956 results about "Solver" patented technology

The invention discloses a multi-modal visual arrangement recommendation method, belongs to the technical field of artificial intelligence and data visualization crossing, and realizes visual arrangement recommendation based on multi-modal input analysis, a dynamic mixed recommendation model and an intelligent optimization algorithm. Comprising the following steps: multi-modal intention analysis: realizing intelligent analysis of multi-modal input through combined use of a base model and a fine tuning model, realizing high-precision intention classification in combination with a pre-training language model and a domain adaptation fine tuning technology, and triggering dynamic prompt word recommendation; performing intelligent layout generation: performing global optimization of component space allocation by adopting a genetic algorithm, performing business rule adaptation by combining a constraint solver, and modeling an interaction relationship between components by utilizing a graph neural network; and dynamic mixed recommendation: constructing a three-level recommendation architecture including collaborative filtering, content matching and reinforcement learning. According to the method, a closed-loop recommendation process of user intention-intelligent recommendation-feedback optimization is realized, and the intelligent level of visual arrangement and the user experience are remarkably improved.

A source-grid-load-hydrogen storage multi-stage planning method and system considering flexible resources, relating to the technical field of source-grid-load-hydrogen storage system planning. The method comprises: collecting source-grid-load-hydrogen storage data for data preprocessing; constructing a flexibility supply and demand characteristic model and a source-grid-load-hydrogen storage multi-stage dynamic planning model; calling a solver to solve the source-grid-load-hydrogen storage multi-stage dynamic planning model to obtain an optimal solution; and outputting a multi-stage source-grid-load-hydrogen storage investment result, a multi-stage source-grid-load-hydrogen storage operation policy, and a multi-stage flexibility supply evaluation result within a planning period. Using the minimization of investment costs, operation costs, and insufficient flexibility penalty costs within the whole planning period as target functions, various constraints such as a new energy permeability constraint and a load loss rate constraint are introduced, a dynamic planning method is proposed, and a source-grid-load-hydrogen storage multi-stage planning solution that has sufficiently economical planning operation and is sufficiently flexible is obtained. According to a processing method based on piecewise linearization, the model is simplified, and the computation speed is increased.

The invention discloses a hierarchical optimization scheduling method for deep peak regulation of thermal power generating units, and the method comprises the following steps: system layering: dividing a power system into a plurality of subsystems, each subsystem comprising a thermal power generating unit cluster and differentiated load demands; subsystem-level prediction: generating a basic scheduling plan; dynamic preference-driven multi-target collaborative optimization: receiving multi-target data from each subsystem, carrying out multi-target collaborative optimization, modeling three targets of a power grid company, an environmental protection department and a terminal user as game participants, quantifying the priority of each party by adopting a fuzzy membership function, generating a dynamic game solution through a Nash equilibrium solver, and carrying out multi-target collaborative optimization; screening out a Pareto optimal solution giving consideration to interests of multiple parties; performing online verification on the dynamic security domain; virtual synchronous machine cooperative support: simulating operation characteristics of a synchronous generator; and global coordination and iterative optimization: uploading the optimal scheduling scheme of each subsystem to a scheduling center through a message queue telemetry transmission protocol, and performing global constraint verification.

The invention discloses a three-dimensional unstructured grid adaptive refinement method and system based on machine learning, and belongs to the field of machine learning, partial differential equation solving and computational fluid mechanics simulation. Residual errors of a fluid control equation are used as a novel error indicator and a refinement criterion, imprecise flow field data and the fluid control equation are obtained by fusing coarse grids of simulation flow through a physical information neural network, and the total residual errors of the equations are conveniently calculated by using automatic differentiation after training is completed. Therefore, the coarse grid units with relatively high residual errors can be adaptively marked and refined. By matching an h-refinement scheme, Delaunay tetrahedron subdivision is executed after vertexes are strategically inserted to maintain the quality of the refined grid. According to the method, any numerical solver can be flexibly matched to carry out grid adaptive refinement, so that various typical flow problems can be solved with high precision. The method achieves better balance between calculation precision and the number of grids, and has the advantages of being simple, convenient, high in compatibility and universality and the like.

