104results about How to "Meet computing needs" patented technology

The invention discloses a mobile edge computing task unloading method in a multi-user scene, and relates to the processing technical field of mobile computing systems. The invention aims to reduce response time delay and energy consumption of a mobile device. The multi-user scene is multiple mobile devices and an MEC server which are connected. Each mobile device can select one of multiple channels between the mobile device and the MEC server to perform communication. The MEC server is connected with a center cloud server through a backbone network. The method specifically comprises the following steps: constructing a multi-user scene task unloading model; and performing a two-stage task unloading strategy based on a game theory: the first stage unloading strategy is as follows: determining whether unloading is executed on the mobile device or on the MEC server, and the second stage unloading strategy is as follows: while MEC server resources are insufficient, determining whether to wait on the MEC server or execute on the center cloud server. The method is capable of, under the precondition of guaranteeing service quality and fairness of a user, considering personal requirements of the user.

The invention discloses a method for optimizing the new energy capacity ratio in layers in a power grid. The method comprises the steps that iteration calculation is conducted on the inner layer and the outer layer, a calculation model is that time series modeling is conducted on output force of the inner layer on the basis that the characteristics of new energy of a region are considered, the optimal energy-saving emission reduction benefit of the power grid serves as a goal, the factors such as the load characteristic, the unit peak shaving characteristic and the thermoelectricity coupling characteristics of heat supply units of different types are comprehensively considered, and therefore an annual time series production analog simulation model related to new energy power generation is established; the outer layer is provided with a capacity ratio optimizing model, the energy-saving emission reduction benefit of the model of the inner layer serves as a fitness function, so that the individual optimizing direction is updated, the new energy power generation ratio capacity is determined, the blindness of random generation of new energy installed capacity is reduced, and the optimizing efficiency and the accuracy are improved. The method for optimizing the new energy capacity ratio in layers in the power grid can be applied to new energy capacity optimization of the province-level power grid and has important guiding significance in grid source planning and practical power system dispatch of the province (region) power grid with the requirements for new energy installed capacity planning and low carbon electric power.

The present invention discloses an electric power load curve shape clustering method based on adaptive segmentation aggregation approximation. The method comprises: dividing an original electric powerload curve data sequence into a plurality of data segments, then calculating the total number of climbing events in a fixed time window and the total number of edge points extracted based on the slope, and performing dimension reduction on the electric power load curve through the total number of climbing events and the total number of edge points; when the dimension of the electric power load curve reaches the preset dimension, selecting cluster centers in the electric power load curve according to the number of clusters, and calculating the SBD distance from the load point of the electric power load curve to each cluster center; according to the SBD distance from the load point to the cluster center, dividing the load point into the category where the nearest cluster center to the loadpoint is located, and updating the cluster center of each category; and using the updated cluster center to cluster again all the load points of the electric power load curve until the number of iterations reaches the maximum number of iterations or the load points of all categories no longer change, and outputting a clustering result.

The invention belongs to the field of electric power system dispatching, and particularly relates to a dynamic probability dispatching method for calculating system operation reliability. A new method based on Benders decomposition is provided for solving the large linear programming problem formed by probability dynamic dispatching. Aiming at the coupling relation among various operation states and according to the idea of decomposition coordination, the original problem is decomposed by the Benders decomposition technology so as to form an iterative solution format formed by a dynamic economic dispatching main problem in a normal operation state and an operation state adjustment sub-problem in an accident operation state; and the solution scale of each optimized calculation is reduced. By the method, the solution speed of problems is increased, and the effective solution of large-scale systems is realized.

The invention discloses a method and device for controlling an air conditioner, and relates to the technical field of household appliances. The method and device are used for solving the problem thata conventional air conditioner comfort control algorithm is complex. The method includes the steps that the air temperature and relative humidity in a room where the air conditioner is located are acquired; the upper limit value and the lower limit value of the indoor temperature in an air conditioner working mode are acquired according to the relative humidity; the setting temperature of the airconditioner is determined according to the upper limit value and the lower limit value in the air conditioner working mode; and the operating frequency of a compressor of the air conditioner is controlled according to the difference value of the air temperature and the setting temperature. The method and device are applied to controlling the comfort of the air conditioner.

