23 results about "Uncertainty model" patented technology

The present application relates to the technical field of power system operation analysis and regulation, and specifically discloses a kind of VSC-HVDC system power flow regulation capability evaluation method, comprising the following steps: S1, the power flow regulation range mathematical model of establishing containing back-to-back flexible HVDC transmission system BTB;S2, establish new energy output uncertainty model;S3, construct power flow over-limit risk and risk adaptive power regulation mechanism;S4, generalized polynomial chaos matrix method is used to evaluate the two-way power flow regulation range;S5, output power flow regulation range evaluation results and visual index.The present application uses the above-mentioned VSC-HVDC system power flow regulation capability evaluation method, realizes the collaborative quantification of new energy randomness, operation risk and BTB regulation capacity, improves the accuracy and engineering practicability of the evaluation results, and provides a decision basis for power system planning and design, operation control.

This invention relates to the fields of tunnel engineering and 3D geological modeling technology, specifically to an intelligent modeling method and system for 3D geological uncertainty in tunnels based on transition probability geostatistics and sparse boreholes. The method includes: S1, integrating multi-source data to construct a 3D geological conceptual model; S2, statistically characterizing a one-dimensional transition probability matrix, calculating and fitting a spatial continuity and 3D anisotropic variability function model; S3, calculating the prior spatial probabilities and transition adjustment factors for various lithologies, obtaining the posterior lithology distribution of nodes through Bayesian intelligent updating, and initially assigning lithology categories to nodes through random sampling; S4, assigning the most probable lithology category to each grid node, calculating the variance of lithology values ​​across all implementations, and measuring model uncertainty; S5, outputting the optimal 3D uncertainty model for the tunnel; and S6, verification and evaluation. This invention can effectively achieve 3D heterogeneous modeling and explicit quantification of uncertainty under strong geological constraints.

This invention discloses a steerable chassis trajectory tracking control method based on composite time delay estimation and compensation, comprising the following steps: establishing a discretized vehicle dynamics model considering steering execution lag, clarifying the difference between steering lag and stochastic network delay, and providing a basis for compensation design; designing an adaptive prediction time-domain function, dynamically calculating the optimal prediction time domain based on the current vehicle speed, road curvature, and network delay state; designing a control quantity considering steering lag and constructing a time delay uncertainty model, using the polyhedral method to handle the parameter uncertainty caused by time delay; performing rolling optimization solution, dynamically adjusting the optimization weights based on the uncertainty model, the adaptive time domain, and an online weight adaptive mechanism of reinforcement learning to obtain the optimal control increment. This invention solves the problems in existing technologies that simplify complex internal time delays into a single, fixed model, failing to distinguish and process their physical causes, resulting in limited compensation accuracy, poor system dynamic adaptability, and insufficient robustness.

This application discloses a method for calculating the thrust envelope of a solid rocket motor based on servo uncertainty, relating to the field of solid rocket motor technology. This method focuses on the gas valve servo system of a throat-operated solid rocket motor, performing uncertainty modeling of the motor drive system and transmission system, analyzing the main influencing parameters of servo system uncertainty, including transmission clearance, eccentric shaft friction coefficient, and other uncertainty parameters and their probability distributions; generating random parameter samples based on the uncertainty model, and using the servo system control model to solve for the actual throat displacement response under random samples, obtaining the engine thrust time history under single-valve and multi-valve cooperative working modes; and fitting the thrust uncertainty envelope by calculating the mean and standard deviation of the thrust response through Monte Carlo simulation. This application quantitatively characterizes the degree of influence of internal factors of the servo mechanism on thrust performance, providing a reference for ensuring precise thrust control of solid rocket motors.

