45results about How to "Realize Distributed Computing" patented technology

The invention discloses a client distributed path computation method based on a software defined network architecture. The client distributed path computation method comprises the following steps: a user obtains network topology and linkcost information from a network controller through a northbound interface of a controller, calculates the path on the basis, determines route and flow distribution, and submits a route request to the controller; the network controller is responsible for updating the network topology and link state, and issuing a flow statement to data forwarding equipment according to the route selection of the user. Through the route calculation, the user participates in the network control to achieve the distributed optimization during the use process of network resources; meanwhile, the controller provides network topology information for the user, and can achieve the virtualization of the network resources and functions for the user and the optimal configuration and the integrated use of the network resources. Through the application of the method, the user can get great rights within a network safety range, the network transparency is improved, and an SDN frame serves for the user.

The invention relates to a flexible direct-current transmission real-time simulation system and a simulation method thereof. The flexible direct-current transmission real-time simulation system comprises a real-time simulation platform, an A-set device and a B-set device. The A-set device comprises an A-set valve control device, an A-set valve control signal conversion device, an A-set converter valve control device, an A-set station control device, an A-set direct-current protection device, an A-set direct-current measurement and control device, an A-set alternating-current measurement and control device and a centralized wave recording device, wherein the real-time simulation platform is connected with the A-set valve control device through the A-set valve control signal conversion device, the A-set valve control device is connected with the A-set converter valve control device, the A-set converter valve control device is connected with the A-set station control device, and the real-time simulation platform is respectively connected with the A-set direct-current protection device, the A-set direct-current measurement and control device and the A-set alternating-current measurement and control device through a signal interface screen and connected with the centralized wave recording device. The B-set device and the A-set device are the same in composition and connected relation of the devices. According to the flexible direct-current transmission real-time simulation system, the steady state and transient characteristics of the flexible direct-current system can be precisely simulated.

The related new intelligent mobile phone with high-performance computation, grand-scale memory, and high-speed transmission comprises: a baseband processor chip, a RF module, a SDRAM, a LCD, a keyboard, and some DAC and ADC circuits, wherein a built-in 8-pin intelligent card socket accords with ISO / IEC 7816 standard and connects with C4, C6, C8 pins of socket as well as the baseband processor chip pin; a power management chip provides 3.3-5V voltage for all modules in phone, as well as the SIM card through intelligent card socket C1 pin. This invention supports GSM communication, has massive data storage and high-speed data communication, and supports new application in intelligent card.

The invention provides a dose distribution simulating method for a nuclear installation decommissioning radiation field. The method comprises the steps that firstly, the geometrical information, which needs to be simulated in a nuclear installation decommissioning scene, of the radiation field, the position of a radiation source and the geometrical information of a shielding object are determined; secondly, according to the position of the radiation source and the position of the shielding object, a dose monitoring point distributing network is built and used for extracting sample data; thirdly, according to whether there is the shielding object in the radiation field or not, dose distribution calculation is divided into partitioned dose calculation and non-partitioned dose calculation; fourthly, according to the sample data, a radial basis neural network model is built; fifthly, the dose value of any one point is calculated through an inverse distance weighted method; sixthly, the dose distribution of the radiation field is calculated. By means of the dose distribution simulating method, no radiation source term models are needed, the dose distribution of the radiation field is calculated by depending on a small amount of dose monitoring points, and the steps are simpler. By means of the dose distribution simulating method, the calculation of the dose distribution of the radiation field affecting the shielding effect is achieved.

The invention discloses a power station boiler combustion modeling and optimization method facing mass high-dimensional data, belongs to the field of power station boiler optimization running and particularly relates to the power station boiler combustion modeling and optimization method facing the mass high-dimensional data. The method comprises the following steps that 1, input data and output data are extracted from history running data of a power plant distributed control system and subjected to data processing; 2, the NOx emission amount is modeled to a boiler combustion emission model byusing an improved distributed extreme learning machine, and the boiler efficiency is modeled to a boiler combustion efficiency model; 3, the boiler combustion emission model and the boiler combustionefficiency model are combined, and a multi-target boiler combustion model is established; 4, the multi-target boiler combustion model is converted to a single-target boiler combustion model by adopting a weight coefficient method; 5, the single-target boiler combustion model is subjected to parameter optimization by using a distributed particle swarm algorithm, and optimization control over the boiler combustion process is achieved.

