17results about How to "Speed ​​up the solution" patented technology

The invention discloses a power system unit commitment model solving method based on an improved simulated annealing algorithm, and the method comprises the steps: carrying out the optimization solving of a model objective function through employing the improved simulated annealing algorithm on the basis of a standard simulated annealing algorithm, and carrying out the optimization solving of a model objective function through adaptive temperature control and a multi-starting point strategy. And the global search capability and the convergence speed are improved when the unit commitment optimization problem of the power system is solved. Through the improvement of the invention, the algorithm can more effectively avoid falling into local optimum during solving, and the solving process is accelerated.

The invention provides a DNN model-based division and reasoning acceleration method and system for vehicle-mounted edge intelligence. The method comprises the following steps: constructing a DAG graph based on a vehicle-mounted DNN model; constructing an auxiliary network flow graph of the DAG; adding a head node representing local calculation of a vehicle and a tail node representing calculation of an edge server or an auxiliary vehicle, and setting the weight of an edge as time delay overhead for segmenting the edge; obtaining different layering modes of an auxiliary network flow diagram of the DAG and corresponding maximum tolerance point sets meeting local time delay, memory and energy consumption limitation of the vehicle; correspondingly modifying the weight of the edge in the auxiliary network flow graph of the DAG based on each maximum tolerance point set, and calculating the maximum flow of each modified network flow auxiliary graph; and selecting a division mode corresponding to the maximum flow as a DNN model division mode. According to the method, the vehicle and road cloud cooperative reasoning is realized through a DNN model division mode, so that the DNN task cooperative reasoning completion time delay is reduced, and the vehicle safety and the user experience are improved.

The invention discloses a unit commitment acceleration optimization method and system based on inducible function feasibility detection. The method comprises the following steps: acquiring an LP form solution of a unit commitment problem to provide an initial reference point for a subsequent solution process; and constructing a sub-problem according to the LP form solution, solving to obtain a feasible solution of the sub-problem, substituting the feasible solution of the sub-problem into an original unit commitment problem, repairing to obtain a feasible solution of the original unit commitment problem, and obtaining a target function value of the original unit commitment problem. The objective function value is compared with the LP form solution, whether the feasible solution is within the constraint condition can be judged, and finally the optimized feasible solution of the unit commitment problem is obtained. Compared with a traditional solving method, the method has the advantages that a good feasible solution can be found in a short time, and the solving process of the optimal solution is accelerated. The method can adapt to unit combination problems of different scales and complexity degrees, and has certain flexibility for continuously changing conditions in a power system.

The invention relates to a unit combination scheduling method and device, computer equipment, a medium and a product. The method comprises the following steps: acquiring topological connection information and system operation information corresponding to a target unit combination; the topological connection information is used for indicating a topological connection relationship in a target power network where the target unit combination is located; the system operation information is used for indicating system operation characteristics of the target power network; based on the topological connection information and the system operation information, performing spatial decoupling on the target power network to obtain a plurality of target decoupling sub-regions corresponding to the target power network; and determining a plurality of sub-scheduling results corresponding to the unit combinations in the plurality of target decoupling sub-regions, and determining a target scheduling result of the target unit combination in combination with the plurality of sub-scheduling results. By adopting the method, the scheduling processing efficiency can be improved.

The invention relates to a calculation method for load conversion of a local coordinate system, and the method is based on a part simulation model, and comprises the following steps: S1, determining a hard point coordinate of a part; s2, establishing a local coordinate system; s3, establishing a local coordinate load calculation formula: F < x > Local = a * F < x > Globe + b * F < y > Globe + c * F < z > Globe; s4, setting boundary conditions; s5, solving a coefficient; the method has the advantages that a model of the part does not need to be arranged during calculation, calculation can be carried out only through the hard point position, specific simulation analysis software is not needed, calculation is easy, the calculation amount is reduced, the calculation capacity is high, calculation efficiency is improved, large-scale and batch mechanical analysis calculation of the automobile part can be carried out, and the method is suitable for popularization and application. The application range is wide and the practical value is high.

The application provides a kind of offshore wind farm double-sided annular power collection system planning and design method, comprising the following steps: S1: obtaining all candidate construction cables in the planning area: receiving all node coordinates and the maximum distance, the node includes offshore wind turbine and offshore substation, for each node, list all optional construction cables between it and its surrounding nodes, select the cable less than the maximum distance as the candidate construction cable;S2: obtain all candidate cable combinations that occur intersection, and impose constraints so that candidate cables that can occur intersection cannot be constructed at the same time;S3: based on capacity constraint vehicle routing planning and power system expansion planning to establish power collection system planning model;S4: solve offshore wind farm power collection system planning optimization model using commercial solver.The technical scheme of the application can guide the design of large-scale offshore wind farm power collection system topology structure that meets the "N-1" rule, and has better stability.

