32 results about "Density distribution function" patented technology

The invention relates to the technical field of formation data acquisition, in particular to a logging formation information intelligent analysis system which comprises a response model construction module, a dynamic weight generation module, a composite evidence distribution module and a multi-source information fusion module. According to the method, core calibration data are obtained, a probability density distribution function of basic lithologic logging response is constructed through a kernel density estimation algorithm, dependence on a fixed plate and a static cut-off value is eliminated, dynamic probabilistic description of stratum response characteristics is achieved, and then the initial reliability and sensitivity weight of response numerical values are calculated based on distribution; the method is used for adjusting evidence distribution, then calling an evidence theory combination rule to fuse adjusted multi-source information, determining the stratum state according to the highest cumulative reliability, effectively solving the problem of misjudgment caused by response overlapping, and remarkably improving the accuracy and objectivity of stratum lithology and fluid property identification.

The invention discloses a double-Beta distribution vector pulse width modulation method and system introducing an overlapping factor, and the method comprises the steps: constructing double-Beta symmetrical distribution introducing the overlapping factor, and constructing a probability density distribution function of a double-Beta symmetrical distribution random carrier period strategy; constructing a power spectral density function of the expanded random carrier switching period signal based on the random carrier signal; and carrying out power spectral density analysis on the power spectral density function of the expanded random carrier switching period signal based on the probability density distribution function of the double-Beta symmetric distribution random carrier period strategy to realize random carrier switching period permanent magnet synchronous motor control. According to the invention, local energy accumulation can be weakened, smooth spectrum transition can be realized, and adjustable control of a sideband voltage harmonic peak value can be realized. The overlapping factor-introduced double-Beta distribution vector pulse width modulation method and system can be widely applied to the technical field of pulse width modulation driving.

The application provides a turbine blade thermal barrier coating force-thermal coupling reliability evaluation method, which comprises the following steps: measuring and constructing a density distribution function of a thermal barrier coating microstructure and thermodynamic parameters, completing macroscopic complex turbine blade thermal barrier coating temperature field and strain field simulation, constructing a Monte Carlo sample space of random parameters at different positions of the blade, establishing a micro-scale force-thermal coupling finite element model and obtaining a life data set, building and training a deep learning agent model, and completing turbine blade thermal barrier coating reliability evaluation and sensitivity analysis. The reliability evaluation method provided by the application comprehensively considers the influence of the thermal barrier coating microstructure factor and the force-thermal coupling failure mechanism, solves the problems of high dimension, high nonlinear calculation cost and the like in the reliability evaluation of the complex blade thermal barrier coating, improves the accuracy and efficiency of the reliability evaluation, and provides an evaluation means for the safe application and optimized design of the thermal barrier coating.

The disclosure provides a gamma adjustment method, a computer readable storage medium and a computer program product. The gamma adjustment method comprises the following steps: obtaining a model of a to-be-tested module, determining a target model corresponding to the model and an error probability density distribution function according to the model, the target model being used to represent a mapping relationship between a gamma register value and a target influence factor, and the error probability density distribution function being used to represent an error distribution between a predicted gamma register value output by the target model and an actual gamma register value; obtaining a target influence factor value when the to-be-tested module displays a target gray scale picture, and inputting the target influence factor value into the target model to obtain a first gamma register value; detecting whether a display picture of the to-be-tested module meets a requirement when the to-be-tested module performs gamma adjustment by using the first gamma register value; and when the display picture does not meet the requirement, adjusting the first gamma register value by using the error probability density distribution function until the display picture meets the requirement.

The present application relates to the technical field of biomechanical detection and automobile safety evaluation, and discloses a device and method for testing the abdomen safety of a car crash dummy. The testing device comprises a human feature modeling module, a parameterized model of the abdomen of a target population is established based on statistical analysis, a key size design domain covering the target population percentile is determined, and an anthropometry database of the target population is constructed; an abdomen mechanical property simulation module, a human tissue relative density distribution function is obtained based on a super-elastic constitutive model, the density distribution function is taken as an input parameter for manufacturing a gradient honeycomb structure, and the abdomen mechanical properties of the human body are simulated through the relative density gradient distribution in the thickness direction; and a multi-modal sensor integration module, a distributed intelligent sensing network architecture is adopted, and the multi-modal sensor integration module is composed of a perception layer, a transmission layer and a processing layer. Through integration of pressure, deformation, acceleration and tissue stress sensors, and in combination with a real-time feedback algorithm, collision parameters are dynamically optimized, and the testing precision and biological fidelity are improved.

