26 results about "Centralized computing" patented technology

The invention discloses a global cooperative control method and system for unbalanced fault ride-through of a multi-terminal DC power grid, and the method comprises the steps: constructing a fault detection model based on the positive sequence voltage drop depth and the negative sequence voltage size of the AC side of each converter station, automatically judging an unbalanced fault, and carrying out the fault detection when the fault is detected. Calculating the maximum allowable active current based on a fault station unbalanced current control model to carry out current limiting control on the converter station; and carrying out output power adjustment on the DC voltage deviation of the non-fault station based on the non-fault station power cooperation model. Asymmetric fault ride-through is carried out by using a constrained and optimized current instruction generation algorithm, and the capacity of a converter is dynamically and accurately distributed and utilized in a limited manner, so that the voltage support capability of a fault alternating-current power grid is maximized, and globally optimized adaptive power distribution is realized; the power of the non-fault station is calculated and distributed in a centralized manner and is dynamically adjusted according to the real-time available capacity, so that the self-adaption and global optimization of the running state change of the power grid are realized, and the fault handling toughness and reliability of the power grid are improved.

The invention discloses a retrieval question and answer method and system combined with a time sequence context, a medium and equipment, and belongs to the crossing field of artificial intelligence and intelligent operation and maintenance of an electric power system. Extracting time sequence semantic elements in the event sequence, and retrieving a corresponding event sub-graph in a time sequence directed graph constructed based on an event sequence, a time sequence and a logic link of the power equipment; performing feature aggregation on the sub-graph nodes to obtain context vectors; performing weighted fusion on the query semantic vector and the context vector to obtain a joint vector; calculating the similarity between the knowledge fragment vector set and each knowledge fragment in the knowledge fragment vector set, and screening out a plurality of most relevant knowledge fragments; and inputting the original query and the knowledge fragments into a text generation model, outputting answers, and completing questions and answers. By implementing the method and the device, the technical problem that an intelligent question and answer retrieval method in the prior art lacks pertinence and professional traceability of retrieval results in a complex event sequence scene in the power field can be solved.

This invention discloses a smart energy meter data acquisition device, method, equipment, and storage medium for carbon emission metering, relating to the fields of power system and carbon emission management technology. It includes a carbon emission hardware calculation circuit module that verifies frozen data and regional-level carbon emission factors and line loss power from the upper-level node to generate carbon emission data; a first non-volatile processor that stores the carbon emission data; and a second non-volatile processor that serves as a backup for the first non-volatile processor and stores frozen electricity data. The device of this invention expands the carbon emission metering function at low cost through the idle RS485 ports of existing smart energy meters, generates dynamic carbon emission factors through local calculation, accurately calculates carbon emission data, improves data accuracy, real-time performance, and reliability, reduces the burden of centralized calculation, lowers system transformation costs, supports large-scale application, and effectively promotes low-carbon transformation and green power management.

A comprehensive and centralized system for managing and tracking communications being delivered through both digital communications channels and physical communication channels. A centralized computing hub receives communication requests from originating systems of record or via user input. In response to receiving a communication request, content is generated or otherwise obtained and quality assurance checks are performed to assure that the content meets the requirements of the originating system of record and is applicable to the intended communication recipient. Once the quality checks have been performed and the content is deemed verified, delivery of the communications is orchestrated in accordance with the chosen communication channel. The timeliness of the communications is tracked throughout the system and alerts are generated in response to the timeliness exceeding thresholds. Thresholds are intelligently determined on a continuous basis using rolling averages of previous communications that result in predictions of future communication averages.

This invention discloses a distributed safety learning control method for mobile robot swarms, relating to the field of robot cooperative control technology. The method includes: calculating the future short-term motion position based on a nominal control quantity, and calculating the predicted neighborhood distance by combining it with a neighborhood state set to form multiple sets of safety constraints; combining these multiple sets of safety constraints with environmental boundary information to form obstacle avoidance constraints, and establishing constraint priority rules with actuator limits; obtaining a hierarchical safety constraint set by adding constraint level identifiers; using the nominal control quantity as the optimization objective and the hierarchical safety constraint set as the constraint conditions to construct a local optimization subproblem, and performing a distributed solution using the alternating direction multiplier method to generate a safe and feasible control quantity. This invention achieves the output of safe and feasible control quantities without centralized computation, thereby improving the real-time performance and reliability of swarm cooperative control.