The invention relates to the technical field of server architecture, Provided are a device and a method for freely switching GPU server topology. The device includes a CPU board, GPU board, a PCIE board and a switch board. A PCIE Switch is arranged on the switch board. Four PCIE interfaces are arranged on the PCIE Switch, and the interfaces are used to connect with the CPU board, the GPU board andthe PCIE board. The switch board changes connection relationship among the four PCIE interfaces by switching working modes, and realizes changes of a topology structure of the GPU server. By introducing thea switch board and configuring the working mode of the switch board, the device and the method realize dynamic adjustability of a PCIE interconnection structure, so that a user can switch between two topological structures without changing an internal structure of the server under the condition of not opening a case, so as to satisfy computing requirements of different scenes, and improve operation and maintenance efficiency of a data center.

The invention discloses an algorithm processing module for a block chain, and the module comprises a PCIE interface chip which is used for communication with a PCIE interface of an outer main unit; a USB control chip which is used for communication with a USB interface of the outer main unit; an algorithm chip which is used for cooperating with an FPGA for ECC calculation, wherein the FPGA comprises a Microblaze module which is used for controlling the operation of each submodule of the FPGA; an FIFO-RAM interface module which is used for achieving the transformation from the USB interface to an internal RAM interface, reading data from the USB chip and storing the data in the RAM module; a Local Bus-RAM interface module which is used for the transformation from the Local Bus interface to the internal RAM module interface; the internal RAM module which is used for storing the downloaded data of a user and the data to be uploaded to the user; an ECC control module which is used for carrying out the transformation from an APB bus to an ECC chip asynchronous interface, and carrying out the call of the algorithm chip to carry out the dot multiplying and modular multiplication calculation; a noise chip control module which is used for the transformation from the ABP bus to the noise chip interface; and an iterative Hash calculation module which is used for carrying out the iterative Hash calculation and carrying out the parallel processing of the algorithm module.

The invention discloses a multi-GPU-based graph data processing system and method. The method comprises the steps of firstly dividing input original graph data through a data division module, and allocating the divided data to GPU devices; secondly allocating different computing tasks to vertexes on the GPU devices according to computing attributes through a task management module, and managing an executive sequence of various tasks; thirdly finishing execution of parallel computing tasks on the GPU devices while ensuring data consistency through an executive module; and finally finishing a data collection process after computing completion through a data collection module. According to the system and the method, a new task allocation and management mechanism is proposed; parallel efficiency is maximized; and communication is masked with computing, so that the problems of high GPU-CPU communication overhead and finite graph data processing scale of an existing GPU-based graph processing system are solved and the efficiency of processing large-scale graph data in a GPU is improved.

The invention discloses a monitoring and evaluation system of electrostatic deduster operation efficiency, and specifically relates to a system which is used to monitor on-line working condition of the electrostatic deduster and to evaluate a grade of working efficiency of the electrostatic deduster. The scheme in the invention includes that: a central processor is respectively connected with a high-voltage electric-field current signal collection interface, a high-voltage electric-field voltage signal collection interface, a smoke and dust flow velocity signal collection interface, a storage, a transmission interface, a display, an alarm and power supply through leads; in an automatic checkout device, a high-voltage electric-field current signal collector is connected with the high-voltage electric-field current signal collection interface, a high-voltage electric-field voltage signal collector is connected with the high-voltage electric-field voltage signal collection interface, a smoke and dust flow velocity signal collector is connected with the smoke and dust flow velocity signal collection interface. The invention is used for monitoring the working condition of the electrostatic deduster.

The invention discloses a mobile edge computing unmanned aerial vehicle group auxiliary communication method for explosive traffic, which is responsible for providing computing service for users withcomputing requirements on the ground when a network is congested. Firstly, in the prediction part, an improved PSO-BP neural network is used for predicting the calculation demand distribution situation of a target area, and unmanned aerial vehicles supporting mobile edge calculation are deployed as required; secondly, a demand partitioning part is calculated, a target area is partitioned based ona fairness principle, and each unmanned aerial vehicle serves users in a sub-area; and finally, the unmanned aerial vehicle energy optimization part in each sub-region performs joint optimization on the unmanned aerial vehicle calculation frequency and the unmanned aerial vehicle flight path on the premise of meeting the user calculation requirement, so that the energy consumption of the unmannedaerial vehicle in each sub-region is minimum, the energy consumption of the whole unmanned aerial vehicle group is minimum, and the service time of the unmanned aerial vehicle group is prolonged.