The invention discloses a distributed parallel co-simulation architecture. According to the architecture, a simulation soft bus is defined, simulation software is interconnected with the simulation soft bus, and data cooperative interaction between the simulation software is completed through the simulation soft bus. According to the architecture, co-simulation tasks are decomposed from two dimensions of space and time, so that the simulation efficiency is improved, and multiple simulation tasks are supported to be executed concurrently at the same time through the partitioning technology of the simulation soft bus. According to the architecture, concurrent execution of multiple simulation tasks established by multiple users is carried out through a design environment; distributed computing and multi-node interconnection is achieved through the established simulation soft bus. Data interaction with the simulation soft bus is performed through an independent model interface relative tothe simulation software so as to complete collaborative calculation of the simulation software; and parallel synchronous resolving is completed through a simulation scheduling engine.

The invention provides an extensible microphone array and an establishment method thereof. A synchronous sampling mechanism between chips is adopted, and adc synchronous sampling of the chips is controlled through a gpio, so that a phase difference of multi-channel adc data is ensured to be within 1 microsecond, and synchronous operation of the chips is guaranteed; each chip independently completes noise reduction, echo cancellation or a beam forming algorithm of the double microphones, so that the system overhead can be effectively dispersed, and the load of the whole system is reduced; audiodata processed by a single slave chip is cascaded and transmitted to a main chip to complete algorithm processing of the microphone array, the system is expanded to any number of microphone arrays through cascading, and meanwhile, the chips form the microphone array, so that compared with a single-chip multi-adc scheme, the cost is lower and the expansibility is more flexible.

The invention discloses a geographic cloud data based spatial matching method. The method comprises: step 1, establishing a distributed spatial database, and performing point cloud data registration by adopting an ICP algorithm, wherein in the point cloud registration process, the adjustment is performed with a multi-point least square fitting method, and a transformation matrix is calculated; and step 2, performing target matching of entities same in name. According to the method, geographic information, spatial position information and attribute information can be quickly queried and called, and accurate matching of spatial data is realized by further applying a method for matching the entities same in name; and meanwhile, the least square fitting is also adopted, and the transformation matrix is calculated through a plurality of points in a three-dimensional space, so that the calculation method is simple, and the registration precision can be effectively improved.

The invention provides a real-time efficient distributed virtual-real combination simulation system, which comprises a front-end simulation device used for constructing one or more environments including but not limited to a simulation design environment, a simulation operation management environment and a virtual experiment environment; a distributed server cluster, the load balancing device selecting a distributed server to receive the simulation task. The distributed server comprises a simulation deployment unit, a model calling unit and a simulation model unit which are connected with thesimulation soft bus. The model calling unit and the simulation model unit are respectively connected with the simulation soft bus and used for calling an API function, and the simulation deployment unit distributes simulation tasks to the model calling unit and the simulation model unit for processing. According to the method, different simulation task concurrence documents can be established formultiple users based on a design environment, and distributed computing, multi-node interconnection and interaction can be realized through the established simulation soft bus. And parallel synchronous calculation can be realized through an engine in the simulation calling unit.

The invention relates to a transmission network-distribution network joint fault analysis method based on an improved node method. The fault analysis method comprises the steps that 1) electrical parameters of a transmission device are acquired, and electrical parameters and control parameters of direct current transmission, a direct current power supply and a distributed power supply are acquired; (2) a coordination transmission network-distribution network is decomposed; (3) a phase sequence hybrid network is established; (4) a disconnection line of a vertical fault line is replaced by a constant current source branch with the current of 0, and a constant voltage branch with the voltage of 0 is added to a short circuit node of a crosswise fault line; (5) a transmission network and a distribution network are respectively modeled, and current and voltage electrical parameters of a failed network are calculated; and (6) a node voltage error is calculated, and if the error is greater than 10-8.p.u., returning to step (5) is carried out for recalculating. According to the technical scheme of the invention, by studying the mutual influence of the fault calculation of the transmission and distribution network, a phase sequence hybrid circuit is processed; and non-linear characteristics under various faults are analyzed.