The invention relates to the technical field of high-entropy alloy, and discloses a transferable element learning algorithm framework, which integrates element learning based on an adaptive migration sea image optimizer, a balanced relative density peak value clustering (DPC) algorithm and a migration strategy, and constructs a transferable element learning model. Wherein the self-adaptive migration sea elephant optimizer dynamically adjusts a migration strategy according to the change of a population average fitness value, and the convergence speed is improved; according to the balanced relative DPC algorithm, the clustering effect is optimized by considering the local density and the mutual nearest neighbor relation, and the density difference problem between different clusters is solved. According to the migration strategy, a most matched historical problem is searched from a source domain by using the maximum mean difference (MMD), optimal solution information is migrated to a target domain, and the model performance is enhanced; according to the method, by combining meta-learning and transfer learning strategies, the calculation burden is relieved, the prediction accuracy is improved, efficient and accurate algorithm recommendation is provided for material designers, and the material development period is remarkably shortened.

The invention belongs to the technical field of regional power grid electric energy quality management, and discloses an offshore wind plant station harmonic resonance suppression method, system and device and a medium, and the method comprises the steps: calling the residual capacity of an offshore wind power access node, carrying out the primary harmonic compensation based on the residual capacity, and carrying out the primary evaluation of a regional power grid after the primary harmonic compensation; if the initial evaluation does not meet the preset suppression requirement, establishing a collaborative suppression mechanism by combining an active filter, performing secondary harmonic compensation by applying the collaborative suppression mechanism, and performing secondary evaluation on the regional power grid after the secondary harmonic compensation; and if the secondary evaluation result still does not meet the preset suppression requirement, performing iterative optimization on the parameter configuration of the active filter and the utilization configuration of the residual capacity by using an improved particle swarm optimization algorithm to obtain an optimal configuration scheme. According to the invention, through cooperative utilization of the residual capacity of the wind power, the number of active filters is reduced, the treatment cost is reduced, and the wind power compensation economy is optimized.

The present invention belongs to the field of transportation, and specifically relates to a method and device for bus station site selection and route design based on passenger demand. The method steps are as follows: S1: Establish a directed graph representing traffic conditions based on the geographical characteristics and road distribution of the region. S2: Determine the alternative routes, starting and ending arcs and virtual arcs of the bus route, and establish an operation map. S3: Determine the alternative boarding and alighting points for passengers, passenger boarding and alighting arcs, operation arcs, transfer arcs and virtual arcs, and establish a demand map. S4: Take the arcs in the operation map and the demand map as decision variables; the minimum comprehensive cost of travel cost and route operation cost is the optimization goal; set the constraint condition of the rationality constraint of the route, and establish a line network optimization model. S5: Use a variable neighborhood search algorithm to iteratively optimize the line network optimization model to obtain the optimized site selection and line layout. The present invention solves the problem that the existing bus line site layout is unreasonable and cannot meet the travel needs of passengers.

The utility model discloses a kind of photolithography alignment mark pattern structures of supporting auxiliary focusing, with the first alignment mark main pattern of cross shape and 4 second alignment mark main patterns of square block shape respectively set in four quadrants of cross shape, characterized by: in the cross shape of the first alignment mark main pattern 4 end portions, each cooperation is provided with a first MTF target pattern, in the 4 square block shape of the second alignment mark main pattern outer corner, each cooperation is provided with a group of second MTF target pattern.The utility model can realize the quick quantitative evaluation of alignment mark imaging quality, help to ensure that the mark image is acquired in optimal state, and then the position coordinate where mark is calculated with higher precision.

The present invention belongs to the field of geophysical exploration technology. A method, system, medium, product and equipment for three-dimensional inversion of transient electromagnetic data are provided, and three-dimensional regularized inversion is performed with the objective function of minimizing the sum of the product of the model constraint term and the regularization parameter and the data fitting term; the acquisition of the sensitivity matrix in the gradient of the objective function includes: solving the three-dimensional forward electromagnetic field full discrete equation to obtain the forward field, constructing the adjoint forward electromagnetic field full discrete equation whose coefficient matrix is ​​a low-order tridiagonal matrix, solving the adjoint forward electromagnetic field full discrete equation to obtain the calculated adjoint field, and obtaining the sensitivity matrix according to the forward field and the adjoint field. The present invention uses a direct splitting strategy to decompose the coupled electromagnetic field, and reconstructs the large sparse matrix into a series of low-order tridiagonal matrices. Whether it is the forward process or the sensitivity calculation, it only needs to solve the low-order tridiagonal matrix, which significantly speeds up the three-dimensional inversion speed.