A method for migrate and dispatching a software-defined vehicle network computing task When the computational task generated in the vehicle driving process cannot be completed locally in time, the method requests the vehicle to communicate with all the vehicles within the one-hop range of the vehicle, and screens out the task migration execution vehicles according to whether the computational resource is satisfied and whether the task is willing to be executed. Requesting the vehicle to send the IDs of all the task execution vehicles to the controller; The controller establishes and maintainsflow tables for each of the execution vehicles and the requesting vehicle until the requesting vehicle receives the calculation results for each of the execution vehicles. The method also considers the robust computation and communication between the vehicle and the software-defined network controller in the software-defined vehicle networking. When the controller is unable to change the flow entries in the partial vehicle flow tables due to information overload, the agent module of the software-defined network controller in the base station executes instead of the controller.

The invention belongs to the technical field of ship motion measurement, in particular to a ship turning motion simulation method. The method does not model a propeller, but simulates propeller thrustwith virtual paddle volume force, and simulates ship or submarine rotating motion in water with integral dynamic mesh, so that the simulation precision is improved and the modeling difficulty is simplified, and accordingly designers and testers can quickly and accurately predict the hydrodynamic performance of the ship 's gyration.

The invention provides a BP (Back Propagation) neural network-based exhaust dryness computing method which aims at the problem of online computing the exhaust dryness of a large-scale USC (Ultra-Supercritical) unit in domestic. The BP neural network-based exhaust dryness computing method is characterized in that the exhaust dryness of a turbine under different working conditions can be computed by using a heat balance diagram of a real USC unit under different unit loads and different exhaust pressure working conditions as a basis, the exhaust dryness of the turbine can be finally, quickly and actually computed by utilizing a BP artificial neural network after a computing result is subjected to certain data processing, the obtained computing result is very close to an actual running value of the turbine, the computing accuracy is ideal, the model structure is simple, the computing is rapid, the purpose of online computing the exhaust dryness of the turbine under the real working condition can be achieved, an abnormal phenomenon of the exhaust dryness is prevented from happening, an operating person can be helped knowing the running state of the USC unit, and safe and economic running of the turbine can be maintained.

The invention discloses a domestic BMC (baseboard management controller) chip based SM4 algorithm IP (intellectual property) core design method. The method includes that an SM4 symmetric cryptographic algorithm is adopted from selection of cryptographic algorithms, the SM4 algorithm is realized by adopting hardware description language Verilog in specific design and is encapsulated into hardware IP core integrated into a domestic BMC chip, an interface of the IP core adopts the form in line with AMBA (advanced microcontroller bus architecture) bus interface specifications of ARM (advanced RISE machines), and the SM4 algorithm IP core is connected to an ARM processor in the domestic BMC chip through the AMBA bus. By the adoption of the SM4 algorithm IP core, high speed and safety can be achieved, requirements on protocols of IPMI (intelligent platform management interface), SSL (security sockets layer) and the like of the BMC can be met, integration level is high, hardware logic resources of the BMC chip are effectively utilized, extra costs of hardware cost and a mainboard are saved, requirements in establishment of trust chains of the trusted computing technology are met, and limitation of a TCM (terminal-to-computer multiplexer) itself is effectively made up.

The invention provides a power grid line fault diagnosis system and method based on a Hadoop cloud computing system. The system comprises a voltage transformer, a current transformer, a transmitter, a remote terminal, a modem, an optical fiber and a plurality of upper computers. The method includes the steps that voltage and current of a power grid line are acquired in real time and transmitted to the remote terminal; when a power grid has a fault, electrical quantity information of the power grid is acquired and transmitted to a NameNode in a Hadoop cluster and then is assigned to a DateNode to be processed in parallel, and the fault of the power grid is diagnosed and displayed. According to the power grid line fault diagnosis system and method, the electrical quantity information of the power grid line is fully utilized, the current, the wavelet signal rate of the voltage, the wavelet fault rate and the wavelet variation rate are calculated according to the result obtained after wavelet transform reconstruction based on multi-resolution analysis, fusion is performed to form a preliminary credibility distribution function, accurate division is performed after preliminary classification is performed on a fault power grid line set and a non-fault power grid line set, the fault power grid line set is determined finally, and fault power grid lines can be accurately judged out.

