The invention relates to the technical field of wireless meter reading and provides a data monitoring method of a remote wireless meter reading system The data monitoring method comprises the following steps that an acquisition terminal acquires electric meter data in fixed time and transmits the electric meter data to a concentrator; the concentrator generates an electric meter data index table according to a set electric meter number list, and stores the electric meter data in the index table and transmits the meter reading data to a data server by use of a front-end server; the data server gathers the meter reading data transmitted by the concentrator; a monitoring server calculates a dynamic line loss rate according to the meter reading data in the data server and determines whether the dynamic line loss rate exceeds a preset value; a promoting alarm is generated when the dynamic line loss rate exceeds the preset value, and when the dynamic line loss rate does not exceed the preset value, a line loss analytic statistical form is generated according to the calculated dynamic line loss rate The data monitoring method of the remote wireless meter reading system realizes monitoring on the whole remote wireless meter reading system, and consequently, the reliability of the whole remote wireless meter reading system is improved

Data monitoring method of remote wireless meter reading system

A mobile device, computer readable medium, and method are provided for allocating resources within a cloud. The method includes the steps of receiving metrics data associated with one or more tasks, training one or more models based on the metrics data to predict scores for tasks executed with a particular number of resource units, receiving a request that specifies a first task for processing a dataset, determining an optimal number of resource units to allocate to the first task based on predicted scores output by a first model, and allocating the optimal number of resource units to a resource agent in the cloud to manage the execution of the first task. The metrics data, which is collected by a plurality of cognitive agents, is received by a cognitive engine service in communication with the plurality of cognitive agents deployed in the cloud.

Learning-based resource management in a data center cloud architecture

Some examples described herein relate to testing of a cloud service. In an example, a cloud service to be tested may be deployed in a cloud system. A test load may be applied to the cloud service. Upon application of the test load to the cloud service, a determination may be made whether a performance metric related to the cloud service meets a pre-configured criterion. If the performance metric related to the cloud service meets a pre-configured criterion, the cloud service may be scaled. Operations of applying, determining, and scaling may iterated until an end condition is met, wherein the test load applied to the cloud service may vary after each iteration operation.

Testing a cloud service

The invention provides a large-scale electric power system node load flow computing method. A conventional admittance matrix and parameter vector data are stored in a key value pair mode; each Mapper reads a key value pair from a K-V database to act as input; each Mapper computes a Jacobi matrix element according to the input; the output results of all the Mapper are ordered for preparation of merging of Reducer; the Reducer merges the Jacobi matrix elements outputted by all the Mapper so that a matrix and a correction equation are formed; the equation is solved and a voltage phase angle is corrected; and the next round of iteration is performed through the correction results. According to the load flow computing method, the Jacobi matrix elements are parallel computed on multiple computers via a cloud computing platform so that superposition of computing power of multiple physical computers is realized, and computing efficiency, fault tolerance of the cloud platform and reliability of the computing results are enhanced.

Large-scale electric power system node load flow computing method

The invention relates to a scheduling optimal configuration method for a smart grid. The scheduling optimal configuration method for the smart grid is characterized in that the method comprises the following steps that: 1) an equivalent model is established, and with districts adopted as the unit, the number and locations of power supply power stations are determined by a power supply bureau, and the power stations are connected with each other in pairs through straight lines, such that an undirected weighted power station power supply model can be formed; 2) model parameter calculation is performed: according to the undirected weighted power station power supply model formed in the step 1), with distances between every two power stations adopted as parameters, calculation is performed according to a situation that AC power loss is 0.05kVA per km, such that a power loss matrix table between every two power stations can be made; 3) according to the power loss matrix table made in the step 2), a Dijkstra algorithm is adopted to perform smart grid resource scheduling simulation; 4) an ant colony algorithm is adopted to perform visualization calculation on the power loss matrix table made in the step 2) and perform simulation analysis on smart grid resource scheduling; and 5) a scheduling optimal configuration method for the smart grid is determined according to the step 3) and the step 4). With the scheduling optimal configuration method for the smart grid of the invention adopted, the quantity of calculation can be decreased, and acceptable scheduling configuration schemes can be obtained fast. The scheduling optimal configuration method for the smart grid has the advantages of fast convergence and high efficiency.