The application discloses a regional power grid transformer group anti-short-circuit capability distributed checking system and method, and relates to the technical field of electric power engineering. The system comprises a distributed sensing module, a transformer group management and control module, a short-circuit checking module, a terminal centralized control module, and is distributed in a local power grid information measurement center, a transformer group management and control center and a regional power grid anti-short-circuit checking and analysis center. The application collects data and preliminarily judges faults through the distributed sensing module, filters faults and transformers to be checked through the transformer group management and control module, performs multi-physical field checking through the short-circuit checking module, and divides risk levels and gives early warnings through the terminal centralized control module. The application solves the problems of low static checking efficiency, difficulty in adapting to power grid changes and insufficient centralized computing power in the prior art, realizes dynamic and distributed checking, and improves the safety and stability of power grid operation and maintenance.

Embodiments described herein are directed to efficient content management within a data-processing environment. In some embodiments, data is obtained at an edge processor. The edge processor may perform data processing in proximity to generation of the data and provide the processed data to a centralized computing service for subsequent processing or storage. At the edge processor, basic data processing may be performed to process the data based on a basic data processing pipeline executed on the edge processor. The basic data processing pipeline may use basic data processing content to perform the basic data processing. The processed data may be provided to a component of the centralized computing service.

The application provides a train braking force distribution system, method and train, the system fuses a train ATO module and a CCU module to form an OCU module, and fuses a single-car corresponding TCU module and a BCU module to form a VDU module corresponding to a car, reconstructs a train braking force distribution system architecture, and can effectively solve the problems existing in the current distributed braking force distribution mode. The OCU module serves as a centralized computing core, analyzes and calculates the braking force demand values of each axle of the whole train according to the data uploaded by each VDU module, and issues the demand values to the corresponding VDU module, without the cumbersome connection process of the original TCU feeding back the actual electric braking value to the BCU and the BCU issuing an execution instruction again, so that the connection of the braking force distribution processes is smoother, the problem of poor process connection is effectively solved, and the distribution efficiency of the train braking force is improved.

The invention discloses a data interconnection method, an energy management system and a box transformer substation measurement and control device. According to the method, a data request instruction is sent to a box transformer substation measurement and control device, so that the box transformer substation measurement and control device transmits first total power of a low-voltage side of a box transformer substation to an energy management system; determining a power set value of each energy storage converter according to the first total power, and transmitting the power set value to the corresponding energy storage converter, so that the energy storage converter adjusts the current power according to the power set value; therefore, the total power of the low-voltage side of the box transformer substation is used as a regulation and control reference, the power set values are calculated and distributed in a centralized mode, multi-PCS load balance regulation and control are achieved, the possibility that faults occur in long-term full-load operation of the box transformer substation can be reduced, and the energy conversion efficiency can be improved. When the tripping signal of the box transformer substation measurement and control device is obtained, the emergency shutdown instruction is sent to the energy storage converter so that the energy storage converter can be shut down, and therefore when a fault occurs, a PCS operation loop is rapidly cut off, energy storage equipment is prevented from being further damaged, and the safety of the equipment is improved.

1. Name of the designed product: computer server. 2. Use of the designed product: the designed product is used to provide centralized computing, information publishing and data management services for network users. 3. Design points of the designed product: in shape. 4. Picture or photo best indicating the design points: perspective view.

This invention discloses a high-performance automotive-grade chip design method, a multi-domain fusion chip architecture, and a vehicle. Based on the functional requirements of the target vehicle, this invention divides the design into multiple independent functional control modules; integrates these multiple functional control modules into a single high-performance system-on-a-chip (SoC); establishes a sharing mechanism within the SoC for real-time data exchange between the multiple functional control modules; and uses a pre-defined packaging process to manufacture and package the designed chip, forming an automotive-grade chip product. This achieves centralized computing power and reduces overall size and weight, as well as the number of operating system maintenance requirements and application software splitting, through integration.

