148 results about "Energy functional" patented technology

The invention relates to the field of image processing, in particular to a pin shaft forging forming quality detection method and system, and the method comprises the steps: carrying out the image collection of a to-be-detected pin shaft forge piece; then determining a total energy function of the active contour model and determining a neighborhood window, obtaining a structure tensor based on a gradient magnitude in the neighborhood window, and calculating to obtain local gradient direction dispersion; then, a defect edge structure enhancement index is calculated; then calculating a self-adaptive external energy scaling adjustment factor, and fusing the self-adaptive external energy scaling adjustment factor into an energy function of the active contour model to form an improved active contour model; and finally, accurately segmenting and extracting the surface defects of the pin shaft, and carrying out quality detection by combining the extracted defect characteristics. According to the method, by constructing local gradient direction dispersion and defect edge structure enhancement, a self-adaptive external energy scaling adjustment factor is calculated, and an active contour model is improved so as to accurately detect the surface defects of the pin shaft forge piece.

The invention relates to the technical field of detection, in particular to a complex shell detection method and system based on structured light scanning. The method comprises the following steps: calculating a wrapped phase value and a phase modulation amplitude of each pixel point under each frequency, calculating discrete probability distribution containing candidate stripe series and a corresponding probability for each pixel point based on a multi-frequency wrapped phase difference, generating an initial pixel confidence map, and constructing an energy function; the penalty weight of the smooth item is determined according to the phase modulation amplitude and the wrapped phase gradient local variance, obtaining an initial fringe order distribution diagram, updating discrete probability distribution by using an initial pixel confidence map, re-minimizing an energy function to obtain a corrected fringe order distribution diagram, and reconstructing a final three-dimensional shape. And iteratively optimizing a projector principal point and a radial distortion coefficient in system calibration parameters. According to the scheme, the geometric details of the surface can be reserved, and the processing effect and the measurement precision of the complex curved surface and the high-curvature area are improved.

The invention discloses a security boundary consistency control method for a multi-agent spatio-temporal dynamic system. The method specifically comprises the following technical steps: firstly, constructing a leader-follower spatio-temporal dynamic mathematical model of the multi-agent system based on a partial differential equation; then establishing an abnormal working condition mathematical model containing an actuator fault and a network spoofing attack; deducing to obtain an error dynamic system equation by defining a system consistency control target; constructing an energy function by using a Lyapunov stability theory, and deducing to obtain a system stability criterion; finally, a security boundary consistency control method is designed, and multi-agent space-time dynamic consistency control is achieved. The method effectively solves the problem of high cost caused by arrangement of actuators in a whole spatial domain in a traditional scheme and the problem that a multi-agent system is easily interfered by network attacks in a communication process, and has remarkable application value in engineering practice.

The invention discloses a VSG-DFIG subsynchronous oscillation adaptive suppression method based on a Hamiltonian model, and belongs to the technical field of subsynchronous oscillation suppression of a power system. The method aims at solving the problem that an existing method for suppressing subsynchronous oscillation existing in grid-connected operation of the doubly-fed induction generator is poor in suppression effect. Comprising the following steps: constructing a port controlled Hamiltonian dissipation model; constructing a closed-loop system dynamic equation based on interconnection and damping distribution under strict passivity; an expected energy function in a unit closed-loop form is constructed, a DFIG unit feedback control law based on a Hamiltonian model is obtained, and further deformation is carried out to obtain a deformed DFIG unit feedback control law; the port controlled Hamiltonian dissipation model is corrected, and a corrected port controlled Hamiltonian dissipation model is obtained; constructing a dynamic equation of the anti-interference adaptive observer, and establishing an error dynamic equation; and constructing a Lyapunov function, and designing an estimation error feedback control law according to the stability condition of the Lyapunov function. The subsynchronous oscillation suppression circuit is used for suppressing subsynchronous oscillation.

