265 results about "Random model" patented technology

A plurality of stochastic models is built that predict the probabilities of state transitions for components in a complex system. The models are trained using output observations from the system at runtime. The overall state and health of the system can be determined at runtime by analyzing the distribution of current component states among the possible states. Subsequent to a low level component failure, the state transition probability stochastic model for the failed component can be analyzed by uncovering the previous states at N time intervals prior to the failure. The resulting state transition path for the component can be analyzed for the causes of the failure. Additionally, component failures resulting from the failure, or worsening state transition, in other components can be diagnosed by uncovering the previous states at the N times prior to the failure for multiple components in the system and then analyzing the state transition paths for correlations to the failed component. Additionally, transitions to worsening states can be predicted using an action matrix. The action matrix is created beforehand using state information and transition probabilities derived from a component's stochastic model. The action matrix is populated probabilities of state transitions at a current state for given actions. At runtime, when an action is requested of a component, the probability of the component transitioning to a worsening state by performing the action can be assessed from the action matrix by using the current state of the component (available from the stochastic model).

The invention discloses a method for establishing a wind farm random model based on factors such as wind farm input wind speed and wind direction chance fluctuation, wake flow effect between wind generating sets, an electric network in a wind farm and the like. The technical scheme comprises the following steps of: grouping the wind generating sets based on the wind farm wind speed and wind direction chance fluctuation; equating wind farm power according to a constant power exchange principle between the wind farm and a power grid before and after the equating; equating model parameters such as a generator of a wind generating set, a shaft system, a control system and the like according to a principle that the dynamic characteristic of the wind farm is constant before and after the equating; and keeping the detailed degree of an equivalent wind generating set model the same as that of a single wind generating set by rationally selecting an equivalent wind generating set reference value. When wind farm interconnecting analysis is performed by the model established by the method, the defects brought on the basis of the absence of the factors such as the wind farm input wind speed and wind direction chance fluctuation, the wake flow effect between the wind generating sets, the electric network and the like are overcome, and more accurate wind power data is provided for power grid dispatching and reasonable production plan arrangement.

The present invention relates to a random energy management method of double energy source mixed power automobile, belonging to the field of dynamic energy distribution method of mixed power automobile. Said method includes the following steps: firstly, inducing statistic law of actual power from lots of vehicle road running records, obtaining condition transfer probability matrix of power required by driver and obtaining single-step cost of conversion between arbitrary conditions, and creating Markov random model of power required by driver, then utilizing said Markov random model to make calculation and obtain optimum control quantity of energy output.

The invention belongs to the field of social network modeling and analysis, and particularly relates to a method for predicting the maximum information spreading range on the basis of a random model, and by means of the method, dynamic characteristics of a social network are explored. According to the method, a set of functions capable of describing the network information spreading dynamic characteristics are constructed, a dynamic information spreading model is built according to historical data of social network information spreading, a random model detector is used for predicting the possible maximum information spreading range through a verification and emulation technology, and the node set capable of maximizing the spreading range is found out, wherein information is spread through different node sets. Compared with a traditional spreading range maximization modeling method, the dynamic characteristics of the network can be modeled, so that the initial node set which is predicted out is higher in quality, and the success rate of a network marketing strategy is increased.

The invention belongs to the field of control technologies, and provides an energy control method and device for a hybrid electric vehicle. The method includes the steps that required power Preq(k+j) of a driver in the [k, k+p] time period is predicted, j equals 0, 1, ..., p, and k represents the current sampling time; required torque Treq(k+j) and the vehicle speed v(k+j) of the hybrid electric vehicle in the [k, k+p] time period are obtained; through nerve cell dynamic planning, the optimal control vector [u(k), ..., (k+p-1)] is calculated; and a control variable sequence u(k) of the current sampling time is output. According to the method and device, energy management control of the hybrid electric vehicle is achieved on the basis of random model prediction control and a nerve cell dynamic planning algorithm, and on the premise of guaranteeing the vehicle dynamic quality, the fuel economy of the vehicle can be improved; the calculated quantity is small, the real-time performance is good, and the fuel economy of the hybrid electric vehicle can be further improved.