This invention discloses a method for optimizing the dynamic transmission error distribution characteristics of a harmonic reducer, comprising: establishing a static transmission error probability model to obtain the probability distribution of the overall static transmission error; constructing a dynamic transmission error mathematical model considering static transmission error and dynamic parameters; constructing a high-precision surrogate model of dynamic transmission error including the probability distribution of static transmission error and the range of dynamic parameters; obtaining the probability distribution of dynamic transmission error; and dynamically adjusting the range of dynamic parameters based on a particle swarm optimization strategy to find the parameter range that optimizes the dynamic transmission error distribution. By introducing static transmission error into the dynamic model of the harmonic reducer, considering the influence of the processing and assembly of the harmonic reducer on the transmission error, a probability-range hybrid uncertainty model is used to mathematically describe the probability distribution characteristics of static transmission error and the range of dynamic parameters, and a Chebyshev polynomial method is used to construct an approximate model of dynamic transmission error.

This invention provides a method for predicting the dominant frequency of blasting vibration by integrating parameter fluctuation processing and the Dream Optimization Algorithm (DOA), belonging to the field of underground engineering safety control technology. The system includes a data acquisition and uncertainty modeling module, a data preprocessing and feature construction module, a hyperparameter optimization module, and a prediction model training and result output module. The method obtains relevant parameters through on-site investigation and monitoring, and generates an extended sample set using a combination of probabilistic perturbation modeling and fuzzy triangular modeling. The data is preprocessed and features are selected. The DOA algorithm is used to globally search the hyperparameters of the Support Vector Regression (SVR) model, and the optimal hyperparameter combination is selected by combining a robustness fitness function. The SVR model is trained, and prediction results and uncertainty prediction intervals are generated. This invention can explicitly characterize the uncertainty of geological parameters, achieve efficient global optimization of hyperparameters, improve prediction accuracy, robustness, and generalization ability, and is applicable to different geological conditions and blasting scenarios. It can be extended to various blasting dynamic response prediction tasks.

The application provides a kind of biomass energy participates in peak shaving green township power distribution network regional collaborative autonomy method, the method first obtains the source and load prediction data of each regional microgrid of green township, energy supply equipment basic parameters and local grid purchase and sale electricity price data;Then, according to the time scale characteristics of source and load prediction data, an uncertainty model of source and load is established;Then, a double-layer optimization model of green township power distribution network is constructed, including a regional microgrid energy trading price model based on the energy supply and consumption conditions of each regional microgrid as the upper layer strategy, and a two-stage robust optimization model of regional microgrid considering comprehensive constraint conditions with economic efficiency and clean energy consumption rate as optimization objectives based on the uncertainty model of source and load and regional microgrid energy trading price as the lower layer strategy;Finally, the double-layer optimization model of green township power distribution network is iteratively solved, and the distributed energy output, energy storage charging and discharging power and regional interaction power that make the system economic efficiency and clean energy consumption rate optimal are obtained.

The present application relates to the field of new energy power system and optimal scheduling, in particular to a kind of multi-time scale energy management method of light storage grid-connected system, comprising S1, based on day-ahead operation forecast data, under the condition that energy storage operation constraint, grid-connected constraint and system power balance constraint are satisfied, to establish day-ahead scheduling model with light storage grid-connected system operation cost, S2, introduce uncertainty modeling mechanism in day-ahead scheduling model, generate day-ahead scheduling scheme considering uncertainty, S3, construct intraday operation optimization model under shorter time scale, and combine system real-time operation state, to the rolling correction of day-ahead scheduling scheme, S4, construct penalty function to the deviation of intraday operation decision relative to day-ahead scheduling scheme, combine day-ahead scheduling scheme and output the final operation control instruction of light storage grid-connected system, the present application system effectively suppresses minute-level power fluctuation, while ensuring the safe operation of system, reduces the transaction cost with power market and prolongs the service life of energy storage.

