721 results about "Linearization" patented technology

The invention relates to the technical field of environment perception, and discloses an optimization method and device for a multi-climate self-adaptive environment perception cooling fan system, and the method comprises the steps: collecting multi-point temperature, humidity and air pressure data, and carrying out the filtering processing, and obtaining an environment state vector; executing mathematical modeling of the multi-degree-of-freedom cooling system according to the environment state vector to obtain a linearized state space model; performing temperature gradient estimation and humidity compensation based on the linearized state space model to obtain an adaptive control law; inputting the adaptive control law into a model-free adaptive prediction controller to obtain a rolling optimization control sequence; weight self-adaptive adjustment and heat dissipation efficiency hierarchical prediction control are carried out on the rolling optimization control sequence based on climate conditions, fan rotating speed control and wind direction adjustment execution signals are generated, the problem that a traditional heat dissipation system is not sensitive to space temperature distribution and humidity changes is solved, and the heat dissipation control precision under different humidity conditions is improved.

The invention discloses a power distribution area flexible interconnection group planning method for mutual energy assistance, and relates to the technical field of power distribution network planning, and the method comprises the steps: screening out area pairs meeting conditions based on the topological parameters of a power distribution network, area loads and new energy output data, and constructing an initial interconnection set; constructing an initial two-stage opportunity constraint optimization model comprising a planning stage and an operation stage based on the set; in the planning stage, an interconnection strategy and a new energy consumption rate are determined by taking minimization of investment cost and punishment cost as a target; the strategy feasibility is verified through a random scene in the operation stage; carrying out polyhedral approximate linearization processing on nonlinear constraints in the model, reconstructing a power flow model into a compact form, converting probabilistic opportunity constraints into deterministic bilinear constraints by using a sampling average approximation method, forming a deterministic approximation model, and carrying out iterative solution through a column constraint generation decomposition algorithm to obtain a target interconnection grouping scheme; and high-efficiency energy mutual aid and load balancing between power distribution stations are realized.

The invention relates to the technical field of ocean renewable energy power generation, and particularly discloses a floating type wind wave combined power generation system model prediction control method, which comprises the following steps: establishing a nonlinear mathematical model of a floating type wind wave combined power generation system; determining a steady-state working condition point of the system; carrying out linear processing on the nonlinear model; constructing a discrete time prediction model; designing a target function; defining a state and an input constraint condition; performing real-time rolling optimization solution; and dynamically feeding back a correction mechanism. Starting from a wind wave excitation mechanism, on one hand, response in a turbulent wind frequency band is reduced through fan variable pitch control, and on the other hand, response in a wave frequency band is reduced through wave energy power generation device PTO control, so that tower footing load is effectively reduced, and pitching motion of a platform is restrained. Global multi-objective optimization is realized by designing an objective function, defining constraint conditions and solving the objective function.

The invention discloses a GNSS (Global Navigation Satellite System) / INS (Inertial Navigation Satellite System) anti-deception navigation method based on ESKF (Enhanced Shift Keying Function) and dynamic window detection, which comprises the following steps of: 1, acquiring observation data of an INS and a GNSS, performing time synchronization and coordinate system unification on the observation data of the INS and the GNSS, and respectively eliminating a time error and a coordinate difference; 2, a dynamic window detection mechanism is designed, an error state dynamical model is constructed, the dynamic window detection mechanism is used for detecting whether GNSS cheating exists or not, and meanwhile the error state dynamical model can output stable navigation parameters; and 3, outputting a final high-precision navigation result according to the stable navigation parameters. According to the method, the problems of error accumulation and covariance distortion of nonlinear system model linearization are effectively solved, pollution of GNSS deception signals can be effectively inhibited, high-precision positioning and anti-deception capability are kept in complex environments (such as urban shielding and electromagnetic interference), and a navigation system with high precision and high reliability is formed.