The invention discloses a mold and formwork design system based on virtual simulation, and particularly relates to the field of mold design, the mold and formwork design system comprises a multi-mode perception fusion module, a digital twin modeling module, a multi-physics field coupling simulation module, a hybrid intelligent optimization module, a digital twin closed loop verification module and a data center module, and each module forms a design closed loop through a data center. The multi-modal sensing fusion module adaptively collects and fuses multi-source signals to generate high-credibility data; the digital twin modeling module constructs and iteratively corrects a model based on the data; the multi-physics field coupling simulation module realizes multi-solver co-simulation under a dynamic boundary; the hybrid intelligent optimization module accelerates to generate an optimal solution through secondary optimization and an agent model; the digital twin closed-loop verification module detects defects and generates a correction instruction; the data center module is responsible for data management, cross-module scheduling and precision control; the system improves the design precision and efficiency of the mold frame, reduces the physical mold testing cost, and is suitable for a high-precision mold development scene.

The invention discloses a full-reactor-core parallel fluid-solid coupling simulation method and system. The system comprises a global collaborative coupling simulation scheduling module, a hierarchical index forest management module, a high-performance parallel solver module and an intelligent parameter transmission module. Wherein the global collaborative coupling simulation scheduling module is used for being responsible for overall process control, time stepping, solver scheduling, convergence judgment and inter-module communication triggering; the hierarchical index forest management module is used for being responsible for distributed parallel construction and efficient dynamic updating of CLGIF and providing rapid geometric query service; the high-performance parallel solver module is used for embedding or tightly integrating numerical solvers of fluid and solid physical fields and applying a dynamic parallel multi-solver optimization module to maximize the calculation performance and expansibility of the high-performance parallel solver module; and the intelligent parameter transmission module is used for executing physical quantity exchange on a fluid-solid interface, the core of the intelligent parameter transmission module is a GME-driven region sensing RBF mapping engine, and the transmission precision and efficiency are ensured.

The invention discloses a visual setting calculation system and method, and relates to the technical field of power grid setting calculation, and the method comprises the following steps: obtaining operation data of a target system, extracting feature data, and constructing a knowledge graph of entities and relationships, the feature data including control data; constructing a system structure diagram, and generating a semantic enhancement diagram in combination with semantic information in the knowledge graph; inputting the semantic enhancement graph into a pre-trained graph neural network model, performing node feature aggregation and edge weight learning in combination with a prior rule of a knowledge graph, and outputting sensitivity scores of all control data nodes on performance indexes; according to the sensitivity score, constructing a boundary constraint condition, and generating a control data setting candidate solution based on a CSP solver; performing parameter response simulation verification on the control data setting candidate solution, and screening out an optimal control data setting candidate solution; according to the application, accurate sensitivity scoring can be realized by fusing the knowledge graph, the semantic enhancement graph and the graph neural network.

A k-parallel row sorting problem solving method considering multiple channels relates to the technical field of disassembly line layout, and mainly comprises the following steps: determining an objective function, calculating channel coordinate information in a region, generating a population P1 and randomly generating a population P2 by using a greedy strategy, combining the population P1 and the population P2 into an initial population P, calculating the fitness of all solutions in the initial population, and obtaining a k-parallel row sorting problem; screening an elite solution, selecting solutions except the elite solution in the initial population based on the fitness by using a roulette mechanism to execute genetic circulation, sequentially carrying out crossover and mutation operation based on Q-learning, combining the population with the elite solution after mutation with the population P1 and the population P2 to update the population, according to the maximum number of iterations, it is judged that iterative calculation continues or a result is output; according to the variable domain genetic algorithm based on Q-learning, an optimal solution for solving kPROPP can be provided in a short time, the solving efficiency of the variable domain genetic algorithm is greatly superior to that of a conventional accurate solver, the solving effect of the variable domain genetic algorithm is superior to that of other methods, and reliable support is provided for solving the problem of parallel layout planning.