The application discloses a method and device for evaluating the radiation emission level of power electronic equipment based on a reverberation chamber, a medium and equipment. The method comprises the following steps: obtaining a reverberation chamber radiation receiving power sample and a transmission coefficient sample; obtaining a radiation power calculation model of the power electronic equipment to be measured, constructing a radiation power function of the power electronic equipment to be measured, obtaining a probability density function of a radiation power estimation value based on the radiation power function of the power electronic equipment to be measured, and obtaining an uncertainty model; evaluating the first radiation emission level of the power electronic equipment to be measured based on the radiation power calculation model and the uncertainty model; and determining the second radiation emission level according to the radiation power function of the power electronic equipment to be measured. The method can accurately describe the statistical distribution characteristics of the electromagnetic field inside the reverberation chamber, quantitatively analyze the influence of the number of independent samples on the measurement uncertainty, and effectively evaluate the statistical characteristics and measurement uncertainty of the radiation emission of the power electronic equipment.

The application belongs to the technical field of satellite-ground communication and physical layer security, and particularly relates to a satellite sensing assisted secure communication method based on dynamic uncertainty modeling. The method comprises the following steps: constructing a sensing-integrated transmission architecture, using a dual-function beam to actively detect potential threat areas while transmitting data to downlink users; performing primary positioning by capturing target echo signals, and dynamically calculating the uncertainty boundary of positioning in combination with echo signal-to-noise ratio; establishing a multi-dimensional channel uncertainty model, including a user-side angle uncertainty model established for the high-speed movement of low-orbit satellites, and a norm-bounded uncertainty region constructed for eavesdroppers based on the above boundary; taking the maximization of the worst-case secrecy rate as a global optimization goal, jointly designing optimal beamforming and radar receiving filters under the constraints of sensing, power and security; and using semi-positive relaxation and convex-concave process algorithms to convert the non-convex problem into a convex optimization sub-problem for iterative solution. The application realizes a closed loop of active sensing and mathematical modeling, and significantly improves the dynamic adaptability and robustness of satellite secure communication.

This invention relates to a multi-dimensional evaluation method and system for the carrying capacity of distribution networks based on probabilistic assessment. The method includes: collecting distribution network operation data and constructing a distributed generation processing probability model and a load demand uncertainty model; performing sampling and power flow calculations based on the distributed generation processing probability model and the load demand uncertainty model; calculating and normalizing multi-dimensional indicators based on the calculation results at each sampling time; the multi-dimensional indicators include resilience indicators, including transient voltage stability index, islanding reconstruction power, and energy storage throughput margin; calculating the subjective and objective weights of the multi-dimensional indicators and fusing them to obtain a comprehensive weight; calculating the score for each sampling and the expected score based on the comprehensive weight and the normalization result; calculating the risk probability based on the expected scores of all samplings; and classifying the carrying capacity level based on the expected score and the risk probability. Compared with the prior art, this invention improves the reliability of carrying capacity evaluation for distribution networks with uncertainties.

This invention discloses a multi-objective robust optimization configuration method for integrated energy systems. The method comprises the following steps: First, establishing a multivariate load uncertainty model; then, establishing a multi-objective robust bi-layer joint optimization configuration model for the integrated energy system: the upper-layer model is the master model, and the lower-layer model is the sub-model. Through iterative optimization of the upper and lower layers, the optimal configuration strategy for the integrated energy system is obtained; finally, an intelligent optimization algorithm is used to solve the problem. For the multivariate load uncertainty model of the lower-layer model, a multi-scenario technique and a minimum-maximum regret criterion are used for joint solution, ultimately obtaining the Pareto solution set of the multi-objective robust configuration scheme for the integrated energy system. This invention proposes a multi-objective robust bi-layer joint optimization configuration method for integrated energy systems, which can provide optimized configuration schemes for integrated energy systems, improve energy utilization efficiency, reduce total costs during the planning period, and reduce pollutant emissions.