The underlying invention generally relates to the field of Estimation of Distribution Algorithm, especially to optimization problems, including single-objective optimization and Multi-Objective Optimization. The proposed method for optimization comprises six steps. In a first step it provides an initial population or a data set with a plurality of members respectively represented by parameter sets. Then one or a plurality of fitness functions are applied to evaluate the quality of the members of the population. In a third step offspring of the population is generated by means of a stochastic model using information from all members of the population. One or a plurality of fitness functions are applied to evaluate the quality of the offspring with respect to the underlying problem of the optimization. In a fifth step offspring is selected. Lastly the method goes back to the third step until the quality reaches a threshold value.

The invention provides an electric vehicle charging power prediction method in consideration of delay and V2G charging modes, which is applied to research of an electric vehicle charging and discharging random model and enables charging and discharging power of an electric vehicle to be obtained via simulation. The method comprises the steps of firstly analyzing the space-time uncertainty of electric vehicles and building a dynamic probability model of electric vehicle charging power; secondly, selecting a proper charging method and modeling characteristics of electric vehicle charging power for a common charging mode, a delay charging mode and a V2G charging mode; finally, performing simulation tests on a single electric vehicle under the three charging modes and calculating the expectation and standard differences of electric vehicle loads in all periods. The method can obtain electric vehicle charging and discharging power expectation values of the three charging methods under the requirement response background and has the advantages of simple principle and convenient realization; the probability distribution characteristics enable dispatching personnel to know electric vehicle charging and discharging cluster loads more clearly.

The invention discloses an adaptive prediction method of residual service life of service equipment modeled based on degradation data. Degradation modeling and residual service life prediction of the service equipment are realized by a Bayesian method and an EM algorithm, and the method comprises the following steps of (1) randomly degrading and modeling; (2) updating random model parameters based on the Bayesian method; (3) predicting the residual service life; and (4) establishing model parameters based on the EM algorithm. An adaptive parameter updating mechanism based on the EM algorithm is introduced into a method for establishing a random index degradation model for predicting residual service life of service equipment, so that all parameters of the random index degradation model are continuously updated when the real-time data of the service equipment are accumulated, and thus the actual operation condition of the equipment can be reflected by predicted results, and the purpose of minimizing uncertainty prediction is achieved. According to the adaptive prediction method, historical data of multiple similar equipment are not required to initialize the degradation model, namely model parameters and residual service life distribution can be adaptively updated.

A load flow calculation method of a distributed power supply connection power grid comprises the steps that S1. initial data of an electric power system are read; S2. sampling frequency N and the dimensions s of input random variables are determined; S3. an s * N order sampling matrix is generated; S4. sampling frequency is initialized, namely n is equal to 1; S5. whether n is larger than the sampling frequency N is judged, and yes, the probability statistics results of the variables are directly output; otherwise, S6 is carried out; S6. a wind power and photovoltaic power generation output model is determined, and a load random model is determined; S7. a load flow calculation model is determined; S8. an optimized economic model is determined; S9. load flow calculation is carried out; S10. data such as voltage, branch power and power generation cost of a <n>th node group are determined; and S11. a next round of load flow calculation is carried out, t is equal to t + 1, and S5 is carried out. The probability distribution of the output random variables can be estimated well, the uncertainty problem in an electricity market can be well solved, debugging manpower and material resources are saved, and production cost is lowered.

The underlying invention generally relates to the field of Estimation of Distribution Algorithm, especially to optimization problems, including single-objective optimization and Multi-Objective Optimization.The proposed method for optimization comprises six steps. In a first step it provides an initial population or a data set with a plurality of members respectively represented by parameter sets. Then one or a plurality of fitness functions are applied to evaluate the quality of the members of the population. In a third step offspring of the population is generated by means of a stochastic model using information from all members of the population. One or a plurality of fitness functions are applied to evaluate the quality of the offspring with respect to the underlying problem of the optimization. In a fifth step offspring is selected. Lastly the method goes back to the third step until the quality reaches a threshold value.

The invention provides a BDS_GPS_GLONASS precise point positioning integration method. The BDS_GPS_GLONASS precise point positioning integration method comprises the steps of first of all, reading original data in three systems (GPS, GLONASS and BDS) and making cycle slip detection and gross error removal by M-W combined detection method; secondly, unifying time and space datum of three systems; next, building three-system observation equation, correcting error in the method of iono-free combination of dual-band pseudo range and carrier phase measurement and setting up a random model; lastly, solving ambiguity and coordinate by extending Kalman filtering. The BDS_GPS_GLONASS precise point positioning integration method of the embodiment can greatly improve geometric strength of satellite, remarkably enhance independence and integrity of positioning system, effectively reduce time of positioning and convergence, and enhance positioning precision in small number of one-system satellites or poorer satellite constellation distribution.