The invention relates to the technical field of power system automation, in particular to a climbing-frequency modulation coordinated optimization method and system considering energy storage capacity uncertainty, and the method comprises the steps: building an opportunity constraint model of an energy storage system based on the capacity uncertainty, and enabling the opportunity constraint model to be used for describing a reliable feasible region under the deviation of energy storage electric quantity; establishing an energy storage system linearization model considering a climbing-frequency modulation power unified constraint, and realizing power coupling and sharing constraint description of two types of adjustment services; aiming at the nonlinear characteristic of the gradeability of the thermal power generating unit, establishing a piecewise linearization model of the gradeability by adopting a piecewise linearization method; and establishing a thermal power-energy storage combined climbing-frequency modulation coordinated optimization model, and solving the optimization model by adopting a quadratic programming method to obtain the climbing power and frequency modulation reserve capacity of the thermal power generating unit and energy storage. According to the method, the actual available range of the energy storage system capacity can be dynamically reflected, the electric quantity out-of-limit risk caused by the uncertainty of the energy storage capacity is avoided, and the reliability of the scheduling result is improved.

The invention provides a bridge pier nonlinear earthquake random response analysis method based on a BWBN model, and belongs to the field of civil engineering structure random vibration analys.The method comprises the steps that discretization processing is conducted on a concrete bridge pier structure, and a finite element nonlinear analysis numerical model is constructed; constructing a BWBN model by combining linear and nonlinear differential equations, setting a hysteresis parameter value of the BWBN model, simulating by adopting an Euler-Bernoulli beam unit, and establishing a degraded beam finite element model; obtaining a unit hysteresis vector, constructing a system motion balance equation, solving an equivalent linearization coefficient, constructing a covariance matrix of random response of the concrete pier structure, performing random response analysis on the concrete pier structure, and exploring typical working conditions of random excitation. According to the method, the operation time is shortened, the calculation precision is improved, and meanwhile, the response characteristics of the reinforced concrete pier structure under nonlinear earthquake random excitation can be truly represented.

The invention relates to the technical field of crane control, in particular to a method and system for achieving active control over cargo swing of a bridge crane, and the method comprises the steps that the trolley speed, the trolley acceleration, the cart speed, the cart acceleration, the cargo mass, the trolley mass and the lifting rope length are input into a pre-established cargo swing dynamic model; the method comprises the following steps of: firstly, carrying out conversion to obtain a linearized model, then carrying out pull-type transformation on the linearized model to obtain a standard transfer function of goods in an X-axis direction, and then predicting the acceleration of a trolley at a next time node when a goods swing angle is within a swing threshold value by adopting an integrated function of a preset controller, a preset input shaper and the standard transfer function. And finally, a driving instruction for predicting acceleration motion is sent to the trolley. Compared with the prior art, the method can effectively restrain goods from swinging in the process that the trolley carries the goods to move.

The invention discloses a multi-satellite cooperative distributed game control method and device, a computer program and a storage medium, belongs to the technical field of spaceflight, and particularly relates to a satellite attitude control and game control method. The problem that in the prior art, resolving of the overall control quantity depends on a center unit, and constraint processing on the size of the microsatellite control torque is not accurate enough is solved. The method comprises the following steps: S1, according to attitude dynamics modeling and pseudo-linearization processing of the combined spacecraft, obtaining a state dependence coefficient model; s2, obtaining a local optimal control strategy of each satellite in the combined spacecraft through a model prediction control method according to the state dependence coefficient model in combination with a dynamic model in a prediction time domain and flywheel constraint conditions; and S3, obtaining a distributed Nash equilibrium solution through a distributed iteration and global coordination method according to the local optimal control strategy of each satellite in combination with communication topology information. The method is suitable for efficient cooperation tasks of large-scale satellite groups and microsatellite systems.