The present application relates to the technical field of power network remote monitoring, in particular to a multi-terminal cooperative control response method for hydropower substation monitoring nodes. The specific implementation process includes: collecting three-phase voltage and feeder current and calculating electrical power, and extracting electrical mutation data packets; based on the power mutation direction, the power backtracking transmission path is established, the power adjustment quota is calculated in combination with the reactive power compensation capacity and the load classification state, and the amplitude margin is generated; when the amplitude margin is greater than zero, the data packet is added along the path to the upstream, and the driving instruction sequence is generated to complete the cooperative power adjustment. The present application realizes the distributed allocation of the overrun power by constructing the power backtracking transmission path and the adjustment quota calculation based on the node state, reduces the congestion risk of the communication backbone link and the centralized calculation load, avoids the problems of excessive compensation or misoperation caused by isolated control, and improves the adjustment capacity and operation reliability of the power supply and distribution network.

Disclosed herein relates to a self-checkout anti-theft vehicle system, comprising: a self-checkout vehicle having a plurality of sensors and components implemented thereon, the self-checkout vehicle being used by shoppers for storing selected merchandises in a retail environment; and a centralized computing device. The centralized computing device is configured to: obtain information related to each merchandise selected and placed into the self-checkout vehicle by a shopper by exchanging data with the plurality of sensors and components via a first communication network, identify each merchandise via a second, different communication network based at least upon the information obtained from the plurality of sensors and components, and process payment information of each merchandise.

The invention discloses an AGI starry sky cloud platform system and method based on overlapping multiplexing of a future network / FN and OVXDM, and belongs to the crossing field of communication technology and artificial intelligence. The system comprises a terminal computing power layer, a network switching layer and an intelligent service layer, wherein the terminal computing power layer aggregates global idle computing power through data equipment and a digital center / FN root cluster; the network switching layer is integrated with an OVXDM communication module and an FN / m virtual-real routing addressing module, so that efficient transmission and accurate addressing are realized; and the intelligent service layer supports AGI task decomposition and execution through resource arrangement and task scheduling. According to the corresponding method, AGI training and reasoning are completed through task analysis, resource addressing, distributed calculation and hierarchical aggregation. The AGI computing power scheduling method solves the problems of high centralized computing power cost, low communication spectrum efficiency and the like, realizes low-cost and high-robustness global AGI computing power scheduling, and is suitable for a space-air-ground-sea integrated intelligent service scene.

The invention discloses an intelligent power distribution network self-healing method based on a reaction type multi-agent system, an intelligent power distribution network, terminal equipment and a storage medium, and belongs to the field of electric power systems.The method comprises the steps that when it is determined that a managed feeder area breaks down, a cross-feeder-area information request is sent to an area agent of an adjacent feeder area, and the cross-feeder-area information request is sent to the area agent of the adjacent feeder area; acquiring first local network information of an adjacent feeder line area; transmitting a mapping request to adjacent load agents in the managed feeder line area to obtain second local network information, and completing fault area mapping; according to the first local network information and the second local network information, determining an available tie line of the cross-feeder region; and controlling the tie line with the minimum power loss to be closed to recover power supply of the fault area. By implementing the method and the device, the problems that in the prior art, centralized calculation is difficult to meet the real-time requirement, the communication network bandwidth has huge pressure, the system expansibility and flexibility are insufficient, and a self-healing system is easy to break down can be solved.

Aspects described herein relate to a centralized computing system that interacts with a plurality of data centers, each having an edge server. Each edge server obtains sensor information from a plurality of sensors and processes the sensor information to detect an imminent shutdown and sends emergency data to a centralized processing entity when detected. In order to make a decision, the edge server processes the sensor data based on dynamic sensor thresholds and dynamic prioritizer data by syncing with the centralized computing system. Because of the short time duration to report emergency data before an imminent complete shutdown, an edge server may utilize a quantum data pipeline and quantum data storage as a key medium for all data transfer in a normal condition and at the time of emergency for internally transporting processed sensor data and providing the emergency data to the centralized processing entity.

