31results about How to "Reflect running status" patented technology

The invention discloses a space-domain sector classification method based on complexity assessment. The space-domain sector classification method includes creating a time-domain sector point-link type structure model to assess the complexity of sectors by using radar track data and simulation flight data in a sliding time window mode; computing critical complexity, current complexity and stability indexes of the sectors; and dynamically clustering and analyzing according to the relevant indexes to classify the space-domain sectors. The space-domain sector classification method has the advantages that space traffic situation can be objectively, truly and integrally reflected by the characteristics of the complexity of the sectors, the sectors are transversely compared and classified by a k-Means dynamic clustering and analyzing method, and guidance and basis for managing the sectors and making relevant decisions are provided.

A bearing fault diagnosis method based on a quantum genetic algorithm optimized support vector machine of the invention comprises the following steps: (1) acquiring a vibration signal of a bearing; (2) calculating dimensionless indexes; (3) optimizing model parameters C and Sigma of a support vector machine based on a cloud model quantum genetic algorithm; (4) training a support vector machine model; (5) performing fault diagnosis using the support vector machine model; and (6) outputting a bearing fault diagnosis result. The bearing fault diagnosis method of the invention has the advantage of high diagnosis accuracy. A new method is provided for solving the problem of bearing fault diagnosis.

The invention provides an online monitoring system and method of a heat transfer coefficient and fouling resistance of a steady heat exchange process and belongs to the field of heat exchange. The online monitoring system comprises a first temperature sensor, a second temperature sensor, a third temperature sensor, a fourth temperature sensor, a flow meter, a central processing unit and a display device; the online monitoring method adopting the system comprises the following steps: 1, at an operation initial state of a heat exchange system or after the heat exchange system is washed, determining a standard heat transfer coefficient K[0] under the condition that no dirt is deposited at a cold side of a heat exchanger; 2, according to production requirements, calculating a detected heat transfer coefficient K[time] in real time after the heat exchanger is operated; 3, calculating the fouling resistance. According to the system and the method, provided by the invention, the measured heat transfer coefficient and fouling resistance can effectively reflect a real heat exchange condition of a reaction system; a lot of data is accumulated to establish a data set of the heat exchanger; a data foundation is laid for long-time stabilization of the heat exchanger; the construction cost of system equipment is low and an application range is wide.

The invention provides a power equipment safety analysis system based on ultraviolet spectrum technology. The system comprises an identification module, an analysis module and a comprehensive determination module which are successively connected, wherein the identification module comprises an image acquisition client, an environmental parameter acquisition end, and a data concentrator connected with the image acquisition client and the environmental parameter acquisition end. The analysis module includes an image intelligent security indication unit and a communication unit connected thereto.The comprehensive determination module includes a security indication unit and a display unit. The data concentrator is connected to the image intelligent security indication unit. The image acquisition client comprises an ultraviolet imaging device, a data acquisition card and a visible light sensor. The environmental parameter acquisition end comprises a temperature and humidity sensor, an eddycurrent sensor, a wind sensor, a voltage sensor and a current sensor. The power equipment safety analysis system, by identifying the ultraviolet spectrum of the power equipment, identifying the equipment identification and considering the influencing factors of the environmental parameters, establishes a comprehensive analysis safety database and realizes the safety analysis of the power equipment.

The invention discloses a method for constructing a digital twin system, which comprises the following steps of determining a plurality of equipment types, and respectively establishing an equipment model corresponding to each type of equipment, namely establishing a component model corresponding to each structural component of the equipment; establishing a measuring point model corresponding to each structural component of each type of equipment; building a monitoring model corresponding to each type of equipment based on the working condition attribute and the environment attribute of the equipment; establishing a fault model corresponding to each equipment model, wherein the fault model comprises one or more fault indexes corresponding to the equipment model; and based on the equipment model, the corresponding measuring point model, the monitoring model and the fault model, constructing a digital twin system corresponding to the equipment so as to collect state data of each component model in the equipment model according to the corresponding measuring point model and the monitoring model, and determining fault information of the equipment according to the fault model. The invention also discloses corresponding computing equipment.