The invention discloses a high-speed rail canopy steel structure corrosion image recognition and evaluation method based on deep learning, and the method comprises the following steps: obtaining and calibrating a multi-view image, and organizing the multi-view image into an observation sequence; performing normalization and quality checking on the observation sequence, and extracting a state vector and an observation driving vector; constructing an energy function and a structure matrix in the Hamiltonian neural network, and promoting state vector time evolution to obtain an evolved corrosion state vector; inputting the evolution result into a decoder, generating a pixel-level corrosion mask and a boundary position, and obtaining a corrosion segmentation result; the corrosion type is judged based on the segmentation result, and corrosion severity indexes are calculated and graded; and extrapolating and predicting the severity index by using time evolution, outputting a predicted maintenance time window and priority, and finally generating a detection report. According to the method, the Hamiltonian neural network is introduced to realize identification, grading and evolution prediction of steel structure corrosion, and the detection stability and the engineering availability are improved.

The invention relates to the field of shared control, and discloses an intelligent shared control method and system of a plasma waterless washing machine, which are used for solving the core pain points of the traditional shared washing machine in the dimensions of prediction, energy consumption, maintenance and control. According to the method, through fusion of multi-model cooperative calculation and interdisciplinary theory, full-link optimization control is combined with an evolutionary game theory incentive mechanism and a Lyapunov energy function optimization algorithm, an equipment start-stop strategy and a user integral rule are dynamically generated, and energy consumption and service quality are balanced; and constructing a thermodynamic entropy increase optimization model to dynamically update an environment coupling coefficient and a weight parameter, and integrating meteorological and power grid data to form a self-adaptive negative feedback control ring. According to the invention, the limitation of traditional single-point optimization is broken through, the cleaning uniformity error is improved, the fault early warning response speed is accelerated, and the energy efficiency and reliability of the shared washing machine are remarkably improved.

The invention discloses a vibration solving method for an elliptical perforated plate in any shape based on an elliptical energy element, and belongs to the technical field of thin-wall structures, and the method comprises the steps: carrying out the overall mapping of the elliptical perforated plate in any shape, building an elliptical equation in a dimensionless coordinate system, and carrying out the Boolean operation modeling of geometry; constructing a boundary condition function simulation constraint; simulating board deformation according to a first-order shear deformation theory, and constructing a global trial function in combination with a Legendre polynomial and a boundary condition function; establishing a total energy functional based on a minimum potential energy principle and a Ritz method, solving a total energy functional stationary value, and obtaining element calculation formulas in the stiffness matrix and the mass matrix; and constructing an elliptical energy element based on local mapping, and performing numerical integration on each element of the matrix based on the elliptical energy element and completing vibration problem solving. According to the method, an energy functional and a numerical integration formula are both constructed on a square solution domain in an overall mapping stage, so that the calculation solution process of the elliptical perforated plate in any shape is completely standard.

The invention provides a transient stability analysis method for a grid-connected system. The method comprises the following steps: establishing a mathematical model of a heterogeneous new energy grid-connected system comprising a network construction type new energy unit, a network following type new energy unit, a synchronous generator and a receiving end power grid, and defining a state variable set of the system; solving a stable equilibrium point and an unstable equilibrium point of the system according to the operation condition of the system; and by taking the unstable equilibrium point as a starting point, calculating by adopting a numerical integration method or a numerical continuation method to obtain a stable manifold starting from the unstable equilibrium point. The method provided by the invention realizes global and accurate depiction of the transient stability domain, overcomes the defects that a traditional small signal linearization method can only analyze local stability and a traditional energy function method depends on an artificial construction function and is limited in applicability, fundamentally changes an analysis normal form of the transient stability domain, and improves the analysis accuracy of the transient stability domain. And a more effective analysis tool is provided for safe and stable operation of the high-permeability new energy grid-connected system.