This invention discloses a design and manufacturing method for a uniform cross-section explosive load simulator based on gradient elastic metamaterials, relating to the field of impact loading. Addressing the problems of high cost per use and the susceptibility of variable cross-section elastic rods to local buckling instability under high-speed impact leading to waveform distortion in existing simulators, this invention, while maintaining the macroscopic uniform cross-section shape of the simulator, derives the ideal wave impedance gradient required to achieve the target explosive load based on the one-dimensional elastic wave propagation inverse design theory; establishes the mapping relationship between the relative density of a three-period minimal surface (TPMS) metamaterial unit cell and its equivalent mechanical parameters, and inversely solves the spatial relative density distribution function; extracts the waveform through dynamic impact finite element simulation, and introduces an impedance gradient correction factor to perform nonlinear iterative correction of the density function, obtaining a final three-dimensional metamaterial geometric model that accurately matches the waveform, and uses a flexible polymer material for additive manufacturing. This invention achieves the purely elastic, non-destructive, and reusable simulator, perfectly adapting to the stable firing of light gas cannons, and exhibiting excellent anti-instability capability and waveform fidelity under strong dynamic loads.

The application discloses a sparse Bayesian learning vector acoustic direction finding method based on orthogonal acoustic intensity flow, and belongs to the technical field of array signal processing. First, an orthogonal acoustic intensity flow data model is constructed, an acoustic intensity flow is obtained by cross spectrum of sound pressure and vibration velocity channels, and acoustic energy flow density is obtained by acoustic pressure autospectrum. Then, a joint probability density distribution function is established by means of a sparse Bayesian learning model. Finally, according to maximum log-Bayes evidence estimation of signal power, spectral peak search is performed on the signal power estimation value, and the direction estimation of the target is realized. The method uses acoustic intensity and acoustic intensity flow as input, has the effect of suppressing isotropic noise and reduces the calculation amount. When the hyperparameters are updated for the first time, a deconvolution algorithm is introduced to avoid the judgment error of the number of signal sources and the direction of arrival caused by the proximity of two targets. Moreover, the method has good direction estimation accuracy and direction resolution capability, and can maintain good performance under the condition of low signal-to-noise ratio.

The application provides a simulation prediction method and system for slow positron source intensity in a research reactor, comprising the following steps: S1, reading arrangement information of components in a reactor core of the research reactor; S2, constructing a Monte Carlo model of the reactor core containing a slow positron source device, and obtaining neutron multi-group angular flux and photon multi-group angular flux information near the slow positron source generating device; S3, constructing a slow positron Monte Carlo model; S4, performing energy-angle double sampling to obtain an incident neutron energy probability density distribution function f n,ig,ia , an angle probability density distribution function f n,ia , an incident photon flux energy probability density distribution function f g,ig,ia , an angle probability density distribution function f g,ia , and a neutron-photon weight factor; S5, performing function sampling of particle types, energy and angle; and S6, obtaining slow positron distribution information generated in the slow positron generating device. The application accurately considers the whole life cycle of slow positron generation and transportation through multi-stage coupling, and realizes accurate prediction of slow positron source intensity.

The invention provides an X-ray free electron laser pulse transmission simulation method. The method comprises the steps that a three-dimensional light field of an X-ray free electron laser pulse light field generated through simulation is acquired; according to the type of the X-ray free electron laser pulse, different dimension reduction methods are selected, and a probability density distribution function of the X-ray free electron laser pulse in the six-dimensional phase space is determined; and selecting the random scatter points based on the probability density distribution function corresponding to the X-ray free electron laser pulse, and determining a target light source model of the X-ray free electron laser pulse light field in the six-dimensional phase space so as to evaluate the free electron laser pulse in the beam line. Six-dimensional phase space distribution is reduced to low dimension, six-dimensional phase space scatter points are obtained, transmission simulation of free electron laser pulses in a beam line is further carried out, the calculation speed is higher compared with a wave optics method, and rapid evaluation of transmission of XFEL pulses in the beam line is achieved.