This invention relates to the field of compressor blade fouling, and particularly to a two-dimensional geometric uncertainty modeling method for compressor blade fouling. Based on the actual morphology of compressor blade fouling, the fouling is divided into a compact layer and a loose layer along the blade profile normal. Considering the uncertainty of fouling distribution on the compressor blade surface under actual operating conditions, a sparse compact layer fouling geometric uncertainty model is constructed by combining the KL expansion method with a sparse grid numerical integration method, and a randomized coarse loose layer fouling geometric uncertainty model is also established. The geometric uncertainty modeling method for compressor blade fouling provided by this invention is applicable to establishing geometric uncertainty models for compressor blades with arbitrary fouling distributions. The sparse blade fouling geometric model represents all geometric models within the entire distribution space, making it particularly suitable for providing CFD calculation geometric models for predicting the probability of aerodynamic performance degradation in fouled compressor blades.

This invention discloses a multi-resource collaborative scheduling method for distribution networks under earthquake disasters, designed to address the challenges posed by the uncertainty of line faults and the interruption of power supply to critical loads caused by earthquakes to the operation of distribution systems. The method constructs a line fault probability model using a seismic attenuation model and vulnerability curves from the US Earthquake Disaster Loss Assessment System, and introduces line reinforcement decisions to form a decision-dependent uncertainty model, accurately reflecting the coupled impact of earthquake intensity and planning decisions on line faults. Simultaneously, a multi-dimensional flexible resource scheduling framework integrating mobile hydrogen energy resources, building thermal inertia, and demand response is adopted to enhance energy supply capacity and system resilience during the disaster phase. A 1-norm-based multi-resource collaborative scheduling optimization model is introduced to characterize the probability shift of earthquake scenarios, and a column constraint generation algorithm is designed to achieve efficient collaborative solution of planning and scheduling. This invention can significantly improve the operational reliability of distribution networks under earthquake disaster scenarios and has certain engineering application value.

This application provides a slope uncertainty modeling method and related equipment based on micromotion analysis. The slope uncertainty modeling method includes: acquiring micromotion signals of the slope collected by a detector array; obtaining a shear wave velocity profile of the slope based on the micromotion signals; the shear wave velocity profile is used to reflect the number of strata, the shear wave velocity of each stratum, and the thickness; fitting the autocorrelation function of the strength parameters of each layer and the corresponding correlation length based on the shear wave velocity profile; the correlation length is the horizontal correlation length; and constructing a random field model of slope strength parameters based on the autocorrelation function and the correlation length to achieve slope uncertainty modeling. This method can accurately obtain the parameters required for constructing the random field, especially the horizontal correlation length, thus improving the accuracy of the final obtained slope parameter random field model.

This invention relates to the field of computer vision technology, specifically to a method and apparatus for calculating the position coordinates of an object through AI image recognition. The method involves controlling a mobile camera device to capture a first image and perform recognition, thereby achieving multi-dimensional geometric uncertainty modeling and determining the dynamic confidence level of the first image for object recognition. This allows the mobile camera device to be controlled to move in the error-sensitive direction (i.e., the depth direction of AI recognition). Under multiple physical constraints and corrections, a second observation and capture are performed to obtain a second image, improving recognition efficiency and accuracy. Finally, based on the first coordinates of the target object in the first image and the second coordinates of the target object in the second image, a fusion threshold is applied based on the uncertainty models of the two capture stages to determine the target object's location data. This application addresses the issue that AI image recognition relies on the AI ​​confidence level of the model, which can lead to misjudgments, blind spots, and illusions. From a physical perspective, it improves accuracy through movement control of the environment and the device's optical path.

The application discloses a eutectic high-entropy alloy design method and device based on machine learning and uncertainty evaluation. The design method comprises the following steps: S1, collecting component data and crystal structure data of high-entropy alloys, and establishing a high-entropy alloy crystal structure data set through data cleaning and classification; S2, training using a machine learning classification algorithm to obtain a trained machine learning classification model; S3, designing a virtual component space and predicting the same using the machine learning classification model to obtain a predicted crystal structure; and S4, evaluating the predicted crystal structure in combination with an entropy-based uncertainty model, and screening components of a eutectic high-entropy alloy with low uncertainty according to a calculation result. The design method can realize accurate prediction of the eutectic high-entropy alloy and accelerate development of advanced eutectic high-entropy alloys.