The invention provides a ship micro-grid energy management method based on hierarchical coordination of model predictive control and a chaos evolution optimization algorithm, and the method comprises the steps: constructing a hierarchical coordination control architecture which comprises an upper rolling optimization scheduling layer and a lower real-time stability control layer; the upper layer adopts a model prediction control framework, a chaos evolutionary optimization algorithm is built in to serve as a solver on the basis of load and new energy output prediction of a future time domain, and an optimal power reference instruction in the future short time domain is solved in a rolling manner by taking operation cost, energy storage aging cost and power grid stability penalty as a comprehensive target; and the lower layer executes power scheduling according to the reference instruction issued by the upper layer, absorbs and compensates high-frequency power fluctuation in real time by using a droop control quick response mechanism of the energy storage system, and maintains the stability of the power grid. According to the method, decoupling cooperation of global economic optimization and local dynamic stability of the ship microgrid can be realized, the fuel consumption is remarkably reduced, and the safety and reliability of the system under complex working conditions are improved.

The invention relates to the technical field of low-altitude equipment, in particular to a low-altitude resource intelligent scheduling method and system based on deep learning, and the method comprises the steps: collecting the real-time state and network load data of a low-altitude flight equipment group, and constructing a dynamic operation data set; generating an operation mode feature set through multi-dimensional airspace situation awareness and analysis, and performing sparse clustering division based on the feature set to form a network resource demand priority mapping table; traversing the mapping table to dynamically calculate the resource demand, determining a multi-dimensional weight coefficient, and performing high-dimensional feature dimension reduction and optimization through a mixed integer nonlinear programming solver to obtain a resource demand feature vector; constructing a resource scheduling strategy optimization model by adopting a deep reinforcement learning algorithm based on the vector; inputting real-time data into the model to execute a resource scheduling decision, and outputting a dynamic allocation strategy; simulation deduction and compliance verification are carried out on the strategy in the digital twin simulation platform, and cooperative intelligent scheduling of communication, calculation and spectrum resources is achieved.

The invention relates to the technical field of computer-aided engineering, in particular to a nonlinear efficient iterative solving method for thermal strain load, which is used for solving an obtained finite element equation by using a modified Newton-Raphson method and comprises the following steps of: firstly, establishing a geometric model to be analyzed and performing grid division; the method comprises the following steps: firstly, setting a model to be analyzed, then setting boundary conditions and temperature field change conditions of the model to be analyzed, finally, inputting the conditions into an algorithm solver, solving a nonlinear equation set, iteratively inputting a load continuously according to a modified Newton-Raphson method in the solving process, calculating node displacement until convergence, and solving stress-strain conditions in the welding process according to a material constitutive relation. In the solving process, by judging the size of the two norms of the displacement increment obtained in the adjacent increment step heuristic stage, the convergence of nonlinear solving of the structure is enhanced through the calculation mode, and the solving speed of the nonlinear problem in the thermal elastic-plastic constitutive structure is increased.

The invention provides a plane layout optimization method and device based on CAD drawing automatic identification, relates to the field of plane layout optimization, and solves the technical problems that only non-overlapping constraints among equipment are considered, actual space obstacles cannot be automatically avoided, and a generated layout scheme cannot be implemented. The method comprises the following steps: extracting barrier data in a layer; and through a parameter configuration mechanism, generating an equipment parameter information structural body containing the mapping relationship between the equipment and the process. And establishing a continuous optimization model containing an IPOPT solver, and obtaining an initial layout solution containing the relative orientation of each device. And based on the relative orientation of each device in the initial layout solution, introducing an obstacle rejection constraint and a device non-overlapping constraint to construct a discrete optimization model, obtaining the center position coordinates of each device through a Gurobi solver, and integrating a dynamic adjustment mechanism and an interlayer relationship optimization mechanism. And generating a new layout map for visual output. The method and the device are used in a plane layout optimization process.

The present disclosure provides a computer-implemented system for automated augmentation of differential equation models. The system stores differential equations representing mechanistic behavior of physical or computational processes and constructs a hybrid computational solver by embedding a trainable universal approximator with adjustable parameters into the differential equations, where approximator outputs augment time derivatives during numerical integration. An iterative training process adjusts parameters through numerical integration, monitors integration failures, assigns infinite penalty values to loss functions when failures occur, and computes gradients using automatic differentiation otherwise. The system computes sensitivity metrics via Jacobian matrix evaluation, classifies input / output subsets as significant based on threshold-exceeding sensitivity metrics, generates a reduced approximator operating on classified subsets, and replaces the universal approximator with the reduced version to create an optimized solver.