The invention relates to a vehicle and unmanned aerial vehicle combined scheduling method for wide-range low-cost inspection. The method comprises the following steps: acquiring prior information; modeling the unmanned aerial vehicle inspection problem of each target area according to the prior information to obtain a mixed integer non-convex optimization problem with the goal of minimizing the weighted sum of the total execution time and the energy consumption of all the inspection unmanned aerial vehicles; performing linearization on a non-convex bilinear term in the mixed integer non-convex optimization problem, and performing discretization processing on a nonlinear function by adopting piecewise linear approximation; an approximate mixed integer linear programming problem is obtained and solved, and an unmanned aerial vehicle scheduling strategy is obtained; modeling according to the unmanned aerial vehicle scheduling strategy and the prior information to obtain an inspection vehicle path planning problem taking the comprehensive driving cost as a target; the routing inspection vehicle path planning problem is converted and modeled into a Markov decision process, a routing inspection vehicle is used as an intelligent agent, a state, an action and a reward function are defined, and a routing inspection vehicle scheduling strategy is obtained. Therefore, combined inspection of the inspection vehicle and the unmanned aerial vehicle is realized, and the inspection range is expanded.

The invention provides a power system economic dispatching method considering dynamic capacity increase of a line and a related device. The method comprises the following steps: establishing a target function taking the minimum unit operation cost of a power system as a target; determining constraint conditions of the target function according to the parameters of the power system, wherein the constraint conditions comprise unit constraint, network variable constraint, linearized alternating current power flow constraint and linearized line thermal stability constraint; the economic dispatching model of the power system is solved, an economic dispatching scheme of the power system is obtained, and the economic dispatching model of the power system comprises a target function and constraint conditions. According to the method, the temperature change of the line is tracked by using the linearized line thermal stability constraint, so that the transmission capacity of the line can be improved on the basis of meeting the thermal stability constraint, an economic dispatching strategy is optimized, and the economic dispatching efficiency is improved by linearizing the alternating current power flow constraint and the line thermal stability constraint. The power system economic dispatching model is constructed into a mixed integer linear programming model, so that the solving speed and efficiency of the model can be improved.

The invention discloses a rope-driven mechanical arm motion control method based on model predictive control, which comprises the following steps: taking a joint angle error as a state variable, taking a rope-driven rotation angle as an input variable, and establishing a nonlinear state-space equation of a rope-driven mechanical arm according to the state variable and the input variable; the nonlinear state-space equation is subjected to linearization processing, a linear approximation model is generated, and the linear approximation model comprises a linearization matrix; defining a prediction interval, expanding the linear approximation model into a multi-step prediction form, and generating a prediction model; constructing a cost function, and combining the prediction model into the cost function to solve and obtain an optimal control sequence; and extracting the optimal control increment at the current moment from the optimal control sequence, and updating the state variable, the input variable and the linearization matrix according to the optimal control increment to form closed-loop control. Accurate path tracking control can be carried out on the rope-driven mechanical arm under complex dynamic constraints, and it is ensured that the mechanical arm moves according to the planned trajectory.

The invention discloses an application method of a deep learning model embedded based on a physical mechanism in urban flood ponding point identification. The method comprises the following steps: collecting and preprocessing basic data of a research area; taking the preprocessed basic data as input, and performing deformation processing, discretization and linearization processing by taking a Saint-Venant equation set as a hydrodynamic equation set; constructing a differentiable physical module based on the discretized deformation hydrodynamic equation set; on the basis of a differentiable physical module, a deep learning model is constructed, and the deep learning model comprises a backbone network, the differentiable physical module, an attention guidance fusion module, an iteration correction mechanism and a mixed loss function; pre-training the deep learning model, combining fine tuning training and constraint strengthening training, and adding adversarial training in the training process to simulate an extreme scene; and urban flood ponding point identification is carried out based on the trained deep learning model. According to the invention, high-precision real-time prediction of urban flood simulation is realized.

The invention provides a dexterous hand dynamics modeling method and equipment. The method comprises the following steps: constructing a data set according to historical state data of the dexterous hand at multiple moments and corresponding historical moments; constructing a linearization model of the dexterous hand according to the dimension raising function; based on the data set, training the linearization model according to a preset loss function to obtain a kinetic model of the dexterous hand; the preset loss function comprises an evolution error term, a reconstruction error term, a controllability discrimination term and a stability constraint term; the evolution error term is used for representing an error between a dimension raising state and a state evolved by a Kupman operator, the reconstruction error term is used for representing an error generated by comparing a result obtained by raising dimension and then reducing dimension of the state with the state before dimension raising, and the controllability judgment term is used for representing a controllability condition of the linearization model; the stability constraint term is used for representing the stability condition of the linearization model; according to the method provided by the invention, the modeling precision and universality can be improved.