The invention relates to the field of digital modeling, and particularly discloses a mine modeling method and system based on point cloud data, a terminal and a medium, and the method comprises the steps: obtaining original point cloud data of a target mine area; preprocessing the original point cloud data, including voxel filtering, to obtain target point cloud data; calculating the curvature and the normal vector of each point in the target point cloud data; inputting the coordinate, the curvature and the normal vector of each point as input values into a pre-trained geological category classification model to obtain the probability that each point belongs to various geological categories; constructing an energy function of a graph cut algorithm based on the probability that each point belongs to various geological categories and the point cloud features of the target point cloud data, and obtaining the final geological category to which each point belongs through the graph cut algorithm; and constructing a digital model of the target mine area based on the geological category to which each point belongs. According to the method, geological category prediction is carried out based on the multi-scale features of the point cloud, optimization is carried out in combination with the graph cut algorithm, and the classification efficiency and robustness are improved.

The invention relates to the field of energy management, and discloses a set generating capacity improvement and power grid optimization operation device based on intelligent scheduling, and the device comprises a data sensing module which is used for collecting the power grid state data of a set and carrying out the preprocessing of the data, and obtaining the processing data; the scale modeling module is used for constructing a unified mathematical model containing a fast variable and a slow variable by utilizing the processed data, describing a coupling relationship between the fast variable and the slow variable by adopting layered Hamiltonian, embedding power balance equality constraints to enable the power of the whole network to be constant, and introducing a Warisstein distance relaxation unit start-stop integer constraint to process a non-convex optimization problem, so as to obtain a scale model; and outputting the multi-objective optimization mathematical model containing the energy function and the constraint condition. Dynamic characteristics such as a rotor angle / angular velocity of a synchronous generator and scheduling decision variables such as unit start-stop, energy storage charge-discharge and the like are brought into the same energy framework by utilizing layered Hamiltonian, energy conservation of the whole network is ensured through power balance equality constraints, and meanwhile, Warisstein distance is introduced to relax unit start-stop integer constraints.

The invention discloses an event causal relationship identification-oriented energy contrast learning and knowledge verification method, and belongs to the technical field of deep learning and natural language processing. The invention provides a multi-stage knowledge verification system fusing multi-source external knowledge and large language model driven causal reasoning to solve the problems that an existing model is low in quality and prone to introduction of a pseudo causal positive example in comparison sample construction. Comprising a concept-level event heterogeneous graph construction module, a multi-level positive and negative sample pool construction module, a multi-source causal verification module, a lightweight model-driven distillation module, a large model-guided fine screening and sorting module and an energy contrast learning module. By introducing a stochastic gradient Langevin dynamics method, an antagonistic negative sample is dynamically generated, and a causal discrimination boundary between positive and negative samples is modeled through an energy function, so that the recognition capability of the model on the event causal relationship is improved.

The present invention relates to the field of image processing, and specifically to a pin forging quality inspection method and system, which collects images of the pin forging to be inspected; then determines the total energy function of the active contour model and determines the neighborhood window, obtains the structure tensor based on the gradient amplitude within the neighborhood window, and calculates the local gradient direction discreteness; then calculates the defect edge structure enhancement index; then calculates the adaptive external energy scaling adjustment factor and integrates it into the energy function of the active contour model to form an improved active contour model; finally, accurately segments and extracts the pin surface defects, and combines the extracted defect features for quality inspection. The present invention improves the active contour model by constructing the local gradient direction discreteness and defect edge structure enhancement, calculating the adaptive external energy scaling adjustment factor, and accurately detecting the pin forging surface defects.