This invention discloses an edge computing module for energy AI software in zero-carbon parks that integrates energy sources, grids, loads, storage, and cloud. It includes a software modular unit, a product database unit, an experience algorithm conversion unit, an integrated energy source, grid, load, storage, and cloud collaborative unit, and a security protection unit. Through distributed edge computing and decoupled modular construction, it achieves flexible expansion, builds a database of energy-saving models for all types of equipment to complete energy efficiency analysis and operation and maintenance diagnosis, transforms expert experience into AI management algorithms to optimize process control, and ultimately achieves three-level collaborative closed-loop control of energy sources, grids, loads, storage, and the cloud platform. This invention overcomes the shortcomings of traditional energy management systems, such as centralized computing power, module coupling, weak customization, and low energy-saving accuracy, significantly improving the real-time performance, flexibility, and intelligence level of energy management in zero-carbon parks. It is applicable to energy IoT management scenarios in various types of zero-carbon parks.

The present application belongs to the field of distributed computing power network and network security technology, and particularly relates to a fraud detection method in a distributed computing power network environment. The method comprises the following steps: step 1, training data preparation stage: each local platform in the distributed computing power network collects local computing power transaction data and corresponding classification labels, and obtains a fraud detection model of each centralized computing power network platform; step 2, distributed collaborative training stage; step 3, inference deployment stage: after convergence through multiple rounds of training, the local platform directly uses the trained local model to perform fraud detection on new computing power transaction records, without the need to communicate with the basic model. The present application has the following advantages: through knowledge alignment, the cross-model integration capability is improved; through customized knowledge learning, the generalization capability of the local model is enhanced; and the data and resource limitation problem is solved.

The present application relates to the technical field of virtual power plant, and discloses a virtual power plant optimization scheduling system and method, wherein the optimization scheduling system comprises a data acquisition module, an edge computing module, a prediction module, a scheduling controller, a topology reconstruction module and an intelligent terminal device cluster; the present application realizes the rapid generation and issuance of the scheduling scheme by localizing the processing and prediction of the sensing data through the edge computing module, avoids the response delay problem caused by network transmission and centralized calculation in the traditional centralized architecture, thereby supporting the millisecond-level scheduling feedback, significantly improving the real-time response capability under the condition of sudden load fluctuation or fault, enhancing the identification and trend prediction capability of the system to the meteorological disturbance, equipment aging and operation anomaly by constructing the multi-dimensional sensing and prediction mechanism based on the LSTM neural network prediction model, improving the intelligent level of the virtual power plant system, and the system can dynamically generate the optimal scheduling strategy to ensure the stable operation of the virtual power plant under various working conditions.

This application discloses an algorithm-driven method for efficient information propagation across multiple scenarios, comprising the following steps: 1) lightweight feature extraction; 2) edge computing deployment; 3) establishing an edge-cloud collaboration mechanism; and 4) implementing accuracy and latency control. This application effectively eliminates redundant features by combining lightweight feature extraction with PCA dimensionality reduction, balancing feature retention rate and computational efficiency, reducing resource consumption while ensuring matching accuracy. It deeply integrates the lightweight FM algorithm with edge computing, replacing centralized cloud computing, significantly reducing propagation latency and saving network bandwidth. It improves the edge-end collaboration and incremental synchronization mechanism, clearly defining responsibilities and achieving full coverage across multiple scenarios, enhancing adaptability to niche scenarios. It constructs a dual closed-loop control mechanism, dynamically adjusting parameters and strategies to achieve both matching accuracy and latency targets. The overall solution is simple and feasible, requiring no complex hardware support, adaptable to heterogeneous needs across multiple scenarios, and highly practical.

