20 results about "Interval model" patented technology

The invention provides a metro section parameterized linear digital model construction method based on Revit and Dynamo, and the method comprises the steps: building a three-dimensional space control reference through analyzing track line design data, and constructing a metro section model in a manner of combining parameterized modeling with an intelligent algorithm; the contact network and pipeline models are ensured to meet the standard requirements through real-time verification of the conductor height change rate and the turning radius-pipe diameter matching matrix; space coordinate unification and interface matching precision control are achieved through a multi-professional model cooperation system, and a parameter linkage updating mechanism is established to respond to design changes; and finally, outputting an LOD400 precision BIM model and associating construction data for dynamic correction, and supporting RVT / IFC format conversion and engineering quantity automatic statistics. The method is mainly used for efficient collaborative design and construction management and control of metro sections, the modeling precision and the multi-specialty coordination efficiency can be improved, and the design error rate is reduced.

The invention discloses a wing load uncertainty quantitative model correction method based on credible Bayesian updating. The method adopts a non-probability interval mode to quantify the uncertainty of some parameters of the wing in a flight condition, establishes an aircraft wing load prediction finite element model, and combines uncertainty propagation influence research to obtain the load uncertainty of the aircraft wing. A credible Bayesian theory is utilized, new sample points obtained by a simulation experiment are introduced to update interval parameters, a Markov chain Monte Carlo method is utilized to obtain a relational expression between a non-probability quantization set and credibility, and a load uncertainty quantization model of a typical wing structure is established and corrected. The method has remarkable advantages in processing small sample and poor information problems in engineering practice, the credibility is utilized to describe the reliability degree of the reliability obtained through calculation, and a solution is provided for the balance problem between conservative property and credibility of the interval model.

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.

A model generation unit (17) generates, on the basis of change interval data obtained from log data stored in a log data storage unit (12), a change interval model obtained by modeling change interval data representing an interval between a start signal that causes operation of a device and a completion signal that appears as a result of the operation of the device. An abnormality degree calculation unit (18) calculates the degree of abnormality of the operation of the device from an actual measurement value, which is an interval between the start signal and completion signal obtained from the device, and a predicted value, which is an interval predicted using the change interval model generated by the model generation unit (17).

The invention discloses a scenic spot equipment management method based on artificial intelligence, and relates to the technical field of intelligent equipment management, and the method comprises the following steps: S1, constructing an equipment operation stability interval model under the constraint of a natural environment; s2, performing difference degree mapping on the real-time sensing data according to the stable operation interval model; s3, performing multi-device coupling interference modeling based on the difference degree; s4, performing multi-stage state evolution in combination with the difference degree and the coupling propagation graph; s5, identifying a risk cluster based on a multi-stage state evolution result and constructing an event chain response model; and S6, executing the standard response scheme by using the event chain response model. Through the arrangement of the multi-device collaborative operation model and the multi-stage state evolution analysis, the two main problems of insufficient analysis of the coupling effect between the devices and weak prediction capability of the dynamic environment change in the prior art are effectively solved, the precision of device management is improved, and the capability of coping with sudden risks is also greatly improved.

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 present application relates to the technical field of commercial vehicles, and specifically provides a robustness optimization method for a commercial vehicle suspension system, comprising the following steps: establishing a power assembly coordinate system, a power assembly mass matrix and a stiffness matrix, and constructing an expression of system natural frequency and decoupling rate; performing a transmission path analysis, and establishing a mathematical relationship between suspension design parameters and an in-vehicle target point; analyzing the sensitivity of an objective function to variables, and determining design variables and parameter variables; using an interval model to represent design variables, using a probability model to represent parameter variables, and constructing an interval-probability model to describe the uncertainty of suspension system variables; using Monte Carlo random number simulation technology to perform multi-objective robustness optimization; and performing Monte Carlo analysis on the optimization results, and evaluating the robustness of the suspension system. Based on the transmission path analysis method, the response of the suspension system and the in-vehicle target point of interest is linked, and vibration isolation and noise reduction at the vehicle level is achieved.

The application provides a rotor assembly geometry precision prediction and optimization method considering multi-source uncertainty, comprising: analyzing uncertainty affecting assembly precision in a rotor assembly process; representing the uncertainty as random variables and interval variables respectively through a random model and an interval model; establishing a precision prediction model considering random-interval hybrid uncertainty to predict rotor assembly coaxiality error; solving the uncertainty model based on a Monte Carlo method to obtain probability statistical results of upper and lower bounds and midpoint values of the coaxiality error interval; performing coaxiality out-of-tolerance probability interval estimation, constructing a rotor coaxiality double-layer optimization model under hybrid uncertainty, and optimizing to obtain optimal installation phases of each stage disk. The application applies an uncertainty quantification method to prediction and optimization of rotor assembly geometry precision, can realize accurate prediction and reliable optimization of rotor assembly geometry precision, and meets high-reliability assembly requirements of an aero-engine rotor.