The invention provides an on-line evaluation method and system for an operation state of an electric energy metering system in an electric power spot market, and the method comprises the steps: constructing an electric energy metering system operation state evaluation model in the electric power spot market, wherein the model comprises a data level index and a system level index; on the basis of the evaluation model, online obtaining index data of the electric energy metering system, performing standardization processing on the obtained quantitative index data, calculating a quantitative index membership degree, processing a qualitative index, and calculating a qualitative index membership degree; constructing a fuzzy relation matrix based on the quantitative index membership degree and the qualitative index membership degree; constructing a fuzzy judgment matrix by evaluating data level indexes and system level indexes in the evaluation model, and calculating single-layer index weights and combined index weights in sequence; and performing fuzzy comprehensive evaluation on the operation state of the electric energy metering system based on the combined index weight and the fuzzy relation matrix, and outputting an evaluation result online in real time. According to the method, fuzziness of evaluation thinking of people can be reflected, and subjective blindness when a judgment matrix is constructed is reduced.

The invention relates to a health state evaluation method, system and device for a numerical control machine tool and a medium, and the method comprises the steps: training model parameters of a health state analysis model based on each pre-obtained training sample data set, and calculating a corresponding likelihood probability, forming a preset model parameter set representing different preset working condition states of the machine tool by each group of trained model parameters, and forming a preset likelihood probability set by each likelihood probability; and based on the obtained preset model parameter set and the preset likelihood probability set, evaluating the health state of the current numerical control machine tool. According to the method, the historical monitoring data of the numerical control machine tool is utilized to construct the multi-dimensional data vector, the health state of the numerical control machine tool is evaluated by calculating the health degree, the number of machine tool sensors does not need to be increased, the model has wider practicability, the calculation complexity is reduced, and accurate, rapid and real-time health degree evaluation is achieved; the method can be widely applied to the numerical control machine tool health state monitoring field.

The invention discloses a fire drill method and system based on digital twinning, and the method employs a digital twinning technology and a BIM modeling technology to construct a digital twinning model of a building, and achieves the real-time mapping from a real world to a virtual world. Therefore, the operation states of fire-fighting facilities, security and protection facilities and public facilities in a building and the motion state of personnel can be reflected visually and truly in real time, and a real world scene can be simulated more truly. When the digital twin model is used for fire drill, only a small amount of manpower and material resources need to be invested during scheme design and drill preparation, and the fire drill cost is greatly reduced; the digital twinborn model is repeatedly used for fire-fighting drilling, so that the fire-fighting drilling cost is further reduced, a social unit can better master four fire-fighting capabilities, and fire accidents are reduced and even avoided.

The invention discloses a substation equipment state evaluation method based on multi-physical-quantity fusion analysis. The substation equipment state evaluation method comprises the following steps:collecting a plurality of physical quantity signals representing the state of substation equipment by adopting a sensor; based on the historical data of each physical quantity signal, calculating theweight of each physical quantity signal when judging the state of the power transformation equipment by adopting a fuzzy analytic hierarchy process and an anti-entropy weight method to obtain a firstweight and a second weight; fusing the first weight and the second weight to obtain a fusion weight of each physical quantity signal; obtaining the relative degradation degree of each physical quantity signal based on each physical quantity signal to be detected at present and the respective corresponding standard value; converting each relative degradation degree into a fuzzy membership degree of each state of the power transformation equipment corresponding to each physical quantity signal to be detected at present; and based on the fuzzy membership degree and the fusion weight, obtaining the membership degree of the power transformation equipment to each state, and evaluating the state of the power transformation equipment according to the maximum membership degree principle.

The invention discloses a wind power plant group health state evaluation method. The method comprises the following steps: 1) establishing a state evaluation model of a flagship wind turbine generator; 2) obtaining a state evaluation model of the common wind turbine generator; 3) acquiring data of the flagship wind turbine generator and the common wind turbine generator, and respectively inputting the data into the state evaluation models of the flagship wind turbine generator and the common wind turbine generator to obtain operation states of the flagship wind turbine generator and the common wind turbine generator; 4) calculating to obtain a group operation state evaluation result according to the operation states of the flagship wind turbine generator and the common wind turbine generator in one group; and 5) according to all group state evaluation results, calculating to obtain an operation state evaluation result of the to-be-evaluated wind power plant. According to the method, on the basis of flagship wind turbine generators, the health state models of all the wind turbine generators are established in groups, the operation states of the single wind turbine generator and the groups can be monitored at the same time, the operation state of the whole wind power plant is better reflected, and operation, maintenance and management of the wind power plant are better facilitated.