The invention belongs to the technical field of material processing simulation, and particularly relates to a simulation method for inclusion distribution in a high-temperature alloy deformation process. The simulation method covers the dynamic evolution of the distribution rule of the inclusions in the high-temperature alloy in the thermal processing and thermal deformation process. The method comprises the following steps: firstly, based on actual detection or early-stage simulation, acquiring the surface density distribution condition of the inclusions in the cast ingot, generating a data file representing the initial distribution of the inclusions, providing the data file to Deform software to calculate the position movement condition of the inclusions in the machining process, and performing nuclear density estimation on the distribution of the inclusions after exporting a calculation result; and obtaining a probability density distribution function of the inclusions in the deformation process of the high-temperature alloy. According to the method, the yield, the quality consistency and the service reliability of the deformation high-temperature alloy component are improved, and the method has important engineering application value and strategic significance for promoting high-quality and high-stability manufacturing of key components in the fields of aerospace, energy power and the like in China.

The invention discloses a mobile energy storage peak load shifting scheduling control method and device, a terminal and a medium, and the method comprises the steps: collecting historical output scene data according to renewable resources in a target power distribution network, and constructing a source load prediction model containing a power generation power probability density distribution function in combination with output characteristics; the output simulation parameters output by the source load prediction model are substituted into a preset double-layer structure mobile energy storage optimization scheduling model, and an upper optimization sub-model aims to maximize the net revenue of mobile energy storage configuration and is solved by a heuristic optimization algorithm; and the lower-layer optimization sub-model aims at realizing power distribution network loss minimization, solving by an adaptive wave search algorithm, and obtaining an optimal mobile energy storage scheduling strategy through collaborative optimization, so that efficient solving is realized, the efficiency bottleneck of a traditional algorithm in complex problem solving is broken through, the peak-valley difference of the power distribution network is reduced, and the peak clipping and valley filling effect is improved.

This invention relates to the field of position estimation technology, specifically to a method and apparatus for predicting the motion of a moving maritime target based on limited prior information. The method includes: acquiring prior information; constructing a tangent plane coordinate system based on the prior information; calculating grid edge lengths; determining the unit time step; constructing a range transfer probability density distribution function and a heading transfer probability density distribution function; determining the task area, performing gridding processing on the task area based on the grid edge lengths, and discretizing each grid point to obtain a one-dimensional vector of discrete points for each grid; constructing a transfer distance matrix based on the starting grid and the target grid, constructing a transfer angle matrix based on the starting grid, the target grid, and the destination point, and obtaining the transfer probability matrix based on the transfer distance matrix and the transfer angle matrix. In this way, with limited prior information, it is possible to quickly search for key target areas through observation, thereby improving the efficiency of target search and perception.

The invention discloses an intermittent fault diagnosis and residual service life prediction method for a hybrid system, and relates to the technical field of fault diagnosis and residual service life prediction, and the method comprises the steps: carrying out the fault diagnosis of the hybrid system based on an extended Kalman filtering algorithm; constructing a double-random process model to describe a degradation process and a fault occurrence process of an intermittent fault element; estimating unknown parameters of the double random process model by using a multi-stage expectation maximization algorithm; a concept of an event-triggered collaborative failure process is proposed, and a probability density distribution function of the remaining service life of an intermittent fault element is derived based on a failure mechanism of the process. By utilizing the method, the residual service life prediction of the hybrid system under the intermittent fault can be realized.

The application discloses a marine abnormal target discrimination method based on frequency analysis and grid adaptive expansion. A radar detection area is divided into grids with equal longitude and latitude span, and target frequency data in each grid is counted based on historical AIS track data of each type of target. Then, historical speed and acceleration of different types of targets are counted to obtain target normal activity representation expressed by a Gaussian probability density distribution function. A plurality of random samples are obtained from Dirichlet distribution, and target frequency data statistical results are recalculated in sequence to quantize uncertainty of the target normal activity mode. A cross-subspace support area of an arbitrary grid is formed through an adaptive expansion mechanism based on grid length and uncertainty similarity, and the target activity path coincidence average is calculated based on the uncertainty quantization result of the target type normal activity mode and the cross-subspace support area of the arbitrary grid, and the abnormal target is judged by combining the target speed and other feature coincidence calculation.

The application relates to a group building earthquake risk assessment method and device based on a probability density evolution theory and a storage medium, wherein the method comprises probability earthquake risk analysis and group building earthquake vulnerability analysis, specifically comprises the following steps: acquiring a historical earthquake catalog database; dividing a potential seismic source area; determining seismic activity parameters and ground motion attenuation relations; generating an earthquake risk curve; determining a consistent risk spectrum; generating a random artificial ground motion time history sample curve; acquiring a group building database; establishing a multi-degree-of-freedom bending-shearing type layer model and determining parameters; based on the random artificial ground motion time history sample curve, adopting a central difference method to determine a maximum interlayer displacement angle; determining a probability density distribution function of the maximum interlayer displacement angle; determining the probability of each building being in different damage states; changing an earthquake recurrence period to generate an earthquake vulnerability curve. Compared with the prior art, the application has the advantages of complete evaluation process and accurate evaluation result.

