109 results about "Energy functional" patented technology

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 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 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 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 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.

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 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.

The present application relates to the field of additive manufacturing, and specifically discloses a kind of generation method of manufacturing three-dimensional Chinese character cup model based on energy driving.The present application constructs the smooth time evolution path from multiple outline initial region to single target outline, adopts level set function to describe the outline evolution process, and establishes energy functional including curve length regularization term, adaptive inflation term and shape prior term;Through introducing the dynamic coupling control mechanism based on area difference of region, shape error and boundary length, the adaptive adjustment of inflation coefficient is realized;Then the level set evolution equation is obtained by variational derivation, and after numerical discrete solution, two-dimensional evolution sequence is added to three-dimensional volume field along time axis;Finally, through isosurface extraction and thick shell processing, the 3D printable three-dimensional Chinese character cup model with complete structure and uniform wall thickness is generated.

The invention relates to the technical field of hovercar stability control, discloses a distributed driving hovercar air-ground switching stability boundary judgment method based on a Lyapunov energy function, and aims to evaluate the stability of a hovercar in real time and optimize the flight control of the hovercar. Comprising the following steps: establishing a distributed driving multi-degree-of-freedom nonlinear kinetic model of the hovercar, and deducing a three-dimensional Lyapunov exponential function framework based on the model; a physical information neural network is constructed, and the future state of the distributed driving hovercar is obtained; evaluating a stability boundary according to the three-dimensional Lyapunov index, monitoring a flight state in real time, and triggering an alarm when the boundary approaches; environment and vehicle state data are obtained in real time through a multi-source sensor, Lyapunov exponent parameters are automatically adjusted in combination with dynamic Bayesian optimization, and the stability boundary is corrected. According to the invention, millisecond-level monitoring and early warning can be realized, and a reliable basis is provided for control instruction generation.

Method for determining energy states of a fermionic system using a hybrid quantum-classical computer system, wherein the method comprises the steps: parameterization of a quantum circuit / approach for preparing the energy state of the fermionic system by the classical computer system with parameters θ j with j = 1,...,N, where N denotes the number of parameters of the approach; determining at least four energy values ​​for the at least one chosen parameter θ j by the quantum computer; determining an analytical energy function for at least one chosen parameter θ j from the at least four energy values ​​by the classical computer, where the analytical energy function is a second-order Fourier series; and determining the global minimum of the analytical energy function by the classical computer and assigning the minimum to the at least one chosen parameter θj The steps of the procedure are repeated until convergence is achieved.

One embodiment sets forth a technique for creating a generative model. The technique includes generating a trained generative model with a first component that converts data points in the training dataset into latent variable values, a second component that learns a distribution of the latent variable values, and a third component that converts the latent variable values into output distributions. The technique also includes training an energy-based model to learn an energy function based on values sampled from a first distribution associated with the training dataset and values sampled from a second distribution during operation of the trained generative model. The technique further includes creating a joint model that includes one or more portions of the trained generative model and the energy-based model, and that applies energy values from the energy-based model to samples from the second distribution to produce additional values used to generate a new data point.

Provided is a solution system capable of obtaining the optimal state of individual spins with a small amount of memory. The matrix simplification means 93 changes a matrix used in an energy function in a model representing states of individual spins by a first value or a second value into simplified form. In this case, the matrix simplification means 93 deletes elements that have a value of “0” from the matrix, and also deletes some elements that do not have the value of “0” from the matrix, thereby to change the matrix into the simplified form. The spin state derivation means 94 derives the optimal state of each spin based on the matrix changed into the simplified form.

A system and method for variational annealing to solve financial optimization problems is provided. The financial optimization problem is encoded as objective function represented in terms of an energy function. An autoregressive neural network is trained to minimize the cost function via variational emulation of classical or quantum annealing. Optimal solutions to the financial optimization problem are obtained after a stopping criterion is set. An optimal solution may be selected according to user defined metrics, and optionally applied to a real-world system associated with the financial optimization problem.

The invention discloses a three-dimensional registration method and device, a computer storage medium and a terminal. According to the embodiment of the invention, for a scene comprising a plurality of objects to be processed and images to be processed collected from more than two viewing angles of AR, residual errors still existing in the pose of a registered target object after rotation pose deviation correction are corrected, and the accuracy of the registration is improved. Performing contour projection on the rotation attitude correction pose on the to-be-processed image, searching to obtain candidate edge points, determining a residual error and a Jacobian matrix of the candidate edge points according to an energy function, expanding contour search to a cross-view angle and a constraint object, and feeding back an observation error on the constraint object to correct the rotation attitude correction pose. The amplification of the rotation error is inhibited; by introducing a normalization equation, solving of pose correction and consistency correction of pose residual errors of all objects to be processed are achieved; and performing pose correction on the registered target object according to the pose correction amount reaching the iteration termination condition, and realizing three-dimensional registration of the scene based on the registered target object without the pose residual error.