The invention relates to the field of business management, in particular to a production and manufacturing resource scheduling-oriented mixed integer programming optimization system, which comprises six modules: a data acquisition module acquires industrial big data streams; the data mapping module processes the data flow and constructs a real-time data model; the dynamic modeling module is used for automatically generating a mixed integer programming model according to a built-in steel production process model library based on a real-time data model; the model solving module generates a resource scheduling scheme through an optimization engine; the instruction analysis module analyzes a specific management and operation instruction; and the scheduling recalculation module starts a new round of mixed integer programming modeling and solving according to the increment information. According to the system, information such as resources is summarized through a mixed integer programming method, a specific production instruction is generated through a solver, a new round of modeling and solving are started when an accident occurs, rapid and accurate response to resource scheduling is achieved, and the response efficiency of resource scheduling and the reliability of the system are improved.

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

The invention discloses a multi-degree-of-freedom mechanical arm control method based on quantum particle swarm optimization of a migration strategy. The method comprises the steps that system modeling is conducted; mPC to-be-adjusted parameter definition and constraint processing are carried out; adopting, adopting, and introducing penalty terms to construct a composite fitness function; updating particle positions by adopting a basic or enhanced quantum updating mode; triggering conditions are judged in a group diversity measurement mode, and when the conditions are met, a migration strategy and dynamic subgroup division are executed; the global optimal solution and the fitness value are loaded into a real-time MPC controller, and online adjustment is carried out; a prediction equation is constructed, tracking errors and energy consumption optimization are converted into a standard quadratic programming problem, and the solving precision is dynamically adjusted in combination with Cholesky pre-decomposition, a structured sparse solver and a warm-start and early stop strategy; and a closed-loop adaptive control system is constructed. According to the method, the precision, robustness and real-time performance of trajectory tracking control of the mechanical arm can be effectively improved.

The invention provides an FPGA parallel Isin model optimization method and system based on a GHMC simulated annealing algorithm, relates to the field of power system combination optimization, and solves the technical problem that an existing FPGA-based Isin model solver adopts a traditional Monte Carlo method and is low in convergence precision and solution quality. The method comprises the following steps: receiving a question input by a user through an upper computer, and mapping the received question into a Hamiltonian parameter of an Isin model; the Hamiltonian parameter is transmitted to an FPGA through a communication interface, and a mixed GHMC sampler and a temperature scheduling module are arranged in the FPGA; the hybrid GHMC sampler carries out parallel solution on Hamiltonian parameters; and the temperature scheduling module gradually reduces the system temperature according to a preset annealing curve, and drives the Isin model to converge to a global optimal solution. The method is used in the power system combination optimization process.

The invention discloses an aircraft scheduling optimization method, device and system, and a storage medium. The method comprises the following steps: S1, generating training data for simulating real route scheduling; s2, processing the flight plan according to the training data, and generating all feasible flight strings and the cost of each flight string; and S3, according to the generated flight string and the corresponding cost, aircraft scheduling optimization is carried out through a feature extraction algorithm, a TRS-GCN model and a Gurobi solver. By adopting the technical scheme of the invention, the route scheduling efficiency is improved.

The invention discloses a quantum bit mapping optimization method for hardware guidance and fidelity perception and a related device, and belongs to the field of quantum computing, and the method comprises the steps: defining each node in a hardware coupling graph of a target quantum processor as an initial community; evaluating all adjacent community pairs according to a reward function defined based on modularity and fidelity of candidate communities, selecting an optimal community pair for fusion, and taking the community with the largest current scale as an optimal candidate area; iteratively executing a community fusion step until a converged single community is obtained; and selecting an optimal candidate region with the physical quantum bit number greater than or equal to the logic quantum bit number of the quantum circuit to be mapped and the minimum scale, and outputting a mapping sub-graph. According to the method, through a recursive community fusion strategy, the operation range of the solver is restrained in a subspace with the scale greatly reduced and the quality better, the problem of combinatorial explosion caused by global search is fundamentally avoided, and the calculation overhead is greatly reduced.