The invention discloses a reweighted inversion method and system for a magnetic anomaly modulus under a residual magnetism condition and a storage medium, and belongs to the technical field of magnetic exploration, and the method comprises the following steps: converting the magnetic anomaly modulus to obtain the magnetic anomaly modulus; carrying out nonlinear forward modeling on the magnetic anomaly modulus; and linearizing a forward modeling formula of the magnetic anomaly modulus, constructing a reweighted minimum model objective function, solving the reweighted minimum model objective function, finally obtaining the magnetization intensity of an inversion space, and delineating a target geologic body. According to the scheme, the nonlinear forward modeling formula of the magnetic anomaly modulus is subjected to linear representation, and the sensitivity matrix serving as depth weighting is further updated by utilizing the magnetic anomaly modulus kernel function matrix updated along with iteration, so that the reweighting minimum model objective function is constructed, the precision of magnetic anomaly modulus inversion under the residual magnetism condition is improved, and the accuracy of the magnetic anomaly modulus inversion under the residual magnetism condition is improved. And a more reliable inversion result is provided for more accurate depiction of a deep magnetic structure, mineral resource exploration and the like.

The invention discloses a power distribution network topology identification method and device, and the method comprises the steps: obtaining the original measurement information of a power distribution network; based on the original measurement information, constructing a mixed integer nonlinear programming topology measurement identification model based on a branch current method; utilizing the target data physical fusion driving linearization model to perform linearization processing on a nonlinear constraint and measurement equation to be used in the topology measurement identification model of the mixed integer nonlinear programming based on the branch current method to obtain a processing result; and constructing a topology measurement identification model of mixed integer linearization programming based on a branch current method by using a processing result so as to output a topology identification result of the power distribution network. Therefore, the problems that in the related technology, the efficiency of maintaining the topology file based on a manual and data statistics method is low, a topology error identification model based on an optimization class method is poor in adaptability and low in accuracy, the accuracy of topology identification is reduced, and the operation stability of a power distribution network is reduced are solved.

The invention discloses a vehicle dynamics Kupman modeling and predictive stability expansion control method, and belongs to the technical field of vehicle dynamics modeling and safety technology control. The invention aims to provide a vehicle dynamics Kupman modeling and predictive stability expansion control method designed by combining a model predictive control method based on a closed-loop data promoted Kupman operator modeling method. According to the method, firstly, a linear two-degree-of-freedom reference model of the vehicle is designed, then a nonlinear closed-loop control system is established, Kupman lifting is carried out, a high-dimensional observation function is constructed, and finally, a linear MPC controller is designed. According to the method, linearization of vehicle dynamics is achieved through the Kupman model, the calculation complexity is remarkably reduced, the real-time performance of the controller is greatly improved, and a control system can rapidly respond under the dynamic working condition.

A robot kinetic parameter identification method based on double-layer iteration and friction compensation comprises the following steps: S1, establishing a kinetic model of a robot, and performing linearization processing on the kinetic model to obtain a linearization model represented by an observation matrix and an inertial parameter vector; s2, designing an excitation trajectory for the linearized model by adopting improved Fourier series of a quintic polynomial, and setting constraint conditions of joint positions, speeds and accelerated speeds; s3, the robot is controlled to move according to the excitation track, and joint state data of the robot are collected and subjected to noise reduction processing; and defining the noise-reduced driving torque as a measurement torque. According to the robot kinetic parameter identification method based on double-layer iteration and friction compensation, the physical feasibility of the robot kinetic parameters can be ensured, the friction model is improved to identify the friction parameters, the friction model is fitted by adopting the radial basis function neural network, and the precision of subsequent robot control is ensured.