The invention relates to the technical field of intelligent optimization design, in particular to a bridge prestressed steel beam intelligent optimization design method based on multi-factor coupling, and the method comprises the steps: carrying out the quantitative modeling of a plurality of parameters in the arrangement process of a bridge prestressed steel beam, achieving the precise expression of controllable variables, and improving the design precision of the bridge prestressed steel beam. The overall search capability of the arrangement scheme under multi-target tradeoff is enhanced, the rationality and stability of steel beam distribution are integrally improved, the structure local overload risk is reduced, an energy function sequence is constructed through indexes such as a midspan displacement value, a section stress ratio and the total length, the quality of a solution set is controlled by utilizing an energy difference value, intervention and screening of a residual value are introduced, and the stability of the structure is improved. The robustness of the design under the multivariable condition is enhanced, through logic judgment of displacement and counterforce, unqualified arrangement is eliminated, feasible arrangement output under structural consistency is achieved, structural efficiency and material use economy are enhanced, and design reliability, optimization solution efficiency and structural response control precision are remarkably improved.

The invention discloses a comprehensive simulation method for splitting failure of a Brazilian disk under dynamic and static actions. The method comprises the following steps: establishing a Brazilian disk finite element model and defining material parameters; performing dispersion crack treatment on the model through a phase field variable and a crack density function; selecting a branch path according to a load type (static / dynamic); the static branch constructs an energy functional and a displacement-phase field control equation based on an F-M variation principle, and the dynamic branch deduces a dynamic control equation through a Lagrange energy functional and a Hamiltonian principle; a displacement field and a phase field are updated through finite element discretization and Newton-Raphson iteration solution; cyclic loading is judged through convergence until the load is applied, and finally the crack propagation process and the mechanical response are visually output. According to the method, integrated simulation of dynamic and static loads is realized through a unified phase field dispersion framework, the problem of model incompatibility in a traditional method is solved, and the crack propagation prediction precision is remarkably improved.

The invention discloses an out-of-distribution sample detection method and system based on a self-adaptive distribution ball framework, and relates to the technical field of artificial intelligence and machine learning, and the method comprises the steps: building an out-of-distribution sample detection model based on the self-adaptive distribution ball framework; inputting the training data into a dynamic distribution boundary adjustment module, and generating an enhanced sample through an adversarial generative network to expand a distribution boundary; inputting the enhanced sample into a dynamic multi-scale feature fusion module, and fusing the global feature and the local feature based on the fusion weight to generate a fusion feature; and inputting the fusion features into a dynamic energy optimization module, calculating an energy value through an energy function, dynamically adjusting a classification threshold by using a closed-loop feedback mechanism, acting an energy loss gradient to a dynamic distribution boundary adjustment module and a dynamic multi-scale feature fusion module for parameter adjustment, and outputting an identification result. According to the invention, through combination of dynamic boundary adjustment, feature fusion and energy optimization, the accuracy and robustness of out-of-distribution detection are significantly improved.

The invention discloses a multi-source data fusion method and system based on prejudice recognition embedding, and relates to the technical field of artificial intelligence, and the method comprises the steps: adding noise through an ICEEMDAN algorithm, carrying out the decomposition through EMD, carrying out the significance estimation and separation signal extraction through a DSC model and an HEPSO optimization function, carrying out the dimension reduction through an auto-encoder, and carrying out the constraint through an optical flow field. The method comprises the following steps: performing multi-scale decomposition by using a Caputo-Kavigampola fractional derivative, calculating an adaptive diffusion kernel by shifting a Chebyshev polynomial, performing solution by using a convolutional quotient domain, and performing optimization by using a fractional gradient feature and an enhanced energy functional. According to the method, through prejudice recognition embedding and multi-scale fractional order decomposition, optical flow field constraint and sparse coding are combined, the precision and robustness of multi-source data fusion are improved, and efficient fusion, denoising processing and feature optimization of data are achieved.

