resource capacity space-time constraint dynamic reconstruction module is used for generating a capacity slack variable based on the semantic cognitive entropy and embedding the capacity slack variable into the resource network load distribution model as a hard physical safety margin parameter; according to the invention, a direct mapping channel from a probability semantic space to a physical resource space-time environment is established, so that the problem of resource allocation mismatch caused by unstructured load fluctuation is effectively solved; and the stability and the self-adaptive balancing capability of the production resource network in dealing with the high-uncertainty load are improved.

Provided are communication methods, terminal devices and network devices. A communication method comprises: a terminal device receiving first DCI sent by a network device, wherein the first DCI is used for triggering a random access procedure or indicating a first PRACH resource. A network device sends first DCI to a terminal device to trigger a random access procedure or indicate a first PRACH resource, so as to provide the terminal device with additional random access opportunities or resources, thereby facilitating the rapid recovery of uplink synchronization, and also optimizing the allocation of network resources and improving the system flexibility.

Techniques for a computing resource network to send a packet through a processing flow (e.g., a service chain) according to an order of processing workloads (e.g., services) included in the processing flow, configured as an optimized service chain. In some examples, the computing resource network may include a policy evaluation engine configured to determine the best probabilistic outcome of an order of routing between the services that results in the lowest computational costs based on the probability that a given packet will be terminated / modified at one of the earlier processing workloads in the service chain, a prediction engine configured to determine the order of the processing workloads included in the processing flow based on a policy and / or telemetry data associated with the processing workloads, and / or an intelligent routing engine configured to route a packet between the one or more processing workloads included in a processing flow according to the order.

A method performed by a first network node is provided. The method is for handling Sounding Reference Signal, SRS, configurations in a wireless communications network. The first network node is comprised in a group of network nodes together with one or more second network nodes. The first network node shares (202) a set of SRS configurations in the group of network nodes. The set of SRS configurations comprises SRS resources to be used for SRS interference measurements. The set of SRS configurations is to be used by the network nodes in the group of network nodes to configure User Equipments, UEs. The UEs to be configured are UEs that are identified to be Downlink, DL, heavy traffic UEs comprising DL data, which has an estimated download time that exceeds a threshold. When the first network node obtains (203) data to be transmitted to a first UE, it measures (204) SRS interference. The SRS interference is measured on SRS transmitted by each respective UE identified to be a DL heavy traffic UE by the respective one or more second network nodes. The respective measuring of 15SRS interference is performed on resources according to an SRS configuration out of said shared SRS configuration set. The first network node then selects (205) a precoder for the data to be transmitted to the first UE, based on the measured SRS interference.

The invention discloses a VPN-based resource access control method and device, equipment and a medium, and relates to the field of computer network security, and the method comprises the steps: obtaining a target data packet corresponding to a current resource access request; analyzing the target data packet through the virtual private network to determine a target structural body corresponding to the target data packet; judging whether a resource access behavior identification code contained in the target structural body exists in a first preset list or not; if not, verifying the target structure body based on a corresponding verification rule in a second preset list, and adding the resource access behavior identification code and a corresponding verification result to the first preset list; if yes, verifying whether the current time meets the preset time period access condition corresponding to the current resource access request or not when the verification result represents that verification is successful, and if yes, performing resource access corresponding to the current resource access request on the target internal resource network through the virtual network card. The flexibility and efficiency of resource management are improved.

The invention relates to a resource networked modeling and scheduling optimization method based on a Neo4j electrical diagram database, and the method comprises the following steps: building a diagram database model based on a power system structure; flexible resources are extracted and managed based on a graph calculation method; and constructing a graph structure scheduling strategy system. Visual representation and multi-dimensional attribute nesting of the power system topology are achieved, real-time identification, dynamic updating and hierarchical classification are carried out on adjustable loads and controllable equipment, high expansibility, high real-time performance and high adaptability are achieved, the method can be widely applied to novel power systems represented by a power distribution network, a park micro-grid and a source-load-storage cooperative system, and the method is suitable for popularization and application. And accurate modeling and intelligent regulation and control of flexible resources are realized. Compared with an existing SQL (Structured Query Language) or relational database scheduling mode, the method has remarkable advantages in the aspects of modeling granularity, response speed and system integration.

Disclosed are techniques for wireless communication. In an aspect, a wireless node establishes sidelink connections with a set of UEs. The wireless node transmits, to a network component, a request for sidelink resources for a sidelink position estimation procedure associated with the set of UEs. The network component determines an available sidelink resource pool for the sidelink position estimation procedure in response to the request. The network component transmits, to the wireless node in response to the request, an indication of the available sidelink resource pool for the sidelink position estimation procedure. The wireless node transmits, to each UE in the set of UEs, a sidelink resource configuration that is based on the available sidelink resource pool for the sidelink position estimation procedure.

This invention discloses a novel intelligent sharing and optimization method and system for aesthetic education resources based on a cloud platform, belonging to the field of resource sharing technology. By deploying multiple types of sensors in the classroom to collect teaching environment data and construct a classroom environment feature vector, a resource environment demand vector is established, and the environmental adaptability between the classroom environment and various aesthetic education resources is calculated. The network link status between the classroom terminal and the cloud platform is detected in real time, and parameters such as bandwidth, latency, and packet loss rate are obtained. Resource network matching coefficients are calculated, and the environmental adaptability and network matching coefficients are further integrated to construct a comprehensive resource availability. A resource scheduling priority sequence is generated by combining course matching degree and concurrent call status. This achieves intelligent sharing and dynamic optimization scheduling of aesthetic education resources in multi-classroom scenarios, improving the environmental adaptability and transmission stability of resource sharing, and enhancing the utilization efficiency and teaching implementation reliability of aesthetic education resources on the cloud platform.