The invention discloses a high-speed big data integration system. The high-speed big data integration system comprises an information acquiring module, an information integrating and analyzing module, a data integrating module and a data service module. The information acquiring module is used for acquiring data from multiple data sources. The information integrating and analyzing module is used for analyzing and processing the acquired data. The data integrating module is used for classifying and integrating the data processed by the information integrating and analyzing module. The data service module is used for interfacing with users. By an information integration process, the linked and acquired data are subjected to formatted processes including duplication eliminating, semantic analysis, automatic clustering, automatic classification, Chinese word segmentation, segmentation finishing and the like, and then are integrated and classified by an intermediate platform for system information integration, so that users can apply the data by means of retrieving, querying, analyzing, monitoring, subscribing for and the like.

The invention discloses a distributed realization method of a quadratic function based on an ADMM (Alternating Direction Method of Multipliers) algorithm. The distributed realization method comprises the steps of: starting a system; reading in coefficient matrixes A and b of a quadratic objective function (FORMULA as shown in the description); dividing data into N processing blocks; calculating each processing block by using a distributed updating expression of the quadratic function; after completing calculation, summarizing results of the processing block to complete a calculation process. According to the distributed realization method of the quadratic function based on the ADMM algorithm, the distributed updating expression of the quadratic function is derived on the basis of distributed updating of an LASSO (Least Absolute Shrinkage and Selection Operator) by using a relation between a quadratic function expression and an LASSO expression, the distributed calculation of the quadratic function which is an objective function is realized in the background of big data, the calculation speed is greatly improved.

The invention discloses a tower bolt state monitoring method and device based on a stack type support vector machine. The tower bolt state monitoring method comprises the steps: collecting and preprocessing a vibration data set; carrying out wavelet transformation on the preprocessed data to construct a feature vector; training an adversarial support vector machine A-SVM by using the feature vector; training a stack type support vector machine S-SVM; and utilizing the trained stack type support vector machine model to realize automatic monitoring of the tower state. The tower bolt state monitoring device is characterized in that the input end of an AD acquisition module is connected with a vibration speed sensor; the output end of the AD acquisition module is connected with an FPGA; the FPGA is connected with a GPRS module and an up2 development board; the up2 development board sends a control instruction to a control relay module, and the output end of the control relay module is connected with the vibration speed sensor; and the GPRS module communicates with a far-end upper computer. According to the tower bolt state monitoring method and device, the safety state of the tower canbe monitored, and accidents caused by bolt loosening of the tower are reduced, and safe and stable operation of the power tower and a power transmission line is guaranteed.

The application discloses a charger power control system and a corresponding power control unit. The charger power control system comprises a plurality of power control units. The number of the plurality of power control units is more than two; the plurality of power control units are configured to communicate with each other to form a network and each has a unique address in the network. Each ofat least parts of the plurality of power control units is used for being connected between a charger main control board and a charging gun. And each power control unit is used for adaptively adjustingthe requested charging current of the vehicle connected with the charging gun according to the change of the available capacity and load of the power supply in the network, so that the total power ofthe charger in the network does not exceed the available capacity of the power supply. According to the invention, the utilization rate of the power supply can be improved, the power utilization safety is ensured, and the expansion and supervision of the charging system are facilitated. The system is also suitable for application of bidirectional charging and discharging V2G of the electric automobile.