The invention discloses a dual-active bridge converter control method and system based on double-layer weighted active disturbance rejection, and relates to the technical field of power electronics. The method comprises the following steps: constructing an input and output voltage dynamic model according to system characteristics of the dual-active bridge converter, and converting the input and output voltage dynamic model into a linear state space equation; constructing an energy function kinetic equation based on a transmission power balance relation according to the linear state space equation; constructing the energy function kinetic equation into a standard extended state-space equation according to an active-disturbance-rejection theory, and designing a double-layer observer according to the extended state-space equation; and according to the double-layer observer structure, a control law is constructed based on the state space model and the energy function, and the control law is used to solve the output voltage of the phase shift tracking dual-active bridge converter. According to the invention, quick response and high efficiency of the output voltage of the DAB converter can still be ensured under the coupling of noise and lumped interference.

A robot time-energy optimal smooth trajectory planning method is provided, an energy function is constructed by the square sum of path curvature rate, a cost function is constructed by the weighted sum of path energy and length, an optimization model is constructed by introducing path curvature peak and safety distance constraints, and the optimal path is searched under the environment of Delaunay triangulation. The initial path is re-optimized through deleting acute angle vertex, path replacement, removing redundant points, etc., is fitted by cubic NURBS, and is controlled by five-section S-shaped acceleration and deceleration motion under the Bang-Bang-Singular trajectory tangential jerk strategy, so that the path is smooth and collision-free, the trajectory satisfies the curvature peak and curvature rate constraints, and the time-energy is optimal. The robot is not simplified as a point, and the working environment and whether the obstacle is regular are not limited, the search of the path of any two-dimensional space containing obstacles can be processed, the smooth trajectory and fast motion are ensured, and the unified planning of geometry and motion is realized.

The present application relates to the technical field of distributed system testing and operation and maintenance monitoring, and particularly relates to a server test environment automatic monitoring method and system, comprising: collecting a micro-service cluster directed call topology structure, constructing a distributed cell layer with node and edge vector spaces; parsing data serialization rules to extract constraint restriction mapping matrices, assembling a Hodge-Dirac operator; obtaining real-time business state vector groups to splice into zero-order co-chains, using the operator to solve an algebraic Laplace matrix to calculate a quadratic form energy functional; when determining that the quadratic form energy functional is greater than zero, generating an early warning signal, constructing and solving a discrete layer diffusion dynamics equation based on the algebraic Laplace matrix, obtaining a state compensation update matrix; finally, analyzing the state compensation update matrix into reverse rollback and cache erasure instructions, and submitting the instructions to a service mesh data plane proxy component for execution. The present application realizes code-free cross-node semantic conflict quantification and distributed consistency state self-healing closed loop.

The invention belongs to the technical field of equivalent modeling, and discloses a hybrid wind power plant equivalent method and device based on oscillation energy decomposition, and the method comprises the steps: obtaining the operation measurement data of a hybrid wind power plant, carrying out the calculation or frequency scanning of each fan grid-connected branch, and obtaining a broadband equivalent impedance characteristic; constructing a multi-machine oscillation energy equivalent unit comprising a plurality of grid-connected branches according to the broadband equivalent impedance characteristic and the actual grid-connected topology of the wind power plant; establishing an energy function and a dynamic equation according to the multi-machine oscillation energy equivalent unit; performing modal decomposition and branch decomposition on the system oscillation energy, and calculating the oscillation energy contribution degree and the dissipated energy participation degree of each fan grid-connected branch in a dominant oscillation mode to obtain an energy characteristic quantity; performing fan grouping to obtain a fan grouping result of the hybrid wind power plant; and performing differential equivalence and parameter aggregation according to a fan grouping result to obtain a small disturbance equivalence model. The calculation model scale and the calculation complexity of the hybrid wind power plant can be reduced.