The application discloses a power equipment differentiation early warning method and device based on a fault probability. The method comprises the following steps: acquiring online monitoring data of a power equipment to be measured; in the case that the online monitoring data are distinguished according to different operating conditions, a first fault probability of the power equipment to be measured is calculated according to a voltage grade of the power equipment to be measured and a probability density distribution function of a fault threshold value under the voltage grade which is established in advance, and an early warning grade of the power equipment to be measured is determined according to the first fault probability; in the case that the online monitoring data are not distinguished according to different operating conditions, a second fault probability of the power equipment to be measured is calculated through a corrected fault probability function according to an ambient temperature of the power equipment to be measured and a corresponding correction coefficient under the ambient temperature, and an early warning grade of the power equipment to be measured is determined according to the second fault probability.

The invention discloses an LBM-based numerical simulation research method for a watertight air chamber device, and the method comprises the steps: S1, constructing an LBM-based wave numerical water tank, and simulating a process that waves enter an OWC air chamber; s2, according to the volume and the mass of air in the OWC air chamber at the initial moment in the simulation process, the initial condition of the gas state is obtained; s3, based on the gas state initial condition, updating the volume of air in the gas chamber at each time step, and based on the updated volume and the gas mass at the previous moment, calculating the current gas mass; s4, calculating the pressure intensity in the gas chamber according to the current gas mass and the updated volume, substituting the pressure intensity in the gas chamber into a free surface boundary condition of an LBM model, and updating a density distribution function; and S5, repeating the steps S2-S4 until a time termination condition is met, and completing numerical simulation of the wave energy conversion process in the OWC air chamber.

The application discloses a new weighted mel-frequency cepstral coefficient (MFCC) feature fusion method, which belongs to the field of underwater acoustic target recognition. The application solves the problem of poor separability of the fusion feature based on the existing weight selection method, and the effect of limited recognition accuracy improvement. The application obtains a first-order differential feature by performing differential operation on the MFCC feature, and then performs differential operation on the first-order differential feature to obtain a second-order differential feature. Then, the MFCC feature, the first-order differential feature and the second-order differential feature are linearly weighted to construct a fusion feature with the same dimension as the MFCC feature. The fusion feature of the application contains a large amount of information in the time dimension, and therefore is more robust than the MFCC feature. Moreover, the distance between the probability density distribution functions of the features of two different targets is calculated, and the maximum value of the feature distribution distance is found to determine the weight. The feature after the weight fusion ensures the maximum feature separability. The method of the application can be applied to underwater acoustic target recognition.

The application relates to the field of aerospace technology, and particularly discloses a high-efficiency infrared feature simulation calculation method based on adaptive pre-sampling and application, which relies on a reverse Monte Carlo method to predict the infrared features of an aircraft. By pre-sampling a rough imaging area, a radiation contribution probability density distribution function within a detection viewing angle is estimated based on a calculation result, and finally, according to the information, the number of sampling rays of different detection directions is adjusted, importance sampling is carried out in the imaging area, the problem of invalid sampling in some detection directions is solved, the calculation variance is reduced, and fast and accurate target infrared feature prediction calculation is realized. The application significantly improves the calculation speed of the traditional reverse Monte Carlo method. Through testing, under the premise of ensuring the same calculation accuracy, the calculation speed is improved by nearly 6.75 times.

The invention discloses a method and system for establishing a double-ellipsoid heat source model containing a distribution coefficient, and belongs to the technical field of welding numerical analysis, and the method comprises the steps: introducing a heat flow density distribution coefficient M, and constructing a double-ellipsoid heat source heat flow density distribution function containing the heat flow density distribution coefficient M; according to the energy conservation theorem, the maximum value of the heat flux density of the front and rear semi-ellipsoids is calculated; and establishing a double-ellipsoid heat source model containing a heat flux density distribution coefficient M. On the basis of a traditional double-ellipsoid model, the heat flow density distribution coefficient M is introduced, heat flow distribution is adjusted by setting the heat flow density distribution coefficient M, and the applicability of the double-ellipsoid model to high-pressure TIG, plasma arc welding and the like with more concentrated heat flow and hot wire TIG, submerged-arc welding and the like with more dispersed heat flow is improved; an independent heat flux density distribution coefficient M is introduced, and the M is adjusted, so that a heat affected zone can be adjusted more flexibly and accurately, and the fusion width and the fusion depth measured by an experiment are matched.