The embodiment of the invention provides a federated learning contribution evaluation method and device, and the method comprises the steps: carrying out the grouping of a plurality of edge computing devices, and obtaining a plurality of sub-federated learning sets; and for the target federated learning sub-set, aggregating model update information corresponding to each edge computing device in the target federated learning sub-set, and determining a collaborative contribution value of the target federated learning sub-set based on the performance index of the updated global model on the common test set. Through a first linear programming solver, according to the collaborative contribution values of the multiple federated learning sub-sets, obtaining the maximum loss value corresponding to all the federated learning sub-sets and optimizing the maximum loss value to obtain the minimized maximum loss value, and through a second linear programming solver, obtaining the maximum loss value corresponding to all the federated learning sub-sets; and according to the maximum loss value after all the sub federated learning sets are minimized and the reference contribution values corresponding to the plurality of edge computing devices, target contribution vectors corresponding to the plurality of edge computing devices are determined, and the contribution degree of each edge computing device in the training process is accurately quantified.

The invention provides a narrow gap swing laser welding pool wettability prediction method based on CFD simulation. The method comprises the following steps that an interaction platform is constructed through Python + PyQt5, and geometric modeling, UDF compiling and a material library module are integrated; the groove parameters and the swing track are dynamically loaded to a Fluent solver, and a full-coupling transient model of a steam recoil pressure-buoyancy-molten drop mass source item is established; in CFD-POST, the widest section of the molten pool is positioned on the basis of the contour surface with the volume fraction being 0.5, and Z-direction displacement-flow velocity data are extracted along the two side wall surfaces; fusing defects are predicted through double criteria of a bilateral average flow velocity difference threshold value and F test variance homogeneity; according to the method, dynamic quantitative evaluation of wetting behaviors can be achieved, the process development period is shortened, the defect detection rate is increased, and the method is suitable for aerospace thick plate welding quality control.

The invention discloses a ship viscous flow numerical simulation method based on a hybrid Cartesian grid, and relates to the technical field of ship CFD. The method comprises the steps that after a computational domain is constructed, a body-fitted grid is generated at the object plane boundary of a ship model; then, on the basis of the outermost-layer body-fitted grid, a full-thread tree is used for carrying out space division on a computational domain to generate a Cartesian grid, and the full-thread tree is output as Cartesian grid topological information matched with the data structure of the target viscous flow solver; and finally, assembling and coupling the body-fitted grid and the Cartesian grid by using an overlapping grid technology to obtain a mixed partition grid unit, and applying the mixed partition grid unit to a solving process of a target viscous flow solver. Therefore, the accuracy and robustness of viscous flow numerical simulation of the ship with the complex geometry are improved, and the method can be well applied to resistance and flow field numerical simulation scenes in underwater and water surface ship body motion states.

The invention relates to the technical field of calculation, and provides a CFD solution acceleration method and system based on parallel calculation and load dynamic balance. Physical characteristics such as a CFL number and a residual error are quantified into load indexes, so that a load balancing decision and a flow field evolution rule are tightly coupled; by adopting an asynchronous communication overlapping framework deeply coupled with a CFD solver, blocked communication waiting time is converted into effective calculation time, and communication delay is systematically hidden; based on an incremental grid migration strategy of real-time load evaluation, high-load grid blocks in a hotspot process can be intelligently identified and migrated, and differentiation and high-efficiency utilization of computing power are realized.

The invention belongs to the technical field of vibration control, and provides a fluid-structure interaction numerical calculation method for controlling flutter of a large-span bridge by a submerged heaving plate, which is based on computational fluid dynamics software Fluent, utilizes a dynamic grid technology, compiles a UDF secondary development program and embeds the UDF secondary development program into a solver for calculation. The method comprises the following steps: establishing a motion control equation of a rigid main beam-sling-underwater heaving plate coupling system in a wind field, embedding a system motion solving process into Fluent software by utilizing a user-defined function, and realizing system fluid-solid coupling solving by adopting a mode of alternately solving a rigid main beam and a wind field computational domain and simultaneously solving a heaving plate and a water body computational domain. And vertical and torsional motion responses of the controlled main beam are obtained. Compared with a physical experiment, rich information such as movement time history data of the main beam, the heaving plate and a nearby flow field under the condition of various sizes can be conveniently and safely obtained, vibration suppression mechanism analysis can be conveniently carried out, and the research difficulty, cost and risk are greatly reduced.