The invention discloses a method for improving a convergence speed of combined precision single-point positioning (PPP) of a GPS (Global Position System) and a GLONASS. A plurality of independent 'time frequency bias' parameters are introduced into a function model to make up the defect in the prior art that GLONASS code inter-frequency bias is ignored in a modeling process; the convergence speed of the combined PPP of the GPS and the GLONASS is remarkably improved under the condition of not losing positioning precision. With the adoption of the method disclosed by the invention, a random model of GLONASS and GPS equal weight can be used for obtaining a code pseudo-range observation value when the PPP is combined, so that the defect that the weight ratio is determined by experiences in the prior art is avoided.

Disclosed is an uncertainty optimization operating method for an alternating current and direct current microgrid comprising high-density intermittent energy source. According to the characteristics of the microgrid and on the basis of taking output uncertainty of the intermittent energy source into consideration, a wind energy and solar energy output fuzzy random model, a diesel generator fuel cost module and an energy storage cost module are established; by combination with the grid structure characteristics of the microgrid and the problem of high output fluctuation caused by access of a large amount of intermittent energy source, a fuzzy random optimization model capable of minimizing the comprehensive operating cost of the alternating current and direct current hybrid microgrid, and a real-time imbalance power adjusting model capable of minimizing adjusting expenditure are established; and the fuzzy random optimization model capable of minimizing the comprehensive operating cost of the alternating current and direct current hybrid microgrid is solved by a fuzzy random uncertainty alternating direction multiplier optimization algorithm so as to obtain an operating scheme of the alternating current and direct current hybrid microgrid. By virtue of the uncertainty optimization operating method, the accuracy of the dispatching plan of the microgrid comprising the high-density intermittent energy source can be effectively improved, imbalance power can be lowered, and imbalance power adjusting expenditure caused by day-head dispatching deviation can be reduced.

The invention discloses a GNSS base station crustal movement velocity estimation method in consideration of nonlinear change. In consideration of the influence on the base station velocity estimationby the GNSS base station nonlinear change, a random model and like, the latest resolving strategy is adopted to acquire a GNSS base station coordinate time sequence under an ITRF2014 framework, a timesequence model for performing separation on the nonlinear change of the base station coordinate time sequence is established by combining a real physical correction model, abnormal location gross error and step detection and like methods; and a GNSS base station coordinate time sequence background noise model determination technology based on the singular spectrum analysis method is adopted, andan improved Bayes information minimum criterion noise model estimation method is proposed base on above conditions, a GNSS base station crustal movement velocity field estimation method is provided, thereby providing accurate and reliable velocity field data basis for the crustal movement velocity field estimation.

The invention discloses a code pseudo-range / Doppler combined speed measurement method based on robust variance assembly estimation, which comprises the following steps: firstly, respectively establishing a speed measurement function model and a random model by adopting observation information such as code pseudo-range, Doppler, carrier-to-noise ratio, satellite altitude angle and the like, then adopting a least square robust estimation method to obtain a robust random model capable of inhibiting the influence of gross error, and finally combining a speed measurement function model of code pseudo-range and Doppler with the robust random model to form a combined speed measurement model. According to the method, the limitation that the conventional single-frequency satellite receiver only uses single information to measure the speed can be broken through, the influence of observation gross error can be inhibited, and the robustness, the unbiased property and the stability of the combinedspeed measurement method can be obviously improved.

The invention provides a method for establishing a wind-driven generator power output random model capable of reflecting wind speed variation characteristics. The method comprises the following steps of establishing a wind speed model in consideration of the wind speed variation characteristics; establishing a wind turbine generator output power model; establishing a wind power plant output power model. According to the method, the wind-driven generator power output random model is established based on a sequential hour determinacy model, a semi-invariant method and a Cornish-Fisher stage number, and can reflect the regularity of power output of a wind-driven generator along with seasonal variations and day and night alternation as well as the fluctuations and the indeterminacy of the power output of the wind-driven generator under the influence of factors such as weather, so that the power output of the wind-driven generator is more consistent with an actual condition.