The invention discloses a cluster decision-making method and system based on evolutionary transfer learning, and belongs to the technical field of local security protection, and the method comprises the following steps: S1, initializing a chasing scene, and generating a current task parameter group; s2, determining a most similar task parameter group; s3, determining a Pareto frontier solution with a fixed size according to the most similar task parameter group; s4, obtaining an optimal solution of the current task parameter group, and determining the fitness of the optimal solution; and S5, updating the positions of the pursuit agent and the escape agent. According to the method, the historical optimal solution set is dynamically screened, global optimization experience of similar tasks is inherited, the local evolution advantage of adjacent tasks is reserved, inter-generation and inter-time-sequence knowledge migration is achieved, and therefore the solving process of the current task is remarkably accelerated.

The application discloses a point cloud registration method based on global feature and local feature fusion, comprising the following steps: obtaining a reference point cloud and a point cloud to be registered; extracting point cloud features of the reference point cloud and the point cloud to be registered respectively to form an initial reference point cloud feature set and an initial point cloud to be registered feature set; introducing self-attention mechanism and cross-attention mechanism, and introducing correlation information between the reference point cloud and the point cloud to be registered to form a final reference point cloud feature set and a final point cloud to be registered feature set; obtaining similarity information between each point pair; constructing a matching point pair relationship matrix according to the similarity information between the point pairs; obtaining a transformation model parameter of the reference point cloud and the point cloud to be registered; and registering the reference point cloud and the point cloud to be registered. The application fully excavates potential spatial topological structure information of point cloud data by combining local features and global features, obtains point cloud fusion features with rich semantic information, and can effectively improve the accuracy of point cloud registration.

The invention provides a method for improving the calculation efficiency and stability of ultra-high-speed collision simulation, and aims to improve the simulation efficiency by establishing an ultra-high-speed collision model, setting symmetric constraints, adopting a rigid-flexible conversion technology and performing parallel processing, so that the solving process is remarkably accelerated; besides, in order to enhance the stability of the solution, the stability of the solution process is ensured through static memory allocation, and unnecessary fluctuation is reduced. By comprehensively applying the strategies, the simulation efficiency of ultra-high-speed collision can be effectively improved, and more efficient calculation support is provided for ultra-high-speed collision research.

The invention provides an inverse kinematics method of a gantry crane, a storage medium and electronic equipment. The method comprises the following steps: measuring and recording an amplitude variation distance / amplitude variation lifting height of a crane hook at an amplitude variation position, and recording an output value of an amplitude variation motor encoder at the amplitude variation position; the recorded data are substituted into a neural network for learning, and the relation between the motion angle of a variable-amplitude motor under pure variable-amplitude motion and the variable-amplitude distance / variable-amplitude lifting height of a crane hook is obtained; and recording output values of encoders of the lifting motor and the rotating motor during lifting and rotating motion, and obtaining a relationship between a rotation angle of a crane hook and a motion angle of the rotating motor and a relationship between a motion angle of the lifting motor and a lifting height of the crane hook according to mechanical transmission parameters of the lifting structure and the rotating structure. The method does not depend on complex structure calculation, inverse kinematics calculation is carried out only through an experimental point taking mode, the applicability is wide, and the method is easily applied to engineering practice.

This application discloses a method, equipment, and medium for longitudinal vibration response analysis of large-diameter floating piles, relating to the field of engineering construction technology. The method includes: constructing a pile-soil coupled dynamic model by simplifying the soil around the pile and correcting for continuous variable cross-sections; performing dimensionless processing on the pile-soil coupled dynamic model; constructing a physical information neural network architecture; calculating the partial derivatives of the network output data corresponding to the network input data to determine the model loss function; training the physical information neural network until the model converges through importance sampling and phased collaborative optimization to obtain the physical information neural model; calculating the predicted velocity curve at the pile top of the large-diameter floating pile; and calculating the residual waveform between the predicted velocity curve at the pile top and the real-time pile top velocity signal to obtain the longitudinal vibration response analysis results of the large-diameter floating pile. This application solves the technical problems of low efficiency, poor robustness, and inability to meet timeliness requirements in the vibration response analysis of large-diameter floating piles through the above method.

The invention belongs to the technical field of integrated energy system planning, and particularly relates to a power grid control method and system. The power grid control method provided by the invention comprises the following steps: setting constraint conditions of new energy power generation, adjustable power load, electrolytic hydrogen production and energy storage; predicting safety parameters through load flow calculation; determining a target function, and constructing a corresponding relation between a penalty function and an out-of-limit value of the safety parameter; carrying out optimization solution on the solution variable through an intelligent algorithm to obtain an optimal variable for realizing the target function, and executing the optimal variable; and judging whether safety parameters are out of limit or not in real time in the execution process. The hydrogen capacity of electrolytic hydrogen production, the energy storage capacity constraint of the energy storage device and the power grid safety constraint are fully considered, and the safety stability of the hydrogen production device, the adjustable power load and the electrochemical energy storage device and the economical efficiency of system operation are guaranteed through the hydrogen, the adjustable power load, the capacity constraint of electrochemical energy storage and the power flow constraint.