The invention belongs to the technical field of crowd counting, and particularly relates to a domain-oriented adaptive crowd counting energy-driven active learning method, which comprises the following steps of: extracting multi-scale visual features from an input image, and training HRNet to obtain an optimal source domain model; source domain and target domain samples are mapped to a unified energy space through an energy function; performing data enhancement on the target domain sample, and calculating uncertainty and a predicted people number mean value under various data enhancement modes; calculating sample energy of the target domain; screening the target domain samples twice by adopting an active learning strategy, and labeling the screened samples; designing energy alignment loss; and performing fine tuning on the optimal source domain model, and obtaining a final crowd counting result by using the fine-tuned model. According to the method, data distribution of the source domain and the target domain is effectively aligned through the energy model, sample labeling is carried out in combination with an active learning strategy, and the cross-domain crowd counting performance is improved.

The application discloses a LoG operator-based active contour model image segmentation method and system, and belongs to the technical field of computer vision, and the technical points are as follows: firstly, an edge fitting function EFF is used to replace a traditional local data fitting function; secondly, an energy functional model is created; finally, two new functions are introduced to serve as the influence of a distance regularization term and a length term, so as to prevent the level set function from being too flat or too stepped, and to smooth or shorten the curve, thereby realizing image segmentation. The LoG operator-based active contour model image segmentation method and system have the advantages that, compared with a segmentation evaluation database and a model of the prior art, the model has a faster segmentation speed, improved robustness to an initial contour, and strong anti-noise capability.

The invention relates to a stochastic dynamics system energy prediction method and device, electronic equipment and a medium. The method comprises the following steps: setting a displacement variable of the stochastic dynamics system about time, and establishing an energy function of the stochastic dynamics system according to the displacement variable; constructing a multi-scale data set according to the energy function; the initial model is trained through the multi-scale data set, and a feature extraction model is obtained after training is completed; the feature extraction model is used for extracting dynamic features of the stochastic dynamic system; the dynamic characteristics at least comprise an energy predicted value at the current moment; constructing a drift term according to the drift network and the dynamic characteristics, and constructing a diffusion term according to the diffusion network and the dynamic characteristics; and calculating an energy predicted value of the next moment based on the energy predicted value of the current moment, the drift term and the diffusion term. According to the method, based on the improved discretization incremental energy prediction algorithm, the discretization scheme conforming to the physical law is adopted to improve the energy prediction numerical stability and prediction precision.

The invention discloses an actuator capability and control rate explicitly coupled satellite attitude adjustment method, and belongs to the technical field of satellite attitude adjustment and control. The method comprises the following steps: firstly, establishing a satellite attitude kinematics and dynamics system, modeling an actuator dynamics system by adopting a hyperbolic tangent function, and widening an attitude control system with limited actuator capability into a three-order attitude control system; then designing an attitude anti-saturation regulation controller, taking the maximum control capability of an actuator as a parameter selection reference, and ensuring the asymptotic stability of the system through the Lyapunov theory; and finally, solving an inequality differential equation of the energy function, and establishing an explicit mathematical relationship between the actuator capability and the control rate. According to the method, the relevance between the actuator capacity and the control rate is revealed through an accurate mathematical equation, a theoretical basis is provided for satellite actuator type selection, and meanwhile, the attitude adjustment stability under the condition that the actuator capacity is limited is ensured.

The invention discloses a transient stability evaluation method and device for a multi-VSG parallel system. The method comprises the following steps: obtaining a power system model and a transient energy function according to the system; the transient energy function comprises a kinetic energy function and a potential energy function; obtaining a critical stable energy value according to the power system model and the transient energy function in combination with a BCU method; constructing a first model for obtaining a dynamic coupling relationship between VSGs based on a PINN network; training the first model according to a preset data set to obtain a second model; solving a transient energy function according to the real-time state vector of the system, and solving a path related item in the transient energy function in combination with a second model to obtain a real-time energy value; and if the real-time energy value is greater than the critical stable energy value, determining that the system is unstable, otherwise, determining that the system is stable.