The invention discloses an independent micro-grid energy storage margin detection method. Wind power, photovoltaic output and load random models are built on the basis of a latin hypercube sampling scene generation technology and a probability distance scene reduction technology; a scene generation and reduction technologies-based micro-grid energy storage margin detection model combining with a storage battery charge-discharge optimization technology and a secant method is built by constructing an expected value of island power shortage and the expected value of island power surplus as reliability evaluation indexes; on the basis of considering a micro-source and load stochastic characteristics, due to introduction of energy storage equipment and the ratio of newly increased load carrying capacity to an original load of a system, the micro-grid energy storage margin is detected or calculated to obtain the required minimum capacity of the energy storage equipment when a micro-grid reaches one energy storage margin under the condition of maintaining the reliability of the system unchanged.

The present invention relates to the technical field of transformer paper oil insulation aging test, especially to a transformer paper oil insulation multi-factor combined aging test device and a lifeprediction method thereof. The device comprises a test box body, a vibration transmission device, test sample clamping devices and an oil-way cycle device. The test box body is arranged on a bearingplatform, the test box body is internally provided with the plurality of test sample clamping devices arranged in parallel, the test sample clamping devices are connected with the vibration transmission device, a vacuum extraction opening, a cycle oil inlet and a cycle oil outlet are processed on the test box body, the cycle oil inlet and the cycle oil outlet on the test box body are connected with an oil-way cycle device through an oil-way cycle pipeline, the vacuum extraction opening on the test box body is provided with a vacuum pump through an exhaustion pipeline, and the extraction pipeline is provided with an exhaust valve. The transformer paper oil insulation multi-factor combined aging test device and the life prediction method thereof can achieve online detection of performance degradation amount of test samples, and employ a random model to combine a traditional life calculation model to predict the life of the transformer.

The invention relates to random model updating method based on an interval response surface model. The method is characterized by including the steps of firstly, building a second-order polynomial response surface model without cross terms according to experiment design and regression analysis; secondly, using a square completing method to convert a polynomial response surface expression into perfect square; thirdly, substituting interval parameters into the response surface expression to allow the definite response surface model to be changed into the interval response surface model; fourthly, performing interval calculation on the interval response surface model to obtain predicted structural response intervals, and combining the predicted structural response intervals with actual response intervals to build a target function; fifthly, building a optimization inversion problem to identify interval distribution of parameters. By the method, the expansion problem of interval calculation is avoided, fast calculation of structural response intervals is considered, finite element analyzing calculation and sensitivity matrix building during (interval) random model updating are avoided, a large amount of calculation time and cost is saved, and ill-conditioned optimization is avoided as much as possible.

The invention provides a modeling method of a photovoltaic output random model reflecting solar radiation intensity variation characteristics. The method includes: determining the solar radiation intensity variation characteristics; building a solar radiation intensity model considering the solar radiation intensity variation characteristics; building a photovoltaic array output power model. The photovoltaic model built by the method can reflect the regularity of the photovoltaic output changing along with season change and day-night alternation and reflect fluctuation and uncertainty, caused by influence factors such as weather, of the photovoltaic output, and the photovoltaic output is allowed to conform to actual conditions.

The invention relates to a strain control method for measuring residual strength and residual service life of a composite material. The strain control method comprises three steps of (1) a strain control residual strength model; (2) a random model of the strain control residual strength; and (3) model parameter estimation. The strain control method is simple and practical, is convenient for operations and has high calculation precision. The strain control method has relatively good practical value and wide application prospects in the field of measurement technology.

There are disclosed techniques for performing data fusion. In one embodiment, the technique comprises: (a) receiving an input; (b) processing the input in accordance with parameters of a stochastic model to derive a state of a stochastic process of the stochastic model, the parameters of the stochastic model previously established during a training period; (c) obtaining a predicted next state of the stochastic process; (d) receiving a next input and processing the next input in accordance with the parameters of the stochastic model to derive a next state of the stochastic process; (e) comparing either (i) the next state and the predicted next state, or (ii) the next input and a predicted next input corresponding to the predicted next state; and (f) if there is a discrepancy between that compared in (e), then using the next input to modify the parameters of the stochastic model.

In a method for estimating a deviation between a free-running transmitter clock and a reference clock, at a receiver stationary with respect to a transmitter, a transmitter signal generated by the transmitter on the basis of the transmitter clock is received. On the basis of the reference clock, a time of arrival of the transmitter signal and a beat phase of the transmitter signals carrier is determined. On the basis of a clock error model, the time of arrival and the beat phase, the deviation between the transmitter clock and the reference clock is estimated. The clock error model is derived by fitting a correlation function of a stochastic model to a measured auto correlation function of the transmitter clock. Deviations for a plurality of transmitters may be estimated and the transmitters may be virtually synchronized based on the estimations.