The invention relates to the technical field of underwater robot cooperative control, and provides an elastic consistency control method and system of a multi-underwater robot system. The elastic consistency control method for the multi-AUV system comprises the following steps: discretizing and linearizing the multi-AUV system, introducing a performance index to describe the influence of communication topology on the dynamic behavior of the system, and calculating the elastic consistency of the multi-AUV system on the basis of a centralized optimal control method of a local decoupling system and a robust stability theory. And designing an elastic consistency controller and obtaining controller gain by solving a local algebraic Riccati equation so as to realize consistency control of multiple AUV system states. According to a sparse mode matrix spectrum of a distributed controller, a system stability condition is given, and efficient cooperation and stable operation of a multi-AUV system in a complex underwater environment are ensured. According to the method, the calculation and communication complexity is remarkably reduced, the method is suitable for a multi-AUV cooperative working environment, and a new technical solution is provided for cooperative control of the underwater unmanned system.

The invention belongs to the field of shield tunneling machine deviation rectification track planning, and discloses a shield tunneling machine deviation rectification track high-precision autonomous planning method and system, and the method comprises the steps: determining the minimum turning radius of a shield tunneling machine; carrying out coordinate calculation on the DTA to obtain the coordinate, the direction and the curvature of each point; projecting the starting point state and the end point state of the shield tunneling machine under the Cartesian coordinate system into the established Frenet coordinate system, establishing a correction curve parameterization equation based on a quintic polynomial line type, and converting the state of the shield tunneling machine into the Cartesian coordinate system; through segment pre-typesetting constraint, determining a deviation correction curve end point feasible region; the deviation degree of the deviation correction curve and the DTA and the length and smoothness of the deviation correction curve serve as optimization parameters, normalization processing is carried out according to the threshold value of each target, a comprehensive cost function is established, an improved particle swarm optimization algorithm is used for solving, and a global optimal deviation correction track is output. According to the method, real-time autonomous high-precision deviation rectification track planning of the shield tunneling machine can be realized, the track feasibility is improved, the pose deviation degree is reduced, and the tunnel quality and the construction efficiency are improved.

The invention provides a finite time cooperative tracking control method based on an adaptive RBF neural network, and belongs to the technical field of networked multi-agent control strategies. Constructing a dynamic mathematical model of a high-order all-drive uncertain nonlinear multi-agent system comprising a virtual leader and a follower; according to a cooperative tracking control target and uncertain items in the system, constructing an error dynamic system comprising an adaptive RBF neural network estimation compensation item; designing a finite time integral sliding mode surface based on a high-order all-drive system theory; designing linearization parameters of a finite time integral sliding mode surface based on a pole assignment method according to system dynamic performance and finite time convergence requirements, and obtaining a finite time integral sliding mode controller; and designing a self-adaptive updating law of an upper bound of an RBF neural network weight and an approximate error, and substituting the network weight and the approximate error into a finite time integral sliding mode controller to realize cooperative tracking control of finite time.

The invention discloses a fault detection method and a fault detection device for an accelerometer of a downhole tool. Taking an accelerometer and a gyroscope as state quantities, establishing a generalized nonlinear system for the downhole tool sensor model, and performing linearization processing to obtain a generalized linear variable parameter system; based on a T-N-L observer architecture, constructing a system state estimation error equation and a residual error generation equation, establishing a fault sensitivity and interference robustness evaluation system on the basis of the system state estimation error equation and the residual error generation equation, and determining observer parameters; and the sensor obtains a system state value, an estimated value is obtained through the observer, a residual value of a current system state quantity is calculated to judge an occurrence event, and a threshold value of sensor fault occurrence under the current event is calculated to judge a fault occurrence condition. A nonlinear model is adopted, the system dynamic state is described more accurately, the model is linearized, it is guaranteed that the model and the actual working condition have the high matching degree, and meanwhile a corresponding linear variable parameter system is obtained to reduce the calculated amount.