The invention discloses a distributed computing method and device, computer equipment and a storage medium. The method comprises the steps of acquiring deployment information of distributed memory grids, and correspondingly setting distributed message queues according to the deployment information; obtaining computing tasks transmitted by message middleware, and executing the computing tasks in corresponding computing nodes of the distributed memory grids to obtain computing results; and obtaining computing logic corresponding to the computing results, and storing the computing results in corresponding to-be-stored nodes in the distributed memory grids. According to the method, the distributed message queues are formed by utilizing local cache addresses of the distributed memory grids; anddata stored in each distributed message queue is sufficient to meet the computing requirements of the current grid, and does not need to be obtained from other distributed message queues, so that thecomputing efficiency of distributed computing is improved.

The invention relates to a method for monitoring and evaluating the operational efficiency grade of a bag type dust remover, in particular to a method for monitoring the working condition of the bag type dust remover in a real-time online manner and for evaluating the operational efficiency grade of the bag type dust remover. The invention aims at solving the problem of providing a system for real-time monitoring of operational efficiency of the bag type dust remover and for quick evaluation of the operational efficiency grade of the bag type dust remover. The adopted scheme is as follows: the system comprises an automatic monitoring device for the operational efficiency grade of the bag type dust remover and a computer system, wherein the automatic monitoring device can realize the automatic real-time measurement of a working current value of a draught fan and the pressure difference value between an inlet and an outlet of the bag type dust remover in working and communicates with the special computer system; through the communication between the computer system and the automatic monitoring device, the setting and the data transfer of the automatic monitoring device are completed; and the quick evaluation of the operational efficiency grade of the bag type dust remover is realized by storing, analyzing and evaluating collected data in real time and outputting evaluation results through the computer system.

The invention relates to the field of wideband identification of ground radar, in particular to a method for extracting a wideband high-resolution range profile based on energy and peak combination. Atarget is preliminarily tracked in a narrow-band mode, and signals are identified in a broadband in the next scanning period. The identification includes an energy summation and maximum peak selection step, an energy and peak combination step, and a target extraction step. Different identification mode weights are adopted in the identification process. Thus, the probability of target extraction is improved.

The invention discloses a real-time risk control variable calculation method based on a distributed streaming calculation engine. The calculation method comprises the steps that a data source configuration module is adopted, wherein a user configures data sources of various calculation dependencies; The variable definition module can be used for developing various variables by using SQL (Structured Query Language); The variable calculation module is used for capturing data from a pre-configured data source according to the calculation request, constructing a memory table, executing all SQL ina related variable pool once, and outputting a calculation result; According to the real-time risk control variable calculation method based on the distributed streaming calculation engine, the variable calculation engine and system researched and developed by the invention are adopted, personnel (such as business personnel and developers) familiar with SQL language can define variables through the system, the variable development period is greatly shortened, and the cost is saved.

The invention discloses a fitness facility exercise data acquisition system based on a distance measurement technology. A fitness facility comprises multiple weights; a hole is formed in each weight;the exercise data acquisition system comprises a bolt, a data acquisition module and a power supply device; when in use, the bolt is inserted into the hole of the weight to be selected; the data acquisition module comprises a shell, and a distance measuring sensor and a control module arranged in the shell; the distance measuring sensor is used for measuring a first distance between the data acquisition module and the bolt when the weight to be selected is located at a static non-suspended state; the control module is used for calculating the total weight of the weights carried by the bolt according to the first distance and a weight-distance relation mapping table; the distance measuring sensor is further used for measuring a second distance between the data acquisition module and the bolt when the weight to be selected is located at a motion state; and the control module calculates real-time exercise journey data of the weight to be selected according to the first distance and the second distance. Therefore, the exercise data of the weights can be accurately measured, demands under different scenarios can be met, and the universality is high.