The application discloses a kind of ship navigation flow field simulation method, device, equipment and storage medium, applied in flow field simulation field, based on LBM method in analog flow area Analog liquid flow field and gaseous flow field in the process of ship navigation;CLSVOF method is based on The gas-liquid interface between liquid flow field and gaseous flow field is captured and the surface tension of gas-liquid interface is determined, and the density distribution function of gas-liquid interface grid point is corrected based on surface tension;Capture the solid-liquid interface between ship model and liquid flow field;IBM method is based on The volume force term of solid boundary in solid-liquid interface to liquid flow field is determined, and the density distribution function of solid-liquid interface grid point is corrected based on volume force term;DEM method is based on The volume fraction of DEM model in solid-liquid interface grid point is determined, and the density distribution function of solid-liquid interface grid point is corrected based on volume fraction.Compared with prior art, the flow field simulated is more accurate, so that the ship accurately predicts the sea working condition in the process of navigation and avoids risk.

The application discloses a kind of robust DOA estimation methods based on sparse bayesian learning, belong to array signal processing field, the method of this application first introduces position error parameter, and determines the prior distribution of grid error and element position error, secondly with the aid of sparse bayesian learning model, establish joint probability density distribution function, finally with the aid of maximum expectation algorithm to each unknown parameter is iterated, obtains spatial spectrum diagram, estimates the direction of incident signal.This application method can better azimuth estimation accuracy and azimuth resolution, still have good performance under the condition of less snapshot, low signal-to-noise ratio, and the application method has good robustness to the array system that exists element position error, has greater engineering application value.

The application discloses a double Beta distribution vector pulse width modulation method and system introducing an overlap factor, and the method comprises the following steps: constructing a double Beta symmetric distribution introducing the overlap factor, and constructing a probability density distribution function of a double Beta symmetric distribution random carrier cycle strategy; constructing a power spectrum density function of an unfolded random carrier switching cycle signal based on a random carrier signal; performing power spectrum density analysis on the power spectrum density function of the unfolded random carrier switching cycle signal based on the probability density distribution function of the double Beta symmetric distribution random carrier cycle strategy, and realizing random carrier switching cycle permanent magnet synchronous motor control. The application can weaken local energy accumulation, realize smooth spectrum transition, and realize adjustable control of sideband voltage harmonic peak values. The application can be widely applied to the technical field of pulse width modulation driving as a double Beta distribution vector pulse width modulation method and system introducing an overlap factor.

The present application provides a multi-agent area exploration method and system based on tree crown shyness and a pollution source determination method, belonging to the technical field of multi-agent area coverage. The present application can establish an agent area exploration model according to a pollutant density distribution function, determine the direction of the next exploration of the agent using the agent area exploration model, determine the moving path of the agent, obtain the next exploration node of the agent, and achieve the purpose of adaptively determining the shyness space of the agent according to the concentration of the pollutant, thereby covering all areas of pollutant concentration detection. At the same time, based on the tree crown shyness mechanism, the agent can automatically adjust the shyness space according to the adjacent agent or the obstacle occupying space, avoid collision between the agent and the obstacle, realize automatic obstacle avoidance, and avoid path conflict and repetitive exploration.

The invention relates to the technical field of numerical control machine tool environment stress spectrum testing and compiling, in particular to a machine tool environment stress spectrum compiling method based on sample entropy-diffusion kernel density estimation. The method comprises the steps of collecting environmental stress data in the machining process of a numerical control machine tool; calculating a sample entropy sequence corresponding to each sample point of the environmental stress data by using a sliding window calculation method; bandwidth parameters are dynamically adjusted according to the sample entropy sequence, so that a probability density function model based on sample entropy-diffusion kernel density estimation is constructed, a probability density distribution function is calculated, an environment stress interval is divided, the time proportion is counted, and finally the environment stress spectrum of the numerical control machine tool is obtained. According to the method, the accuracy of compiling the environment stress spectrum of the machine tool is remarkably enhanced, the dynamic fluctuation characteristics of the environment stress can be more scientifically described, and the engineering applicability and practicability are improved.