This invention discloses a full-format model-free adaptive disturbance compensation control method for unmeasurable disturbances. The method establishes a dynamic linearized data model of the controlled object under unmeasurable disturbances; constructs and solves the cost function, optimizing and updating the pseudo-Jacobi input matrix and pseudo-Jacobi disturbance matrix; designs a full-format model-free adaptive disturbance compensation control scheme for unmeasurable disturbances; constructs and solves the energy function, optimizing and updating the full-format adaptive input matrix and full-format adaptive disturbance matrix; and uses the control scheme of this invention to control the controlled object under unmeasurable disturbances. The control method of this invention can significantly reduce the impact of unmeasurable disturbances on the actual output value of the controlled object system, achieve effective tracking of the desired value trajectory, and significantly improve the disturbance compensation control performance.

The invention provides a curvature weighted 3D-SIFT and adaptive radius FPFH-based point cloud registration method and system. The method comprises the steps of obtaining point cloud data and performing preprocessing; carrying out point cloud key point extraction based on curvature weighted 3D-SIFT to obtain corresponding key points; constructing a point cloud feature descriptor, and calculating the feature descriptor by using an improved FPFH fast point feature histogram algorithm; an attitude estimation scheme in an IGSP algorithm is adopted, firstly, an energy function is constructed, then the energy function is optimized through a KM algorithm to determine global optimal correspondence, and transformation is calculated on the basis; and finally, continuously repeating the process of optimal corresponding matching and transformation calculation through an iteration strategy to obtain a globally optimal solution. The method is combined with density characteristics, the description capability of complex scenes (such as vegetation and irregular terrains) is remarkably enhanced, and mismatching is reduced.

The present invention relates to a method for generating a DSM by optimizing a grid using a satellite stereo image pair. First, a rough original three-dimensional grid is generated from a multi-scene linear array satellite stereo image pair and used as input. Then, based on the RPC model representing the relationship between object points and image points, reprojection errors and Laplacian gradients are calculated, etc. Two energy terms, namely the photometric consistency energy term and the gradient smoothing energy term, are respectively constructed with these two calculated quantities as the main indicators to complete the construction of the energy function. The three-dimensional grid is continuously subdivided with an adaptive threshold. A method of discrete first and then optimization is used for the optimization solution process. This method avoids the problems of the size adaptability of the dense matching window and the implicit problem of the disparity consistency within the window in the traditional generation method, and has higher accuracy.

The present invention discloses a complex system fault location method based on the Ising model, comprising: corresponding each sensor in the complex system to a node in the Ising model, using spin variables to represent the sensor state; determining the interaction parameters in the Ising model based on the correlation between different sensors and the changing trend of sensor readings; defining an energy function, which includes interaction terms, external field terms, and terms related to sensor readings; defining a loss function; randomly initializing parameters; minimizing the loss function using an optimization algorithm and updating the parameters; iteratively updating the node state using the Monte Carlo method, updating the node spin state with a preset probability based on the current energy function and temperature parameters; and determining the sensor fault state based on the final node spin state. The present invention can achieve rapid fault location in complex systems.

The application relates to a configuration design method of a three-dimensional orbiting sail with posture stability, and belongs to the field of spacecraft attitude dynamics and structure design.The application is realized by the following method: considering the environmental perturbation of atmospheric resistance, atmospheric resistance moment and gravity gradient moment, an orbit attitude coupling dynamics model of a three-dimensional orbiting sail system is established based on a position vector and Euler angles; the three-dimensional orbiting sail and a spacecraft body are regarded as rigid bodies, and the windward area is considered to be blocked by airflow so as to more accurately describe the orbit movement and attitude movement of the system in the orbiting process; the attitude movement is simplified for the attitude dynamics of the system; the attitude stability of the system in the orbiting process is analyzed by using a phase plane method, a Lyapunov energy function method and other stability analysis methods of nonlinear dynamics systems; the relationship between important structure parameters such as a cone angle and a support rod length of the three-dimensional orbiting sail and the attitude stability is further constructed; and the three-dimensional orbiting sail satisfying the preset attitude stability requirement is obtained based on the relationship, so that the configuration design is realized.