The embodiment of the invention discloses a deep learning calculation method and a device, a chip and a medium. The method comprises the steps of obtaining an initial calculation graph; generating a reconstructed calculation graph according to the initial calculation graph; wherein the reconstruction calculation graph comprises a plurality of calculation node groups, and execution devices corresponding to different calculation node groups are different calculation clusters in the chip; and processing the reconstructed calculation graph through a plurality of calculation clusters in the chip. According to the technical scheme, distributed calculation in the chip is achieved, the calculation performance and the storage performance of all the calculation clusters in the chip are fully utilized, and the processing efficiency of the chip on the initial calculation graph is improved.

The invention discloses a hardware-in-loop test system and method for a reactive voltage control system of a new energy station. The system comprises a simulation host machine, a simulation target machine, an SVG controller and an AVC controller, wherein the simulation host machine, the SVG controller and the AVC controller are connected with the simulation target machine; the simulation host machine is configured to establish digital models including a power grid model, a photovoltaic power station model, an IGBT module and a transformer model, wherein the mathematical model is downloaded tothe simulation target machine after being subjected to code conversion; and the simulation target machine is configured to simulate the reactive voltage control system of the new energy field stationbased on a received model code and operate in real time. The effectiveness of an AVC substation control strategy and the dynamic response performance among the devices can be effectively verified under the condition close to the actual working condition.

The invention discloses remote control analysis equipment for a water turbine generator set. The equipment comprises two supporting plates, wherein notches are formed in the supporting plates, two supporting pieces are fixed at the upper ends of the supporting plates, the two supporting pieces are respectively positioned at two sides of the notches, a transverse plate is fixed at the upper ends ofthe two supporting pieces together, an oil cylinder is fixed at the lower end of the transverse plate, and the oil cylinder is positioned in the notches. The invention also provides a remote controlanalysis method of the water turbine generator set. According to the remote control analysis equipment for the water turbine generator set, the functions of remote state monitoring, remote fault diagnosis, remote operation guarantee, power generation production data analysis and equipment operation monitoring of the new energy generator set are realized, the intelligent optimization capability ofthe equipment operation parameters is improved, the safety and reliability of the equipment are improved, the intelligent degree of the generator set is improved, the maintenance cost of the equipmentis reduced, the market competitiveness of enterprises is finally improved, the wind power operation level is improved, and a traditional mode is promoted to be converted into an intelligent mode.

The invention provides an urban rail transit monitoring system and method based on a big data architecture. The urban rail transit monitoring system comprises a data access module, a data storage module and a monitoring module, the data storage module is used for storing the data in the data storage module; the data sharing module is used for constructing a data model for the stored data; the data visualization module is used for reading, writing and displaying the data according to the data model to realize data visualization; and the data analysis module is used for processing and analyzing the stored data. Through the big data frame platform, the functions, standardized data access, unified data storage, storage resource setting according to needs and dynamic adjustment configuration of the rail transit monitoring system are realized, the bearing capacity of the system is greatly improved, the system can be expanded according to different scales of the monitoring system, distributed calculation is realized, the resource utilization rate is improved, and the cost is reduced. The implementation is convenient in operation.

The invention relates to a real-time charging management method and system for cloud resources, the method comprises a data acquisition and cleaning step, a ticket uploading step, a distributed statistics step, a data storage step and a charging display step. The ticket uploading step comprises the step of generating a ticket according to cleaned cloud resource use data; the distributed statistics step comprises the steps of storing the ticket and extracting billing data of the ticket, the billing data is divided into a key unit and a value taking unit, the key unit comprises a tenant UUID to which a cloud server belongs, a cloud service type and a cloud resource type, and the value taking unit comprises the number of virtual CPUs, the number of virtual memories, the type of a disk, the size of the disk and the use duration; and according to the billing data, billing data with the same key unit are selected in a distributed manner, calculation is carried out, and a billing result is obtained. Compared with the prior art, the method and systm have the advantages that multi-dimensional customized statistical analysis and metering charging are realized, the processing of huge log information amount of cloud resources can be dealt with, the charging efficiency is improved, and the like.