The invention discloses a GIS (geographic information system) automatic drawing method and system based on a multi-modal large model, and relates to a geographic information system (GIS) automatic drawing technology. A natural language drawing request of a user is received, and semantic analysis is performed on the natural language drawing request through the multi-modal large model; calling a corresponding tool function according to a semantic analysis result; extracting original data from the GIS database, and analyzing and processing the original data to obtain a coordinate matrix and weight data; calculating a density value of each spatial position by using a Gaussian kernel function, and generating a continuous density distribution function; calculating a density value of each grid coordinate point, and remodeling a result into a two-dimensional array format corresponding to the coordinate matrix to obtain a target coordinate matrix; and generating a kernel density thermodynamic diagram based on the target coordinate matrix and the weight data, and performing visualization processing on the kernel density thermodynamic diagram. The invention aims to realize automatic analysis and mapping of geographic data through a natural language interaction and function calling mechanism.

The invention provides a computer interlocking system fault diagnosis method and system, and the method comprises the steps: constructing a fault tree according to collected fault information, and converting the fault tree into an object-oriented Bayesian network model; according to the accumulated fault occurrence frequency and the initial failure rate, continuously updating the instantaneous failure rate, taking the instantaneous failure rate as a prior probability, taking the object-oriented Bayesian network model as a conditional probability, and obtaining a fault probability density distribution function of the whole computer interlocking system according to the conditional probability and the prior probability; and obtaining the fault occurrence probability of each basic fault event according to the fault probability density distribution function. According to the fault diagnosis method and system for the computer interlocking system, intelligent fault diagnosis of the computer interlocking system is achieved, the workload of maintenance personnel is relieved, meanwhile, the difficulty of equipment overhaul is lowered, an accurate basis is provided for system maintenance, and continuity and stability of railway traffic are guaranteed.

The invention provides a digital pre-distortion orthogonal polynomial generation method and device, equipment, a medium and a product. Relates to the technical field of wireless communication. The method comprises the following steps: normalizing the amplitude of an input digital modulation complex signal based on the maximum amplitude of the input digital modulation complex signal to obtain a normalized complex signal; performing probability density distribution function fitting on the plurality of amplitude sample points of the normalized complex signal to determine a target distribution function; and generating a normalized orthogonal polynomial and an orthogonalization matrix based on the target distribution function and the normalized complex signal by adopting a preset normalized three-item recursive algorithm. According to the method provided by the invention, a standardized orthogonal polynomial construction method based on signal real statistical characteristics is introduced, so that the high efficiency and stability of digital pre-distortion coefficient estimation are realized, and the linearity and spectrum out-of-band rejection capability of a communication system are improved.

The present application relates to the technical field of metal material mechanical behavior, in particular to a method for predicting precursor of amorphous alloy shear instability based on sawtooth rheological behavior, which is realized by the following steps: step one: obtaining stress-time curve; step two: obtaining stress drop rate-time curve; step three: obtaining stress drop amplitude value compensation cumulative probability density distribution function; step four: obtaining stress drop amplitude value universal law; step five: obtaining quantitative relationship between maximum stress drop amplitude value and loading strain rate; step six: according to the normalized parameter of stress drop amplitude value universal law and the fitting index of quantitative relationship between maximum stress drop amplitude value and loading strain rate, the precursor signal of amorphous alloy room temperature shear instability under different service conditions is obtained. The present application effectively solves the problems of low prediction accuracy and limited application range of existing amorphous alloy shear instability precursor prediction technology, and is suitable for the prediction of amorphous alloy shear instability precursor.

The invention discloses a micro-grid optimization scheduling method considering system operation risk and user satisfaction, which comprises the steps of generating a power prediction curve of wind power and photovoltaic new energy output in a micro-grid, obtaining a prediction error range by adopting a Monte Carlo simulation method, and generating a typical scene set, obtaining a wind and light prediction error probability density distribution function through a data fitting method; micro-grid operation risks increased by high or low output caused by wind power and photovoltaic power are quantified by adopting the value-at-risk and conditional value-at-risk theories; obtaining the scheduling cost of the adjustable load in the micro-grid system; taking the minimum sum of the conditional value-at-risk cost and the operating cost of the micro-grid system as an objective function, taking the user satisfaction level under demand side response as a constraint, and establishing a micro-grid optimal scheduling model considering the operating risk and the user satisfaction; solving the model by adopting a genetic algorithm, and optimizing the optimal scheduling result of the grid-connected micro-grid system; and the safety, economy, stability and reliability of the micro-grid are enhanced.