23 results about "Real-time data" patented technology

The present invention provides a steel cooling control simulation system and method. A process control device is connected with cooling equipment simulation device, the cooling equipment simulation device includes a transmission fixed with steel bar, a cooling course temperature calculating device simulating the temperature measurement and a patrol demonstrator, an electric potentiometer, a photoelectric switch and a position locator for temperature display, the photoelectric switch is provided on the operation path of the steel bar. The course control device completes the optimization setting function to PLC control variable, PLC completes the real-time control function of the cooling simulation course, the cooling equipment simulation device executes the real switch and adjustment action of the simulation cooling line, and achieving the real-time data collection course. The cooling control course system is simulated by the hardware connection of each device and software cooperation, providing a pilot test device of simulation evaluation in order to improving and completing the control system.

The invention discloses a failure prediction method based on ICA reconstruction, which includes the following steps: step 1, calculating a separative matrix W; step 2, calculating the statistic value I<2>(k), SPE(k) or I<2>e(k) of the real-time data Xnew(k) through adopting the formulas I<2>(k)=S'newd(k)<T> S'newd(k), I<2>e=S'newe(k)<T>*S'newe(k), SPE(k)=(xnew(k)-x'new(k))<T>*(xnew(k)-x'new(k)), S'newd(k)=Wd*xnew(k), and S'newe(k)=We*xnew(k), wherein Wd refers to the matrix formed by the lines expect the first d lines of the separative matrix W, We refers to the matrix formed by the lines except the first d lines of the separative matrix W, and X'new(k)=Q<-1>BdWd*xnew(k), Bd=(WdQ<-1>)<T>, Be=(WeQ<-1>)<T>, and Q refers to a whitening matrix; and step 3, calculating the nuclear density of I<2>(k), SPE(k) or I<2>e(k), and detecting failures as per the control limit. The method provided by the invention solves the problem that the traditional flue gas turbine prediction method can not utilize the multidimensional valid data, takes the multi-channel vibration data into consideration, can be used for directly predicating failures, and improves the prediction accuracy compared with the PCA reconstruction method.

The invention discloses a power system real-time picture Web interface showing method. The method mainly includes the steps that electrical equipment is monitored and state data of the electrical equipment are collected by a back-end server; the real-time data are rendered by a real-time picture issuing server to form a complete interface layer; after it is detected that a real-time picture issuing server website is input through terminal equipment, an interface control script is sent by the real-time picture issuing server to acquire the size and resolution ratio information of the terminal equipment, and a virtual picture same with the terminal equipment in size is drawn according to the size and resolution rate information and is sent to the terminal equipment through the virtual interface; the virtual picture is received by the terminal equipment, and the virtual picture is rendered through the canvas function of a browser kernel. Files suitable for the picture size of the terminal equipment can be output through the power system real-time picture Web interface showing method according to the screen size and resolution ratio information of the terminal equipment.

Disclosed is a real-time control method for dynamic material and energy consumptions, which comprises the following steps: collecting real-time data required by control of dynamic material and energy consumptions, and storing the real-time data into a PI real-time database under a PI real-time database condition; collecting laboratory analytical data required by control of dynamic material and energy consumptions, and storing the laboratory analytical data into a relational database of the analytical data; correcting the data of control of dynamic material and energy consumptions, and obtaining data conforming to material balance; initiating the control program of dynamic material and energy consumptions in different time periods, calculating and dynamically controlling the interdependence coefficient matrix model, and inputting the result of calculation into a reporting system so as to allow the users to use on the Internet. The present invention is capable of obtaining the energy consumption situation of a plurality of products and the whole production chain, and the energy consumption situation of the product in each step can be observed dynamically according to the data collected in real time.

The invention discloses a method of uplink and downlink message routing of a service gateway of internet of things, wherein the method of uplink message touting include: enabling a terminal to send an uplink message to a terminal access-in adaptation layer subsystem; analyzing a terminal protocol by the terminal access-in adaptation layer subsystem, and enabling the uplink message to be changed into an interior message of the service gateway of the internet of the things and sent to a routing control module of a core layer subsystem; enabling the routing control module to query a routing table, and enabling the uplink message to be sent to a corresponding network element according to different order relationship; and enabling a capability gateway module and a real-time data distributing module to supply the received uplink message for an application subsystem to apply. The technical scheme is adopted, the method of the uplink and downlink message routing of the service gateway of the internet of things is elastic and flexible in framework, good in expansibility, capable of achieving multi-granularity order relationship, wide in application, good in application and customization capability and beneficial for the application and development.

The invention discloses a deep learning method for predicting urban traffic jam based on GPS, and relates to the field of intelligent traffic management. Through the deep learning method, a unsupervised large-scale urban network traffic jam prediction method (CRC3D) is innovatively constructed based on GPS real-time data on urban taxies. The method is characterized in that a recursion neural network (RNN), a convolutional neural network (CNN) and a three-dimensional convolutional neural network (C3D) are combined. The recursion neural network and the three-dimensional convolutional neural network can efficiently capture time-varying characteristics of continuous traffic states from different angles. The method is good in effect and high in practicability.