The invention discloses a path planning distributed computation method based on iterative computations in a big data environment. The path planning distributed computation method comprises the following steps: (1) firstly, defining the correlation of a path planning request; (2) secondly, dividing clusters for the path planning request according to the correlation; (3) finally designing a distributed computing framework, performing parallel processing on request clusters with small correlation, and performing serial processing on requests with high correlation in one cluster. According to thecomputation method disclosed by the invention, the superiority of the algorithm in a load balancing aspect is maintained. Moreover, distributed computation is realized, the system processing speed isaccelerated, and traffic response requirements in the big data environment are met.

The invention discloses a cluster system fragmentation timing task scheduling method and a cluster system, and relates to the field of computer software. The cluster system fragmentation timing task scheduling method comprises the following steps that by acquiring a node number of a cluster node and a fragment serial number of a timing task, a cluster node accesses a distributed cache service to apply a certificate; the cluster node which applies for the certificate serves as scheduling node to obtain the scheduling control authority, and then the scheduling node selects the cluster node withthe corresponding number from the cluster nodes as an execution node according to the number of the fragments of the timing task; the node numbers of the execution nodes correspond to the fragment serial numbers; and the execution node obtains the execution authority of the fragments. The problem that in the prior art, resources cannot be fully utilized for distributed parallel computing when timed tasks are executed is solved, and one timed task is fragmented into a plurality of small tasks to be executed on a plurality of cluster nodes at the same time, so that cluster system resources are fully utilized, and cluster system loads are balanced, and timed task scheduling difficulty is lowered.

The invention discloses an edge artificial intelligence computing task scheduling method based on a peer-to-peer network, which comprises the following steps: broadcasting a request packet to other Internet of Things nodes of the peer-to-peer network according to an artificial neural network model, receiving reply packets which are sent by the other Internet of Things nodes and respond to the request packet, establishing a routing table between the Internet of Things nodes according to the reply packet, establishing associated information of the Internet of Things nodes and the calculation task of the artificial neural network model according to the routing table, and broadcasting a calculation task allocation packet according to the routing table, thereby realizing distributed calculationof the calculation task of the artificial intelligence calculation model at the edge end. The computing power of the Internet of Things node is fully utilized, the processing speed of the artificialintelligence computing task is improved, and various defects of completing the computing task of the artificial intelligence computing model at the cloud end are avoided.

Embodiments of the invention provide a target speed calculation method, a mobile edge calculation server and a storage medium. The target speed calculation method comprises the steps of determining whether an (i+1)th vehicle adjacent to an ith vehicle meets a predetermined boundary condition or not, wherein i is a positive integer; and when the (i+1)th vehicle meets the predetermined boundary condition, determining that the ith vehicle is a boundary for dividing a vehicle group; dividing the vehicle group on the basis of the boundary; and calculating target speeds of the vehicles in the vehicle group by the divided vehicle group according to a first risk threshold value corresponding to the vehicle group.

The invention discloses a verification information generation method, a verification information generation device, terminal equipment and a medium, and belongs to the technical field of communication. The method includes the steps of sending a data block generation instruction to a plurality of terminal based on the first filling data filled with the first to-be-processed data and the second filling data filled with the second to-be-processed data, and sending a data block iteration instruction to the first terminal sequentially based on a filling data block of the first filling data and a filling data block of the second filling data returned by each terminal based on the data block generation instruction and triggering the first terminal to generate first verification information corresponding to the combination of the first filling data and the corresponding filling data block and second verification information corresponding to the combination of the second filling data and the corresponding filling data block until the first verification information is the same as the second verification information. Thus, through task scheduling of the plurality of terminals, distributed operation of collision of the verification information is realized, and the efficiency of collision of the verification information is improved.

The invention discloses a computer network management system. The system comprises a network connection device, a management device, a cluster forming device, a cluster increasing device and a cluster eliminating device. The system controls one or more computers belonging to management computers, and each computer can form a cluster with another computer. By network management of the cluster increasing device and the cluster eliminating device of the cluster forming device, each of clusters in different scales is formed by at least one of various computers in a network system and increased on the basis of record information on a reservation table, and accordingly the clusters of any scales can be obtained easily.