8 results about "Interval model" patented technology

The invention relates to the technical field of global navigation satellite system reflection, in particular to a GNSS-R sea surface wind speed inversion method, which comprises the following steps of: firstly, comprehensively considering multiple constraint conditions such as inversion error distribution characteristics, interval quantity, sample distribution uniformity and interval minimum sample requirements by utilizing the global optimal search characteristic of an SA algorithm; a multi-objective optimization function is constructed to preliminarily divide a wind speed interval, and then a GD algorithm is adopted to accurately adjust the boundary of the preliminarily divided interval. And constructing a special XGBoost prediction model for each wind speed interval, and finally, fusing wind speed prediction results of a plurality of sub-interval models through a stacked integrated learning architecture to obtain a wind speed inversion value. According to the method provided by the invention, the problem that the inversion error of a traditional single model in different wind speed ranges is extremely unstable can be effectively relieved, the inversion precision of the whole wind speed range is improved, and meanwhile, the problem that a real-time observation sample cannot correspond to a sub-model under an interval model in practical application is solved; and the application conversion of the GNSS-R sea surface wind speed inversion technology is promoted.

The invention discloses a passenger restraint system collaborative design method, device and equipment, a medium and a product, and relates to the technical field of vehicle safety. The method comprises the following steps: acquiring an initial simulation model and physical test data, and identifying system determinacy design variables and uncertainty parameters; a baseline simulation model is obtained according to the test data calibration model; through an adaptive multi-dimensional parallelepiped interval model quantization parameter value interval and correlation, joint uncertain domain information including a parameter midpoint, a radius and a correlation coefficient matrix is obtained, and the matrix can be dynamically updated; generating sample points based on the baseline model and the joint uncertain domain information, obtaining system response data and constructing an agent model; designing variables are optimized through an agent model to obtain an optimal design scheme, after the scheme is fixed, minimization of time sequence response errors and maximization of Bayesian factors serve as target calibration parameters, finally, final design data of the system are determined by combining the minimization of the time sequence response errors and the maximization of the Bayesian factors, and high-confidence-coefficient design is achieved.

The application discloses a kind of optimal load carrying capacity evaluation method of distribution network considering load response and development, which comprises the following steps: S1, the influence of demand response on load curve and power supply reliability under normal / fault two kinds of scenarios is considered, and a load carrying capacity interval model is constructed;S2, considering the uncertainty of load development, a load carrying capacity optimization model with economic optimization as the target is constructed;S3, the outer model is solved by genetic algorithm, to improve the solving speed, a linearization processing method based on the inner model of large M method is proposed and solved by CPLEX, so as to obtain the optimal load carrying capacity of distribution network and the corresponding demand response strategy.The application provides a solid support for the judgment and determination of distribution network planning scheme.

The application discloses a station yard track operation intelligent adjustment method for a virtual marshalling train, and comprises the following steps: obtaining running state and unmarshalling demand information of the virtual marshalling train; constructing a station yard track logical available interval model based on a route protection boundary; constructing constraint conditions required for track operation adjustment based on the track logical available interval model; generating a track operation adjustment scheme with unmarshalling efficiency as a target; reconstructing the track operation scheme under running disturbance conditions based on a dynamic adjustment and asynchronous triggering mechanism; updating logical occupation states and locking information of corresponding tracks according to the determined track operation scheme, and synchronizing the logical occupation states to a station yard resource management module of a centralized dispatching system, so as to constrain subsequent route arrangement and track resource allocation processes; and the logical occupation states indirectly affect route arrangement results through a dispatching layer resource constraint mechanism without directly participating in interlocking or train control instruction generation. The application also discloses a system, a device, an electronic equipment and a computer readable storage medium.

The invention discloses a thin-wall structure dynamic parameter uncertainty quantification and vibration analysis method, relates to the field of aircraft thin-wall structure dynamics in the civil aviation field, and aims to solve the problem that vibration characteristics of an aircraft thin-wall structure in the civil aviation field are difficult to accurately analyze due to the fact that the aircraft thin-wall structure is affected by multi-source uncertainty under complex working conditions. According to the method, the difficulties of uncertainty source coupling, parameter time varying, high dimension and strong nonlinearity of vibration characteristics and the like are analyzed, and corresponding solutions are provided: a multi-source uncertainty coupling analysis framework is constructed to realize accurate description of complex coupling uncertainty; establishing a time-varying uncertainty dynamic updating model, and quantizing parameter time-varying uncertainty in real time; and a high-efficiency high-dimension nonlinear vibration characteristic analysis method is developed, and the calculation cost is reduced. According to the method, the physical mechanism and the machine learning technology are combined, the uncertainty is described by adopting the probability model and the interval model, and the modeling precision of the aircraft thin-wall system structure dynamic model is improved.

The application relates to a reliability analysis method of a magnetorheological fluid brake based on an ellipsoid model. The method mainly solves the technical problem that a large amount of data is required in a method for describing a failure rate by using fuzzy numbers. The technical scheme of the application is as follows: the magnetorheological fluid brake is composed of a transmission shaft, a bearing, an end cover, a brake shell, an excitation coil, magnetorheological fluid, a brake disc, a sealing ring and a key, and the reliability analysis method comprises the following steps: step 1: adopting a fault mode diagnosis to perform fault layering on the magnetorheological fluid brake and determining a fault damage level; step 2: according to an analysis result, establishing a dynamic fault tree, and modularly decomposing the dynamic fault tree into a dynamic fault subtree module and a static fault subtree module; step 3: according to small sample data, obtaining upper and lower interval extreme values of failure rates of various components, constructing an interval model of failure rates of various bottom events, and further constructing an ellipsoid model of a top event of each fault subtree; and step 4: using a BDD method and a Markov chain to perform qualitative and quantitative analysis and solving on the dynamic fault tree.

This invention discloses an artificial intelligence-based method and system for selecting power grid projects, relating to the field of artificial intelligence technology. The method includes: collecting and preprocessing raw data to construct a dual dataset; constructing a risk interval model, taking the proposed power grid project dataset as input and outputting a risk interval; verifying the risk interval by setting validation conditions; calculating the revenue stability index, environmental impact score, and transmission reliability; and concatenating these with the risk interval to obtain an input vector; constructing a prediction model, taking the input vector as input and outputting a single-point benefit function value; calculating the comprehensive benefit score and benefit-cost ratio; obtaining decision variables based on binary value conversion; defining an objective function; constructing constraints; and optimizing to generate the optimal power grid project selection combination as the final basis for power grid project selection. This invention significantly improves the scientific rigor, robustness, and real-time performance of the power grid project selection process.

This application discloses a collaborative design method, apparatus, equipment, medium, and product for occupant restraint systems, relating to the field of vehicle safety technology. The method includes: acquiring an initial simulation model and physical test data; identifying deterministic design variables and uncertain parameters of the system; calibrating the model based on the test data to obtain a baseline simulation model; quantifying the parameter value range and correlation through an adaptive multidimensional parallelepiped interval model to obtain joint uncertainty domain information including the parameter midpoint, radius, and correlation coefficient matrix, with the matrix dynamically updated; generating sample points based on the baseline model and joint uncertainty domain information, obtaining system response data, and constructing a surrogate model; optimizing the design variables through the surrogate model to obtain the optimal design scheme; fixing this scheme and calibrating the parameters with the objectives of minimizing the time-series response error and maximizing the Bayesian factor; and finally combining the two to determine the final system design data, achieving a high-confidence design.