The invention provides an on-line monitoring system for the electrical performance of multi-channel photovoltaic modules, and the on-line monitoring system at least comprises three sub-loop systems: apower supply loop system which is provided through an internal DC wide-voltage power supply system and enables the input of a plurality of photovoltaic modules to meet the energy supply of the operation of a monitoring loop system, a monitoring loop system which at least comprises a monitoring element used for acquiring electrical data of a plurality of photovoltaic modules in real time, and an internal control system used for analyzing the acquired electrical data, and ae communication loop system which comprises a monitoring loop system internal communication loop and an external communication loop for monitoring internal and external communication of the loop system. The system monitors the voltage, the current, the power and the temperature of the photovoltaic module in real time, andprovides reliable data support for operation and maintenance of a power station; the overall power consumption is low, power generation of a photovoltaic power station is not affected, and the real-time state of the assembly is truly reflected; and long-time digital on-line monitoring is realized, effectiveness and correctness of all parameters are ensured, and detection errors are small.

The invention discloses a five-degree-of-freedom bearingless permanent magnet synchronous generator neural network prediction decoupling controller, which is characterized in that the input end of a fuzzy neural network system is respectively connected with two neural network dynamic prediction modules in series, and the output end of the fuzzy neural network system is connected with a composite controlled object comprising a five-degree-of-freedom bearingless permanent magnet synchronous generator; the first neural network dynamic prediction module outputs a composite control quantity ja at the t + 1 moment, and the second neural network dynamic prediction module outputs a composite control quantity jb and a power generation voltage control quantity at the t + 1 moment; the output of the fuzzy neural network system is a power generation winding reference voltage component, a rotor radial suspension force winding reference voltage component and a rotor axial displacement control voltage at the t + 1 moment; according to the method, the advantages of low sample requirement of fuzzy logic control and good dynamic performance of the neural network on system learning ability and prediction control are combined, various static and dynamic performance such as good rotor radial displacement and power generation voltage control can be obtained, and the control efficiency is improved.

The present invention discloses a cable fault detection and early warning system on the basis of optical fiber communication. The cable fault detection and early warning system on the basis of optical fiber communication comprises a cable fault signal acquisition circuit, an MSP430 single-chip microcomputer module, an ARM microcontroller module, a data storage circuit module and a power supply battery which are connected in order; the cable fault signal acquisition circuit includes an I/V conversion circuit, an amplification circuit, a filtering circuit and an A/D conversion circuit; the input end of the I/V conversion circuit is connected with an A-phase current sensor, a B-phase current sensor and C-phase current sensor; the input end of the ARM microcontroller module is connected with a clock circuit module and a button operation circuit module; the output end of the ARM microcontroller module is connected with a liquid crystal display screen; the ARM microcontroller module is connected with the server to perform both-way communication through a fiber communication system; and the server is connected with an upper computer monitoring computer. The data acquisition efficiency is high, data transmission speed is fast, the implementation cost is low and the work reliability is high.

The invention discloses a data pretreatment method for a steam water leakage diagnosis of a heat recovery boiler. The data pretreatment method comprises the following steps that: (1) the operation data are collected and recorded; (2) the abnormal data among the operating data are removed; (3) the operating data in steady operating condition are screened; (4) steady-state segments are conducted with the mean value polymerization on the basis of the abnormal data removal and the steady-state screening. The data pretreatment method for the steam water leakage diagnosis of the heat recovery boiler is put forward according to the characteristics of the operation data of the gas-steam combined cycle unit heat recovery boiler. The data pretreatment method has the advantages of high reliability, improved operating data quality and improved fault diagnosis accuracy rate.

The invention discloses a multi-feature fusion based axial flow fixed-blade type generator set thrust bearing protection method. The multi-feature fusion based axial flow fixed-blade type generator set thrust bearing protection method includes the steps that dynamic axial displacement amount, thrust block overturning angle, thrust pad inclination and thrust runner wave degree of a water-turbine generator set and non-perpendicular degree of a main shaft and a thrust runner are measured and calculated; and if anyone of the conditions that the dynamic axial displacement amount is greater than a preset dynamic axial displacement amount threshold value, the thrust block overturning angle is greater than a preset thrust block overturning angle threshold value, the thrust pad inclination is greater than a preset thrust pad inclination threshold value, the thrust runner wave degree is greater than the preset thrust runner wave degree threshold value, and the non-perpendicularity degree of themain shaft and the thrust runner is greater than a preset non-perpendicularity threshold value of the main shaft and the thrust runner occurs, an alarm signal is output. The operating state and healthstatus of a thrust bearing of a unit are more comprehensively reflected, so that potential defects and problems existing in the thrust bearing operation can be accurately evaluated and predicted, andthe stability and safety of the thrust bearing operation can be ensured.