87 results about "Resource network" patented technology

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 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.

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 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.

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.

The application provides a kind of algorithm network cooperative scheduling method and device, it is related to data processing technical field, comprising: obtaining the computing power resource under rail transit environment, network resource and the service information containing service priority;Based on the computing power resource, the network resource and the service priority, the comprehensive scheduling score is calculated, and according to the comprehensive scheduling score, target scheduling mode is determined;According to the target scheduling mode, the computing power resource and the network resource are cooperatively allocated to process the service information.

Techniques for a service of a cloud computing system to generate and present users with visualizations of computing resources and their network relationships in private networks of the users. The cloud system obtains resource and network connection data from private networks of users and determines overall network architectures for the computing resources and their network connections. The cloud system generates visualizations that represent the overall network architectures for private networks. The visualizations may comprise single pane-of-glass views for users to quickly understand the architectures and networking inside of their private networks. The visualizations may be interactive in that users can interact with visual depictions of resources and be dynamically presented with networking connections and details for the particular resources.

The invention discloses a resource scheduling method and device, electronic equipment, a storage medium and a product, and relates to the technical field of data processing. The method comprises the steps of obtaining a to-be-scheduled training job of a target large model, and determining a resource demand of each assembly line level in the to-be-scheduled training job; wherein the target large model adopts a cross-wide-area multi-dimensional parallel training strategy; obtaining an available resource state of each intelligent calculation center, and determining a target scheduling intelligent calculation center according to the resource demand and the available resource state; determining a mapping relationship between each assembly line series and an intelligent computing resource node in the target scheduling intelligent computing center, and taking the mapping relationship as an initial scheduling scheme and storing the initial scheduling scheme as an initial scheduling scheme set; determining a storage resource and a network resource state of each initial scheduling scheme, and updating the initial scheduling scheme set according to the storage resource, the network resource state and the resource demand; and taking the initial scheduling scheme in the updated initial scheduling scheme set as a target scheduling scheme to realize cross-wide-area scheduling of resources.

The application discloses a kind of based on heterogeneous computing resource network element instance deployment method, device and electronic equipment, the method is applied to cloud computing field, the method includes: receiving the computing power scheduling request of user, according to computing power scheduling request parsing the computing power configuration information and computing power scheduling operation of user required computing power;With the matching of computing power configuration information and network element metadata set, obtain matching result, wherein, network element metadata set is constructed according to the network element information corresponding to multiple heterogeneous computing resources in cluster;According to matching result and license information set, determine the computing power resources to be scheduled in cluster;According to computing power scheduling operation, the computing power resources to be scheduled are scheduled, and scheduling result is returned to user. By the present application, the problem that the network element instance deployment efficiency is low due to the different structure of computing power resources provided by different manufacturers and the dispersion of license information in the related art heterogeneous computing cloud platform environment is solved.

The application discloses an offline identity authentication and key negotiation method containing biological characteristics, and belongs to the technical field of shared resource network security. The method is completed by three parties of a user intelligent terminal device, an authentication server and a vehicle-mounted device, and contains user registration / vehicle initialization stage, user login / vehicle selection stage, user offline identity authentication and vehicle use stage, user vehicle return stage and user intelligent terminal loss and vehicle use stage. The main purpose of the application is to solve the problem that shared vehicles are scattered in different geographical spaces during use, leading to difficult management of physical vehicle keys. The core idea is to use the user's intelligent terminal as a key substitute for the shared vehicle, and through the introduction of biological characteristics, time stamps, random numbers and the like, the effects of preventing theft of the intelligent terminal attack, preventing replay attack, resisting key manipulation and the like can be achieved, so that the user experience and security are improved.

The invention belongs to the technical field of emergency treatment, and discloses an emergency treatment resource scheduling method and system based on artificial intelligence. The method comprises the following steps: constructing a resource network construction model, a resource network analysis model and a regional resource scheduling model; according to the real-time emergency treatment first aid resource data, a resource network construction model is used to construct a resource network, and a real-time emergency treatment first aid resource network is obtained; according to the real-time emergency treatment resource network, the resource network analysis model is used for carrying out resource data analysis, and a real-time resource data analysis result is obtained; and according to the real-time emergency treatment resource network, performing regional resource scheduling by using the regional resource scheduling model to obtain a real-time resource scheduling scheme. According to the method, the problems of low scheduling efficiency, poor real-time responsiveness and lack of a global view angle in the prior art are solved.