The invention discloses a power grid project selection method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting original data, carrying out the preprocessing, and constructing a dual-data set; constructing a risk interval model, taking a proposed power grid project data set as an input and an output risk interval, setting a verification condition to verify the risk interval, calculating an income stability index, an environmental influence score and power transmission reliability, and combining the risk interval to perform splicing to obtain an input vector; constructing a prediction model, taking an input vector as an input, outputting a single-point benefit function value, calculating a comprehensive benefit score and a benefit-cost ratio, obtaining a decision variable based on binary value conversion, defining a target function, constructing a constraint condition, and optimizing to generate an optimal power grid project selection combination as a final basis for power grid project selection; according to the method, scientificity, robustness and real-time performance in a power grid project selection process are remarkably improved.

The invention discloses a graduate student classification cultivation quality evaluation system and method based on an interval model, and relates to the technical field of education evaluation. The system comprises a model construction module, an acquisition processing module, a matching processing module, a comprehensive evaluation module and an evaluation analysis module. Classification cultivation quality, scientific research and practice achievements and the like are taken as core indexes, an index interval is determined by combining historical data and industry standards, weights are calculated through an expert scoring mean value, and subjectivity and one-sidedness of traditional evaluation are avoided; for missing values, similar sample mean values are adopted for filling, abnormal values are eliminated according to interval screening logic, the data quality is guaranteed, and the method is suitable for multi-college, multi-specialty and cross-period evaluation scenes; relative positions of indexes are dynamically calculated through interval matching, a hierarchical evaluation result is obtained in combination with a weighting algorithm, advantages and shortages are accurately positioned through transverse and longitudinal comparison, a reliable basis is provided for supervision of education management departments and self-improvement of colleges and universities, and classified cultivation quality of graduate students is promoted to be efficiently improved.

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.

The invention discloses a heavy haul railway virtual marshalling operation tracking interval dynamic keeping method, and relates to the technical field of rail transit, and the method comprises the steps: building a heavy haul train multi-particle dynamic model; establishing a dynamic tracking interval model; determining an index function based on a consistency control protocol containing an expected interval, and generating a reference speed curve according to the index function, the line information and the heavy haul train multi-mass-point dynamic model; the actual output of the heavy haul train is determined through the MPC controller; constructing an online optimization problem model; the online optimization problem model comprises a cost function and a constraint condition; the cost function takes reduction of coupler force, tracking errors and reduction of operation energy consumption as optimization objectives; and solving the online optimization problem model to obtain an optimal control sequence of each heavy-load train in a limited time domain, thereby realizing safe short-distance stable tracking of the heavy-load train under a complex operation condition.

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.

The application discloses a kind of based on dynamic marginal cost's carbon emission quota real-time regulation method and system, it is related to carbon emission quota real-time regulation technical field, including the following steps, the real-time disturbance data of construction site is collected, current disturbance state is identified, and disturbance state is mapped to pre-defined state interval;Based on the state interval mapped, the corresponding interval model in pre-constructed carbon emission variable structure disturbance response model is called;Wherein, variable structure disturbance response model is composed of multiple interval models associated with different state intervals, each interval model includes interval variable structure control law and interval disturbance response function;Interval model is fitted, and real-time disturbance marginal elasticity is calculated, and marginal carbon emission cost function is constructed according to real-time disturbance marginal elasticity;Real-time regulation of carbon emission quota is carried out through interval model and marginal carbon emission cost function.To solve the problem of marginal carbon emission cost function distortion.

This invention relates to the field of educational monitoring technology, and more particularly to a method and system for structural analysis of balanced combination intervals within a subject. The method includes the following steps: acquiring subject learning data and performing data standardization processing to obtain standardized subject learning data; performing interval structured modeling based on the standardized subject learning data to obtain a combined interval model; evaluating subject learning quality differences based on the combined interval model to obtain a hierarchical feature dataset; modeling the relationship between subject core competencies based on the standardized subject learning data to obtain a subject core competency relationship matrix; and evaluating subject learning quality based on the subject core competency relationship matrix and the hierarchical feature dataset, and optimizing learning paths to obtain subject optimization strategies. This invention can improve the targeting and accuracy of teaching quality monitoring.

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.

The invention discloses an unmanned aerial vehicle collision avoidance logic table optimization method based on residual reinforcement learning. The method comprises the following steps: establishing a typed safety interval model between an unmanned aerial vehicle and an encountered target aircraft; obtaining the relative state information of the unmanned aerial vehicle and the encountered target aircraft, and extracting a state vector containing the type characteristics of the invading aircraft; querying a pre-generated collision avoidance logic table based on basic state information in the state vector to obtain a safety baseline action; inputting the state vector into a residual reinforcement learning network, outputting a correction amount for a security baseline action, and fusing to generate a comprehensive action value function; and carrying out security verification on the fused actions, and if the fused actions violate security constraints, projecting the fused actions back to a security feasible region defined by a logic table, or eliminating unsafe actions through an action shielding mechanism. According to the invention, the self-adaptive optimization of the collision avoidance strategy of the unmanned aerial vehicle can be realized on the premise of keeping the safety priori constraint of the airborne table look-up collision avoidance system.