The invention discloses an AI unloading optimization method for a random task in an industrial Internet of Things, and the method employs an unmanned plane as an edge server, builds an edge calculation entity network of a two-layer unmanned plane, is used for unloading and processing the random task, and optimizes an edge calculation model through an artificial intelligence method. An NIO method is provided for specific task processing and energy consumption when a communication fault occurs in a production area in a dangerous operation environment, and a system unloading model is constructed according to equipment information collected by an unmanned aerial vehicle under an edge computing entity network of two layers of unmanned aerial vehicles. Firstly, an unloading decision of nearby unloading of dense equipment and centralized unloading of dispersed equipment is proposed based on the distance, then energy consumption optimization processing is performed through a Lyapunov optimization method, and finally, an optimal scheme of computing resource allocation and unmanned aerial vehicle deployment is obtained based on a DDPG-G algorithm. According to the method, the problem of task unloading failure caused by communication faults in the dangerous operation process is solved, and system energy consumption optimization is achieved.

The invention discloses a multi-mode data transmission interconnection device for a coarseness dynamic reconfigurable array. The interconnection device comprises a multi-mode interconnection controller and a multi-mode interconnection structure module. The multi-mode interconnection controller is used for storing different interconnection structure selection information and sending the same to the multi-mode interconnection structure module. The multi-mode interconnection structure module is used for selecting one or more interconnection structures from the array according to the interconnection structure selection information of the multi-mode interconnection controller to realize computing element interconnection in the reconfigurable array. By the interconnection device, an interconnection structure meeting multiple computing needs and with good computing performance can be realized on the reconfigurable array; moreover, the interconnection structure has the advantages of easiness in extension, low power consumption and small area.

The invention discloses a three-dimensional space intelligent positioning and distributed edge computing support method for the Internet of Things, and the method comprises the following steps: acquiring energy consumption of each anchor node and distance information between each anchor node and Internet of Things equipment, and constructing an available anchor node set for each piece of Internet of Things equipment; according to the included angle relation between the anchor nodes, selecting four non-coplanar anchor nodes from the available anchor node set to construct a positioning unit set of the Internet of Things equipment; solving the positioning coordinates of the Internet of Things equipment through the anchor node information in the positioning unit set, and solving the position of the target Internet of Things equipment through the mean value of multiple times of positioning; acquiring positions and task request types of all the Internet of Things devices, dynamically executing path planning by using an unmanned aerial vehicle base station carrying an edge server so as to process requests of the plurality of Internet of Things devices, and determining an unloading decision; enabling the unmanned aerial vehicle to navigate according to the optimal path plan and get close to the Internet of Things equipment, and enabling the Internet of Things equipment to carry out edge calculation unloading.

The invention discloses a data center-based data display method and system. The method comprises the following steps of: creating a data display area; adding components in the data display areas; adding data on the components; realizing data linkage between related components through setting backhaul reference; and finally displaying the data. The system comprises a data display area creation module, a component adding module, a data adding module, a data linkage module and a data dissemination module. Through the method and system, powerful support can be provided for subsequent multilayer data display, manifestation of complex data is more sufficient, convenience is brought to big data analysis, and demands for calculating and monitoring various types of big data in real time can be satisfied.

The invention provides a road network matching method. The road network matching method comprises the steps of recording a first position point and a second position point of a target vehicle; acquiring all route segments in a preset area taking the first position point or the second position point as a central point; respectively calculating an included angle between a connecting line of the first position point and the second position point and each route segment, and determining the route segment corresponding to the minimum included angle; calculating the distance from the central point tothe route segment corresponding to the minimum included angle; and judging whether the distance is within a preset effective distance threshold range or not, and if so, determining the route segmentas a fitting road network route. According to the method, the operation process is simple, the required resources are few, so that higher operation efficiency is achieved, higher calculation precisioncan be maintained all the time to meet the calculation requirement, stream processing calculation can be supported subsequently, and good expansibility is achieved. The invention further provides a road network matching device, electronic equipment and a readable storage medium.