Offloading of data associated with vehicles can be managed. An offloader management component (OMC) associated with a backend server can predict a second location of a vehicle at a second time based on analyzing condition data indicative of a condition associated with the vehicle at a first time, prior to the second time, and location data indicative of a first location of the vehicle at the first time. Based on the predicting, and prior to the second time, OMC can instruct network equipment to authenticate a device associated with the vehicle and schedule a group of resources for use by the device at the second time. Based on the group of resources, the network equipment is able to provide a data communication service to the device at the second location to enable receipt of data by the backend server from the device via the network equipment.

A method at a UE supporting physical resource adaptation for initial access (e.g. a temporary increase of physical random access channel (PRACH) resources); the method comprises receiving PRACH configuration information from a first apparatus (e.g. a network node), the PRACH configuration information including at least one first PRACH resource applicable to any UEs and further PRACH resources applicable only to UEs supporting physical resource adaptation for initial access, wherein the further PRACH resources comprise at least one of a second PRACH resource frequency division multiplexed with the first PRACH resource(s), a third PRACH resource time division multiplexed with the first PRACH resource(s), or a fourth PRACH resource comprising at least one random access preamble comprised in none of the first PRACH resource(s), wherein the further PRACH resources are assigned a higher priority than the first PRACH resource(s); determining, by the UE based on the higher priority, to use one of the second, third, or fourth PRACH resources; and performing random access by using the determined PRACH resource. Also provided is a corresponding method at the first apparatus.

A computer-implemented method is executed using a global energy management system computer in an energy and load resource network, the energy and load resource network comprising a plurality of energy generating resources, a plurality of load resources, a plurality of energy storage resources, and a plurality of virtual energy resources, the computer-implemented method comprising receiving a plurality of energy market data and a plurality of capacity market data items; executing an inference stage of a trained machine learning model over the plurality of energy market data and the plurality of capacity market data items to output predictions of generating capacity and load requirements; executing an optimization algorithm over the predictions of generating capacity and load requirements to output a plurality of optimized generating capacity and load requirements; forming a plurality of operational instructions for a plurality of the resources in the energy and load resource network, the plurality of operational instructions being formatted to cause the plurality of the resources in the energy and load resource network to conform to the plurality of optimized generating capacity and load requirements; transmitting the plurality of operational instructions to the plurality of the resources in the energy and load resource network to instruct the plurality of the resources in the energy and load resource network to conform to the plurality of optimized generating capacity and load requirements.

Embodiments described herein can involve improved network security based on a distributed network access and logical permission architecture. In an example, a computer system can receive, by a first compute instance of the system from a user device, a request indicating an action to be performed on an element of a virtual resource. The first compute instance can be associated with a logical permission to use the virtual resource and may lack network access to the virtual resource. The first compute instance can cause a credential to be generated that enables the action to be performed on the element only and has an expiration attribute. The first compute instance can provide the credential to a second compute instance of the system which has network access to the virtual resource. The second compute instance can cause the action to be performed based on the credential and the network access.

This disclosure discloses a reference signal activation indication method and apparatus, relating to the field of communication technology. The method includes: sending activation indication information to a terminal device in a disconnected state, wherein the activation indication information indicates that a reference signal received by the terminal device within the activation time period corresponding to the activation indication information is active. This method sends activation indication information to the terminal device in a disconnected state via a network device, allowing the terminal device to receive the reference signal within the activation time period according to configuration information, thus saving power and resources. The network device can also flexibly configure the activation time period, reducing system overhead.

The application discloses a logistics cost management method and system based on shared storage resources, comprising the following steps: step one: collecting original actual operation data of each warehouse in the shared storage platform, and performing time alignment and correction processing; step two: establishing a shared storage resource network diagram; step three: obtaining multiple complete cost occurrence paths, calculating various logistics costs, and obtaining a logistics cost set; step four: inputting the shared storage resource network diagram and the logistics cost set into an improved Graphormer model, obtaining a storage cost time sequence attention result through a resource occupation coding module, a cost connection enhancement module and a storage time sequence attention module; and step five: extracting a high-cost conduction risk path according to the storage cost time sequence attention result, calculating a shared warehouse comprehensive cost, and obtaining a shared storage resource reallocation scheme. The improved Graphormer model is used to realize intelligent reallocation of shared storage.