The present invention discloses a transient stability assessment method, apparatus, terminal equipment and computer-readable storage medium for a grid-connected system of a new energy generator set and a grid-connected converter. The method comprises: obtaining system parameters after a fault occurs, and calculating the energy value of each time step after the fault occurs by using a dynamic equation and an approximate energy function; judging whether the energy value of each time step is less than a preset critical energy value, and if so, judging that the corresponding time step is in a stable state, and if not, judging that the corresponding time step is in an unstable state; taking the maximum time step corresponding to the stable state as the target maximum fault clearing time after the fault occurs, and performing transient stability assessment based on the target maximum fault clearing time; wherein the dynamic equation and the approximate energy function are determined in the following manner: constructing the dynamic equation and the energy function according to the structure of the grid-connected system of the new energy generator set and the grid-connected converter; and approximating the damping term in the energy function to obtain the approximate energy function.

The invention provides a text sequence prediction method and system, a storage medium and electronic equipment. The method comprises the following steps: constructing an energy function of a text sequence prediction neural network; constructing a continuous time dynamic equation of a neuron state based on a network function; inputting the text lexical element vector of the current time step into a text sequence prediction neural network, and evolving the text sequence prediction neural network to a first equilibrium state based on a continuous time dynamic equation; adjusting an output layer neuron state of the text sequence prediction neural network into a text lexical element vector of a next time step based on a continuous time dynamic equation, so that the text sequence prediction neural network is evolved to a second equilibrium state; and updating the weight of the text sequence prediction neural network based on the neuron state difference in the first equilibrium state and the second equilibrium state. According to the text sequence prediction method and system, the storage medium and the electronic equipment, rapid prediction of the text sequence is realized based on the neural network evolved by the energy function and the continuous time dynamic equation.

The present application relates to the technical field of image processing, in particular to a kind of forging heat treatment deformation detection method and system, the method comprises: first, the gray image of forging is obtained, gradient vector, gray saturation and local entropy are calculated, gradient reliability is determined, and local reliable vector is calculated, and guide vector field is generated by tensor diffusion;Then, based on the initialization active contour model, reconstruct its energy function when iteratively updating: external energy term is driven by the guide vector of contour point;Internal energy term is constrained by anisotropic stiffness tensor calculated based on gradient vector and local entropy, and finally the model converges to hot profile and carries out deformation analysis.The method is systematically modified to the internal and external energy terms of traditional model, suppresses halo and haze interference, solves feature confusion problem, and improves the accuracy and robustness of contour detection under high-temperature strong interference environment.

The invention provides a rock dynamic shear failure simulation method and equipment considering frictional contact. The method comprises the following steps: calculating a stress tensor and a four-order elastic modulus matrix according to an elastic constitutive relation of a rock material; establishing a crack surface local coordinate system based on a maximum shear stress criterion, and determining normal and tangential unit vectors; introducing a crack density function and a corresponding degradation function of the cohesion phase field model, and configuring to eliminate the influence of the phase field characteristic length; deducing an energy functional of a dynamic shear phase field model, judging a contact state according to normal strain of a crack surface, and deducing strong forms of a displacement field control equation and a phase field control equation; variational processing is conducted on the strong forms of the displacement field control equation and the phase field control equation, corresponding weak forms are obtained, rock dynamic shear failure simulation considering frictional contact is achieved based on the weak forms, and rock mass stability evaluation in geological disaster prevention and control is supported. The model is insensitive to the phase field characteristic length, and the simulation result is more reliable.

The application discloses a kind of water transport ship traffic collaborative control methods based on deep learning, specifically includes: obtaining ship operation data set, time index alignment is carried out, and state vector sequence is generated;State vector sequence is encoded to feature, and deep boltzmann machine model is constructed;First conditional energy function, second conditional energy function and energy coupling matrix are established between model layer, and form hierarchical conditional energy function set;Hierarchical conditional energy function set is carried out to hidden variable probability inference, and hidden layer probability state vector sequence is generated;Parameter flow shape is divided to energy function parameter, and policy optimization is carried out under the constraint of confidence domain, and updated energy function parameter set is generated;Updated energy function parameter is carried out to energy calculation, and state probability distribution sequence is generated;State probability distribution sequence is carried out to strategy mapping, and ship traffic collaborative control strategy is generated.The application constructs hierarchical energy coupling model, realizes the collaborative control of multiple ships state.