The invention discloses a method and device for detecting an algorithm generation domain name, and the method comprises the steps: generating an algorithm through a probability model; building a random model and a normal model through an algorithm generation domain name set and a normal domain name set, and detecting the algorithm generation domain name through the above models, thereby achievingthe quick detection of the algorithm generation domain name, and effectively solving a problem that the quick detection of the algorithm generation domain name cannot be achieved in the prior art.

The invention provides a method for random model correction based on secondary response surface inversion. According to the method, against the problem of uncertainty in engineering system parameter identification or model correction, through single-point successive inversion iteration, errors caused by uncertainty analysis and sensitivity matrix solving in a conventional random model correction method are avoided effectively. It shows through example verification and method contrast that according to the method, uncertainty parameter on a structure having obvious uncertainty can be performed effectively, and the identification efficiency and accuracy are very high.

Method and apparatus for the power control system outer loop of a mobile communications systems in a cellular infrastructure that allows fulfilling a given Quality of Service (QoS) with the minimum power level necessary. This outer loop is in charge of setting the desired signal to interference target ratio SIRtgt that fulfils the required quality of service in a radioelectric environment characterised by stochastic models. The proposed method is based on the application of the Newton-Raphson iteration method on numerical approximations of the various outage probability (QoS) probability density functions in various propagation environments, so that having established this probability and dynamically estimated the second order moments characteristic of each statistic considered, the margin over the SIR median is obtained and thereby the SIRtgt corresponding to the aforementioned outage probability.

The invention provides a service robot movement system fault diagnosis method and device based on time sequence characteristics. The service robot movement system fault diagnosis method based on timesequence characteristics comprises the following steps that: adopting a sliding window way for original data to generate a time sequence training sample; adopting a plurality of random forest models and a plurality of gradient boosting tree models to carry out weighting fusion to form a random module, and utilizing a random model to carry out fault characteristic selection in the training sample;and inputting selected fault characteristic data into a GRU (Gated Recurrent Unit) neural network, extracting the time sequence characteristics of the fault, inputting the last moment data of the sliding window into a BP (Back Propagation) neural network, extracting the fault characteristics of a current moment, and carrying out fault classification after the time sequence characteristics and thefault characteristics of the current moment are combined.

The invention discloses a modelling method for a bubbling fluidized bed based on random motion of bubbles and particles. The method comprises the steps that (1) statistics for particle motion rules of the bubbling fluidized bed computed by CFD-DEM is carried out, and a Markov chain random model of a particle phase is established; (2) image identification is carried out to an instant distribution map of the bubbling fluidized bed particles computed by the CFD-DEM, statistics is carried out to rules for generation, motion and growth of the bubbles, and a bubble random development model is established; and (3) a baseball cap model of bubble shapes is used to couple the particle-phase Markov course with the bubble random model, and a random model of the particle motion of the bubbling fluidized bed is established. The method disclosed by the invention has the advantages that the defect that a single particle-phase Markov course cannot reflect bubble information and influences of the information on the particle motion can be solved; and computation loads are greatly reduced, and a computation speed is increased on the premise of guarantee of accuracy of the bubbling fluidized bed particle motion.

The invention discloses a combination precise single-point positioning method and system based on inter-constellation difference. The combination precise single-point positioning method comprise the steps that original observation values and auxiliary parameters, observed on an observation station, of the different constellations are obtained; the original observation values are preprocessed; thepreprocessed original observation values are subjected to non-ionizing combination; inter-constellation difference is conducted on non-ionizing layer combination observation of a single constellation;an error observation equation is determined based on an observation model for inter-constellation difference; the preliminary position of the observation station and observation value noise are obtained; the elevating angles of all satellites are determined based on satellite orbit parameters and the preliminary position of the observation station; a random model of each set of inter-system difference observation values is determined on the basis of the elevating angles of all the satellites and the observation value noise; a weight matrix is determined; and position parameters, inter-constellation difference ambiguity parameters and troposphere parameters are estimated through a least square method on the basis of the error observation equation and the weight matrix, and thus single-point positioning is achieved. According to the combination precise single-point positioning method, high-precision observation station positioning information and inter-system time difference parameter information can be obtained.