The present application relates to the technical field of multi-energy scheduling, in particular to a comprehensive energy scheduling method and system considering demand response uncertainty, which provides for obtaining electric, gas and heat load data and renewable energy prediction output data in a comprehensive energy system, based on user response triggering boundary effect, cross-period load rebound effect and heterogeneous energy physical coupling constraints, a comprehensive demand response uncertainty model considering space-time coupling characteristics is constructed; based on the model, a double-layer coordinated control framework of source-side controller advanced decision and load-side execution unit tracking response is constructed; according to historical data, load tracking deviation is estimated and instruction correction amount is calculated, and the optimal control instruction sequence is obtained through iterative updating, and is converted into source-side unit output instruction and load-side load control instruction to execute scheduling. The present application realizes the collaborative optimization operation of source and load under the demand response uncertainty environment, improves the renewable energy consumption rate, suppresses the load peak-valley difference and guarantees the user comfort.

A method of modelling a geological structure comprises obtaining geological data for a geological feature, the geological data comprising (i) first measurements corresponding to a first known location of the geological feature, and (ii) second measurements corresponding to at least one of a second known location of the geological feature, an orientation of the geological feature, and an overall orientation of the geological feature, and processing the geological data, including the first and second measurements, with a probabilistic model to generate an uncertainty model comprising a probabilistic representation of other locations of the geological feature in three-dimensional space between the known locations.

The application discloses a kind of adaptive end-to-end decision planning methods for vehicle-mounted edge computing platform, method includes: obtaining the driving parameter of ego vehicle, navigation instruction and image frame sequence, and extracting state feature vector;According to state feature vector, calculate environmental uncertainty using pre-trained SQR network model, and calculate model uncertainty according to multi-SQR network integration calculation model, and add to obtain total uncertainty;Compare environmental uncertainty, model uncertainty, total uncertainty with the division threshold obtained according to uncertainty distribution statistics, determine the risk quadrant to which the current vehicle state belongs;According to the risk quadrant of current state, select the offline trained small expert model, medium expert model or large expert model to output future reference trajectory and target speed.The application realizes adaptive, safe and efficient end-to-end decision planning for multi-scene driving behavior under limited vehicle-mounted computing power.

The application discloses a power grid operation risk assessment method based on load prediction error probability distribution, relates to the technical field of power grid operation safety risk assessment, and comprises the following steps: analyzing the time-space characteristics of historical load prediction errors, and constructing a dynamic probability distribution model; and combining the power grid static safety limit to determine the allowable load error boundary. The super-short-term load prediction result and the dynamic probability distribution model are used to deduce the probabilistic change corridor of future load. The probabilistic corridor is used as the uncertainty input of injected power to perform interval power flow calculation considering network constraints, and the probability distribution of device load rate is scanned. Based on the load rate probability distribution and the preset risk probability measure, a quantitative risk index is synthesized, the power grid risk level is determined, and an evaluation report containing risk positioning is generated. The application realizes the whole process quantification of power grid operation risk from uncertainty modeling to probabilistic evaluation, and improves the accuracy of risk assessment and the decision guidance value.

The application discloses a kind of ore body three-dimensional uncertainty modeling methods of fusing geologic constraint and probability field, it is related to geological modeling technical field, ore body three-dimensional uncertainty modeling method of fusing geologic constraint and probability field mainly includes: according to original borehole data, constructs single-target stratum prior model, including the three-dimensional prior probability model of target stratum, three-dimensional fault influence field and lithology-color mapping relationship, and constructs multi-stratum three-dimensional prior model, combined with single-target stratum prior model, simulation result is obtained using machine learning and multiple-point geostatistics simulation method.The ore body three-dimensional uncertainty modeling method of fusing geologic constraint and probability field provided in the application can improve the geological rationality and spatial reliability of ore body three-dimensional modeling under the condition of sparse data.