Scheduling optimal configuration method for smart grid

The invention discloses a massive real-time data load simulation testing cloud platform for a smart power grid, and a testing method of the cloud platform. The cloud platform comprises a virtualization unit, a central controller module and agent modules, wherein the virtualization unit virtualizes a plurality of virtual machines; the central controller module is arranged in the virtual machine to form a central controller; the agent modules are stored in the central controller in forms of virtual machine mirror image documents, and activated to form agency servers after the central controller is applied to start the virtual machines; the central controller applies the cloud platform for enough virtual machines according to testing plans in different scales; and agency server simulation monitoring devices send massive monitoring data to the servers, and test the performance of the servers. According to the cloud platform and the testing method, powerful computing resources of cloud computing are depended on; the massive virtual machines are applied from the cloud platform; the massive data of the smart power grid is tested by the agency server simulation monitoring devices; and the cloud platform has the benefits of simple structure, low input cost, convenience in use, wide testing range and good testing effect.

Massive real-time data load simulation testing cloud platform for smart power grid, and testing method of cloud platform

The invention discloses a smart power grid line loss detection method and system. The smart power grid line loss detection method includes the steps of obtaining monitoring data of a smart power grid, classifying monitoring values according to serial numbers of monitoring devices to obtain a plurality of monitoring sets, extracting the monitoring values within corresponding set time periods in the monitoring sets through a time coordination mechanism according to sampling time of the monitoring values, computing and outputting line loss values corresponding to the monitoring sets according to the monitoring values within the corresponding set time periods in the monitoring sets and a preset spatial-temporal model, enabling the lowest common multiples of sampling periods corresponding to all the monitoring values in the corresponding monitoring sets to serve as duration of the set time periods to extract the monitoring values, and synchronously computing the line losses through the extracted monitoring values within the same set time periods to guarantee the computing accuracy. The monitoring data of the smart power grid are obtained, coherent processing is carried out on the monitoring data, then the line losses are computed, the practical line loss condition can be truly reflected, and compared with a traditional smart power grid line loss detection method, the computing accuracy is improved, and the detection error is reduced.

Smart power grid line loss detection method and system

The invention provides a power grid large-scale topological structure construction method and system. A stored power grid topological incidence matrix table is read to obtain sparse matrixes, MapReduce is utilized to process the sparse matrixes so as to obtain a stored power grid topological node analysis result, a power grid topological island connecting relation is calculated to obtain a power grid topological network result, and finally a power grid large-scale topological structure is established according to the power grid topological network result. Strict processing computation and processing process are adopted in the whole process, and accurate establishment is ensured. By utilizing a MapReduce expansibility advantage, the matrix computation complexity is simplified, a computation ending condition is optimized, quickness of large-scale sparse matrix computation is ensured, the large-scale power grid topological structure is quickly computed so as to facilitate accurate analysis on topological nodes and topological islands of a large power grid, and finally the power grid large-scale topological structure is efficiently and accurately established.

Power grid large-scale topological structure construction method and system

The invention provides a mobile operation system working platform supporting an extreme network environment, and the mobile operation system working platform comprises a server engine and a client engine, wherein the server engine is used for monitoring an event for newly creating mobile operation in a server and acquiring a description file of the mobile operation; the description file is rendered to obtain a pre-rendered page according to the mobile operation page needed by the file; a recorded scrip is received and played back, and a request for submitting the page is uploaded to the server; the description file comprises a mobile operation type, mobile operation pages contained by each mobile operation type and a datasheet for storing the mobile operation; the client engine is used for caching the pre-rendered pages and intercepting the request for downloading the pages the request for submitting the pages; the request for downloading the pages is re-orientated to the pre-rendered page; the request for submitting the page is recorded as the script; and the recorded script is uploaded to the server engine until the normal connection of the network. Due to the adoption of the mobile operation system working platform, the reliability and normal work of the mobile operation can be guaranteed.

Chen Zhijian

Papers

Massive real-time data load simulation testing cloud platform for smart power grid, and testing method of cloud platform

Smart power grid line loss detection method and system

Power grid large-scale topological structure construction method and system

Mobile operation system working platform supporting extreme network environment