The invention provides a landslide deformation time sequence prediction method based on machine learning, and the method achieves the on-site collection and intelligent analysis of landslide deformation data through arranging a plurality of miniature edge calculation nodes in a sliding source region, a middle section and a front edge of a landslide body. A data acquisition module, a lightweight convolutional neural network and a wireless communication module are configured in each edge node, earth surface displacement, inclination angle, acceleration and ground strain signals are acquired in real time, and feature extraction and short-time deformation quantity prediction are executed locally; according to the method, a plurality of micro edge nodes with a calculation function are deployed in a landslide mass sliding source area, a landslide middle section and a landslide front edge, and the lightweight convolutional neural network embedded in each node can output a deformation quantity trend faster after sampling, so that second-level response to landslide deformation is realized; the central server only needs to fuse low-dimensional features to carry out global time sequence prediction, so that the overall delay is greatly reduced, and the problem of insufficient prediction real-time performance caused by the fact that centralized calculation depends on batch data processing is solved.

The application provides a processing method of an inference request and a storage medium, and relates to the technical field of computers. The processing method comprises the following steps: in response to the inference request, detecting a distributed computing power node and a centralized computing power node connected to a computing power scheduling platform; if there is a schedulable computing power node in the distributed computing power node, obtaining an evaluation result of the distributed computing power node based on a target parameter of the distributed computing power node, the evaluation result being used to indicate whether the distributed computing power node is available; obtaining a target computing power node in the distributed computing power node and the centralized computing power node based on the evaluation result of the distributed computing power node; and executing an inference task corresponding to the inference request through the target computing power node and outputting an inference result. The processing method can improve the stability when the inference task is executed in a controllable cost case by executing the inference task through a suitable target computing power node.

The invention belongs to the technical field of resource scheduling optimization, and provides a distributed AI reasoning method and system based on idle resources, and the method can continuously monitor the state of a terminal node in a resource pool and update the state information, can respond to a reasoning request, screens out a reasoning node from the resource pool, and sends a reasoning instruction to the reasoning node, thereby improving the reasoning efficiency. And enabling the reasoning node to create an independent reasoning environment, loading an AI model corresponding to the reasoning request in the reasoning environment to execute reasoning calculation, and correspondingly forwarding a reasoning calculation result based on the reasoning request after the reasoning calculation result is returned. According to the scheme provided by the invention, idle computing resources are effectively utilized, various reasoning requests can be quickly responded, efficient computing resource allocation is provided, dependence on cloud computing resources is reduced, delay in data transmission and computing processes is reduced, overall resource use is optimized, construction and maintenance of a large-scale centralized computing server are avoided, and the computing efficiency is improved. And the operation investment cost is saved.

The application discloses a kind of based on big data environment and realizes the identification control method of dish individualization, including the following steps: according to acquisition information, database is established;According to touch detection technology, the food species of identification and judgment is selected;Characteristic curve comparative analysis determines whether to update or correct database.The application has the advantages of avoiding food damage and fully utilizing the idle computing resources of networked equipment, improving the centralized computing power of server.

The application discloses a trusted data space system based on a hybrid computing architecture, relates to the field of big data and artificial intelligence, and is characterized in that the system is deployed locally at each data provider through a data connector, original data is stored in the local data connector in a decentralized manner, and the original data does not need to be uploaded to a central node in a centralized manner, a centralized computing mode is adopted in the computing layer, all computing tasks are executed in a security domain constructed by a central computing engine of an operator, data is transmitted to a container of the central computing engine in a required manner for temporary use, the container and the data in the container are destroyed after the computation is completed, and the data is not landed on the central node and is not stored in a centralized manner.

The present application relates to the technical field of data processing, in particular to a kind of multi-source communication link cooperative flight activity dynamic stream data high-speed processing method.Extract the list of stream data to be transmitted and determine the transmission requirement characteristics of stream data class based on the prior scene information of target flight area, combined with communication topology characteristics and link technical characteristics, double-target division is carried out on multi-source communication link, scene communication link list is obtained, and scene link performance feature library is correspondingly constructed;Input transmission requirement characteristics and scene link performance feature library into centralized computing node to make global task decision, obtain global transmission task abstract list;Synchronize global transmission task abstract list to multiple flight activity objects to execute data transmission task.Significantly enhance the stability of cooperative flight control and the high speed of data transmission in complex scene.