The invention discloses a reconfigurable pooling operation module structure oriented to a neural network, comprising a dynamic allocation module and a pooling calculation module, wherein the pooling calculation module is used for completing a calculation process of a pooling layer, realizes different pooling methods under the action of the dynamic allocation module and dynamically allocates a calculation scale of a pooling operation; and a current input queue reads from a data storage device after the previous calculation, and a result is transmitted to the data storage device by virtue of anoutput queue. The invention also discloses an implementation method of the reconfigurable pooling operation module structure oriented to the neural network, by virtue of an external control signal, each unit of the dynamic allocation module realizes control on the pooling calculation module, and according to control signal sent out by all the units, the calculation module carries out a corresponding calculation operation and gives an accurate output result. The technical scheme not only can reduce complexity and time of a calculation process and lower power consumption but also improves calculation flexibility and unit utilization rate.

The invention provides an FPGA-based flexibly configurable power grid data monitoring and processing system and method. The system comprises a data acquisition module and a data analysis module. Wherein the data acquisition module and the data analysis module are interconnected through a backboard PCIe X8 bus; The data acquisition module comprises an analog quantity acquisition unit, a switching value acquisition unit, a GOOSE message acquisition unit and an acquisition FPGA. And the data analysis module comprises a data processing FPGA, an optical port, an Ethernet port, an SATA hard disk, aDDR4 memory and a synchronous clock generation unit. According to the invention, the FPGA is used as a core device for power grid data processing; Data acquisition, data processing and data communication are all completed by the FPGA, which is different from the traditional FPGA + DSP + ARM architecture, the advantages of the FPGA in the aspects of algorithm acceleration calculation and programmable logic are fully exerted, and the power consumption, the flexibility degree, the integration level and the calculation capability are improved.

The data communication process between computing nucleus and external interface includes the following steps: 1. for the computing nucleus to set í‹input requestíŒ signal as valid when it allows and needs data input; 2. for the peripheral receiving the í‹input requestíŒ to respond the í‹input requestíŒ, set í‹input request response signalíŒ as valid and set the data on the í‹input data signalíŒ line; 3. for the computing nucleus to set data on í‹output dataíŒ end and set í‹output requestíŒ signal as valid when it allows and needs data output; 4. for the peripheral receiving the í‹output requestíŒ to respond the í‹output requestíŒ and set í‹output request response signalíŒ as valid; and 5. for the computing nucleus to set í‹busy processingíŒ signal as valid when it is busy in processing input data and there are new data to be input. The present invention has fast calculation speed, simple control, control based on the calculation completing condition, simplified relevant control and other advantages.

The embodiment of the invention discloses a graph data processing method and device, electronic equipment and a storage medium, and the method comprises: determining a target sub-graph and newly addeddata of the target sub-graph according to incremental graph data; determining incremental associated data of the target sub-graph according to the point data in the newly added data of the target sub-graph; determining new version associated data of the target sub-graph according to the incremental associated data and the original associated data of the target sub-graph; and determining new version detail data of the target sub-graph according to the new version associated data of the target sub-graph and the edge data in the target sub-graph, wherein the associated data comprises an endpointid and a sub-graph id, and the detail data comprises a sub-graph id, an edge id, an edge type, an edge associated endpoint id and an endpoint type. The calculation amount under the condition that thedata is continuously increased is greatly reduced, the graph data processing efficiency is improved, and a new thought is provided for sub-graph calculation.

The invention discloses a scheduling method for directed acyclic graph tasks in a heterogeneous multi-core processor system, and belongs to the technical field of computer systems. The method comprises the following steps: acquiring parameters; constructing a priority list of sub-tasks in the DAG; iteratively solving a parameter alpha; and updating the parameter alpha according to a scheduling result obtained by each iteration, terminating the iteration after a judgment condition is met, and outputting a task scheduling result. According to the invention, the task scheduling scheme is iteratively solved by improving the earliest completion time algorithm in the heterogeneous computing environment, the idle period caused by the communication overhead between the processors is reduced, the computing resources of the heterogeneous processors are fully utilized, the completion time of the whole task is shortened, and the real-time requirement of the task is met.