The ELM-based frequency safety constraint linearization optimization scheduling method comprises the following steps: establishing a fire-wind-light multi-energy complementary frequency dynamic response model, and deducing a time domain analytical expression of a frequency safety index by taking a frequency maximum change rate and a frequency maximum deviation as the frequency safety index of a system; considering a frequency lowest point security constraint and a frequency maximum change rate security constraint, and establishing a frequency security constraint-containing optimization scheduling model; a maximum bearable disturbance power linearization mapping method based on ELM is provided, nonlinear frequency security constraints are converted into a series of linear constraint sets, and linearization processing of the frequency security constraints is achieved; and piecewise linearization is carried out on quadratic terms in the established objective function containing the frequency security constraint optimization scheduling model, the overall model is converted into a mixed integer linear model, and a Gurobi solver is used to solve the linearly processed model. The scheduling method can ensure that the system frequency is safe and stable, so that the established optimal scheduling model gives consideration to both economy and frequency safety.

The invention relates to the technical field of power system operation and control, and discloses an improved model-free adaptive flexible low-frequency power transmission oscillation suppression method, which performs zero-phase lag filtering on noisy signals through a nonlinear tracking differentiator, effectively solves the problem of control divergence caused by signal noise, and improves the suppression efficiency. A tight-format dynamic linearization and linear expansion state observer technology is adopted, accurate capture of dynamic characteristics of a strong nonlinear system and real-time observation and compensation of internal and external full-dimensional disturbance are achieved without dependence of a physical model, and particularly, an adaptive estimation algorithm of an output variable quantity penalty factor is introduced. The problem that model parameter identification jumps severely under the broadband oscillation working condition is solved, the robustness of a control system is enhanced, and the self-adaptive optimality of a control strategy is guaranteed and interference of long-term online operation on steady-state performance is avoided by combining parameter optimization of a cuckoo search algorithm and an event triggering mechanism of time-frequency double criteria.

The invention relates to the technical field of aircraft path planning, and particularly provides an aircraft path planning method for a space non-cooperative target. The method comprises the following steps: constructing an aircraft dynamics model and constraint conditions, and obtaining a non-convex optimal control problem; performing discretization processing on a non-convex optimal control problem through a non-equal-interval pseudo-spectrum discrete optimization model, and converting the non-convex optimal control problem into a fixed-time nonlinear programming problem about discrete nodes; according to the method, a fixed time nonlinear programming problem is convexed to form a convex sub problem, a local optimal solution of a non-convex optimal control problem is obtained through sequence iteration, under the multi-state constraint, path planning of an aircraft approaching a space non-cooperative target is achieved, the problem of no feasible solution caused by non-accurate linearization is solved, and the method is suitable for being applied to a non-cooperative target. And the convergence rate of the algorithm is accelerated.

The invention discloses a large-scale wind turbine group power control method and system under a full-wind-domain working condition. The method comprises the steps that wind power plant parameters and wind turbine generator set parameters under the full-wind-domain working condition are collected; according to the parameters, establishing a wind power utilization evaluation coefficient linearization model, a wind turbine generator kinetic energy storage evaluation coefficient linearization model and a converter operation risk evaluation coefficient linearization model; constructing a whole-wind-domain wind power plant power controller based on each linearization model: in a low-wind-speed interval, optimizing the output power of a wind turbine generator to improve the wind energy capture efficiency and reduce the terminal voltage fluctuation; and in a high wind speed interval, the terminal voltage deviation and the operation risk coefficient of the converter are reduced by optimizing the output power and the flux weakening current of the wind turbine generator. The method can effectively improve the wind energy capturing capability in a low-wind-speed interval, reduce the terminal voltage deviation and the converter operation risk coefficient in a high-wind-speed interval, guarantee the operation stability of the whole-wind-domain wind power plant, and achieve the optimal power control of the whole-wind-domain wind power plant.

The invention discloses a non-parametric probabilistic load flow calculation method considering complex uncertainty for a power system. The method comprises the following steps: firstly, constructing source-load nonparametric probability distribution based on a multivariate Gaussian mixture model; then, constructing an alternating current power flow model under a polar coordinate system in the power system, and performing linear expansion on the alternating current power flow model under the polar coordinate system in the power system at a mean point of source-load non-parametric probability distribution by adopting a first-order Taylor series approximation method to obtain a system operation simplified model based on alternating current power flow linearization of the power system; and finally, according to the linear transformation property of the Gaussian distribution, obtaining the probability distribution of the power flow distribution of the power system under each Gaussian component, and in combination with the total probability law, carrying out weighted summation on the probability distribution under each Gaussian component according to the weight to obtain the non-parametric probabilistic power flow probability distribution of the power system. The method provided by the invention has stronger generalization ability and accuracy, and has important reference significance for operation uncertainty quantification of the power system under complex uncertainty.