The invention provides an intelligent front-end multimedia information acquisition system used for accurately monitoring power station equipment. The system comprises a plurality of groups of data acquisition devices arranged in a production area I. The data acquisition devices are connected with equipment systems in the production area I. The data acquisition devices are further connected with anintelligent front-end data server arranged in a production area II through a first one-way safety isolation device. The intelligent front-end data server is connected with a second one-way safety isolation device. The second one-way safety isolation device is connected with a cloud server arranged in a management information large area through a private network. The system does not perform the simple lossy compression on acquired real-time data, wherein the sampling frequency and the data compression are dynamically adjusted according to actual needs. Meanwhile, data acquired in real time arestored in a structured database, while data not acquired in real time are stored in a non-structured database. Moreover, the data acquired by the system has the advantages of accuracy, integrity andconsistency. The system is safe in data transmission and high in efficiency. The same coding rule is adopted, and the data application is more efficient, more convenient and more purposeful.

The invention relates to a fuel cell power system monitoring technology. The invention particularly relates to a fuel cell power system platform upper computer monitoring method based on a CAN bus. Anupper computer is connected to a fuel cell controller, a DC/DC and a CAN network of an adjustable load through a CAN bus. A parallel processing multi-thread structure is adopted to receive messagesof the DC/DC, the adjustable load and the fuel cell controller, and display real-time data and curves and stores data of parameters and states of the DC/DC, the adjustable load and a fuel cell engine.The messages are sent to the fuel cell controller and the adjustable load by operating function buttons on an interface of the upper computer and setting control target values of related components,and then manual control, automatic control and forced manual control are conducted on the fuel cell power system platform. According to the method, state monitoring and energy control strategy debugging of the fuel cell power system platform are facilitated, the anti-interference capability is high, and the control performance is stable.

The invention discloses a method for processing power network monitoring device data. The method includes the following steps that firstly, according to requirements, Nk monitoring device model tables and Mk classes of monitoring device real-time data tables are regularly or periodically extracted to Nk temporary monitoring device model tables corresponding to an operation and maintenance management and control system and Mk classes of temporary monitoring device real-time data tables corresponding to the operation and maintenance management and control system; secondly, the Nk temporary monitoring device model tables are automatically assembled into monitoring device operation and maintenance model tables, and the Mk classes of temporary monitoring device real-time data tables are automatically assembled into monitoring device real-time operation and maintenance data tables; thirdly, according to a source professional system access data sequence, the content of all the monitoring device operation and maintenance model tables and the content of all the monitoring device operation and maintenance data tables are sequentially loaded to a power network monitoring device model table and a power network monitoring data table respectively; fourthly, the first step, the second step and the third step are combined into a scene, the scene is maintained through a TASK management tool, and then models and the data are updated. According to the method, the development cycle is shortened, and meanwhile stability of the system is guaranteed.

The invention relates to the electromechanical field and particularly relates to a tractor chassis two-machine loading running-in test stand. The test stand includes a base, a traction input mechanism, an input lifting adjusting mechanism, a test piece mounting mechanism and a loading mechanism which are mounted on the base, and further includes a real-time data detection and processing system anda PLC automatic control system, wherein the mechanisms and the system are connected with a tractor chassis test piece. The tractor chassis two-machine loading running-in test stand is advantaged functions of synchronous running-in, real-time detection and cleaning purification are achieved, the hidden fault risk is eliminated, the integral quality of a tractor is improved, the energy source is saved, production efficiency is improved, and online loading running-in, real-time detection, cleaning purification and PLC automatic control are achieved.

The invention provides a data analysis visualization method, system and device based on big data artificial intelligence, and the method comprises the steps: building an Internet human resource big data talent demand index standard, and constructing a metadatabase according to the standard; collecting data from recruitment advertisements disclosed on the network according to the talent demand index standard established in the step S1, and storing the data by using a big data platform; cleaning and desensitizing the captured webpage data to form structured data with a standard format; constructing a machine learning algorithm to analyze and mine the data; and carrying out interactive real-time data visualization webpage display on the analysis result. The problems that an existing research institution mainly adopts a sampling survey mode for research on human resource requirements and posts of specific administrative divisions, and a data visualization display mode for human resource big data division and interactive real-time access based on the Internet is lacked are solved.

The invention provides an industrial equipment surface defect fault early warning method based on edge calculation. The industrial equipment surface defect fault early warning method based on edge calculation comprises the following steps: (1) constructing a data set: acquiring multi-angle and multi-scale image data of normal image data of equipment to be detected; (2) graying and normalizing the input data; (3) inputting into a cascade self-coding network, and learning a coding and decoding mode of a normal image; (4) completing image training; and (5) unloading the reasoning task to the terminal, and judging the real-time data acquired by the sensor.

Real-time data from cells is recorded during operation of an electrolyzer. Synthetic data is generated based on historical data of the electrolyzer and the cells, the synthetic data comprising synthetic cell voltages and synthetic product output flow, synthetic anolyte pH, feed brine pH, or oxygen in chlorine gas concentration of the electrolyzer. Cell-specific k-factors or U₀ are determined from the historical data. A slow contamination is detected when a difference between the synthetic product output flow, synthetic anolyte pH, feed brine pH, or oxygen in chlorine gas concentration and a real-time product output flow, anolyte pH, feed brine pH, or oxygen in chlorine gas concentration exceeds a first threshold. A fast contamination is detected when cell-specific k-factors or U₀ exceed a second threshold and a trend of a difference between the synthetic cell voltages and real-time cell voltages or a derivative of the difference meets or exceeds a conditional logic rule.