The invention discloses an aircraft active side lever force feedback control method based on feedback linearization. The method is suitable for pitch axial force feedback control and roll axial force feedback control of an aircraft active side lever. The method comprises the following steps: establishing a kinetic equation of a mechanical transmission system and a pitch axis motor or a roll axis motor, and establishing a state-space equation of an active side lever system according to the equation; designing a linear expansion observer, and estimating the thrust applied to the active side lever by the driver based on the linear expansion observer; and designing a feedback linearization control method, deducing a control law according to a state-space equation of the active side lever system, and introducing the estimated thrust applied to the active side lever by the driver into the control law to realize real-time compensation of external interference. Under the condition that the thrust of the driver cannot be measured, the thrust of the driver is obtained through the linear expansion observer, the thrust of the driver is compensated by designing the control law, and therefore the dynamic performance and the steady-state performance of the servo system are improved.

The invention relates to the technical field of bridge safety monitoring, and particularly discloses a cable-stayed bridge cable force unbalance vortex vibration early warning system and method based on an intelligent calculation model, and the system comprises a multi-source data collection module, a phase-space reconstruction module, a vortex vibration mode decoupling module, a dynamic threshold setting module, a nonlinear prediction module and an early warning decision module. The vibration signals are mapped to a multi-dimensional phase space according to the phase space reconstruction theory, and multi-order frequency band vortex vibration modal decoupling is achieved in combination with density clustering analysis; establishing a multi-dimensional threshold hypersurface by adopting a support vector machine, and dynamically adjusting a judgment threshold according to the environmental parameters and the structural state; short-term high-precision prediction and medium-and-long-term trend prediction are realized by using a local linearization prediction model; a wind-vortex vibration mapping relation is established through the generative adversarial network; the self-adaptive filtering technology is adopted to eliminate traffic load interference, and the problems that in the prior art, cable force unbalance vortex vibration cannot be accurately recognized, early warning is not timely, and the false alarm rate is high are effectively solved.

The invention discloses a deep neural network probabilistic load flow calculation method and system, and belongs to the field of power system analysis. The method comprises the following steps: acquiring power system parameters and confidence levels, and determining state variables and orders; establishing and linearizing a nonlinear power flow equation to obtain a Jacobian matrix; a node voltage vector is used as input, branch admittance is used as physical information to be embedded into a deep neural network model based on a multi-head self-attention mechanism and semi-invariant guidance, a Jacobian matrix is solved, and a sensitivity matrix is calculated; calculating and aggregating semi-invariants, and transmitting the semi-invariants to a state variable; and after standardization, obtaining a distribution function by using a six-order Cornish-Fisher series, and calculating a fluctuation interval in combination with a confidence level under opportunity constraint to realize quantitative evaluation. According to the method, the probability load flow calculation complexity is reduced, the limitation of a traditional method in processing the network topology complexity problem is solved, and quantitative evaluation of the probability load flow is realized.

The invention discloses a mixed analytic-numerical method for solving soil-structure nonlinear interaction, and belongs to the technical field of geotechnical engineering and structural engineering. According to the method, a soil-structure nonlinear resistance curve is subjected to quasi-linearization, and numerical iteration is carried out by utilizing a matrix analytic solution of a multi-section elastic foundation beam theory, so that the internal force and displacement response of the structure is solved. The hybrid analytic-numerical method for solving soil-structure nonlinear interaction provided by the invention can truly simulate a mechanical behavior path of nonlinear resistance without depending on a traditional difference format. According to the method, absolute stability is kept in the solving process, better precision is achieved under the same calculation efficiency, meanwhile, precision improvement is based on fine adjustment of a small number of key variables, and the transparency and interpretability of the algorithm are guaranteed.