The invention provides a power grid traveling wave synchronous positioning method based on mixed integer convex quadratic programming, and the method comprises the steps: building a mathematical model for describing the topological structure and line length of a power grid, obtaining the fault traveling wave arrival time recorded by each monitoring point in the power grid, and building an actual measurement time difference matrix based on the fault traveling wave arrival time and the mathematical model; constructing a mixed integer convex quadratic programming model based on the actual measurement time difference matrix, calling a mixed integer programming solver to solve the constructed mixed integer convex quadratic programming model to obtain a global minimum mismatch degree, and meanwhile, obtaining a global optimal fault line, a fault position and a traveling wave velocity corresponding to the global minimum mismatch degree. And traveling wave synchronous positioning is realized. The method can improve the positioning accuracy and reliability under the condition of unknown wave velocity and complex network topology.

The invention discloses a VLA task space control method and system for a multi-degree-of-freedom mechanical arm, and relates to the technical field of intelligent robot control, visual information and language instructions when the mechanical arm executes a task are obtained, and a trained VLA model is adopted to obtain a target pose of an end effector in a task space; an IK solver corresponding to the mechanical arm configuration is adopted, and according to the target pose, multiple sets of joint angle solutions of all joints of the mechanical arm are obtained; selecting an optimal joint angle of each joint from the multiple groups of joint angle solutions by adopting a preset optimization criterion; and after the optimal joint angle sequence of the mechanical arm is subjected to track smoothing processing, the mechanical arm is driven to move. Mechanical arm motion control is carried out in combination with a VLA model and an IK solver, the problems that the generalization ability between different mechanical arm configurations is poor, the data collection cost is high and the model interpretability is insufficient are solved, and the generalization ability and data efficiency of mechanical arm control are improved.

There is provided a system and methods to determine an improved solution to a Mixed Integer Problem (MIP) using a quantum-assisted MIP solver. The methods are performed by a digital processor in communication with a quantum processor. Methods include: selecting at least one feasible solution determined by an MIP solver, determining a first sub-problem of the MIP based on the at least one feasible solution; casting the first sub-problem as Binary Quadratic Models (BQMs); solving the BQMs using the quantum processor to generate sample solutions; determining a second sub-problem based on at least the sample solutions, and obtaining a current solution to the MIP by evaluating the second sub-problem; and updating an incumbent solution if the current solution improves over the current incumbent solution. The quantum-assisted MIP solver uses hybrid crossover and mutation heuristics to improve the convergence time and accuracy of solutions obtained using Branch-and-Cut solvers.

The invention relates to the technical field of oil and gas field development, and discloses a horizontal well multiphase flow profile inversion method based on multisource data fusion, which comprises the following steps: S1, laying optical fibers and acquiring data; s2, data preprocessing; s3, obtaining empirical parameters and a multi-source database; s4, judging a fluid scene and constructing a solver model; s5, iterative solution and phase fraction inversion are carried out, according to the empirical parameters and the multi-source database, the solver model constructed in the step S4 is adopted, solution is carried out in different phase number scenes, and an inversion index result is obtained; and S6, visual output and abnormal flow detection are carried out, and the inversion index result obtained in the step S5 is visually presented in the depth direction of the shaft and is used for assisting in judging abnormal phenomena. According to the method, the multiphase flow thermodynamic model is constructed based on the state equation, the water-liquid ratio and the gas-oil ratio are dynamically inverted through the iterative optimization algorithm in combination with the DAS sound velocity data, and high-precision characterization of multiphase flow characteristics is achieved.

The invention belongs to the technical field of power grid optimization, and particularly relates to a GAN driven learning-based power grid planning equalization optimization method and device, and the method comprises the steps: constructing a physical constraint-based space-time generative adversarial network PI-ST-GAN, building a composite risk index, and screening out a high-risk scene from a generated source-load probabilistic time sequence scene; constructing a risk-cost balanced double-layer optimization model; reconstructing the double-layer optimization model into a single-layer mixed integer programming (MIP) model based on a strong dual theory, and setting constraint conditions of the single-layer mixed integer programming (MIP) model; based on a single-layer mixed integer programming MIP model, a solver is utilized to carry out joint solution on a line investment scheme and operation scheduling variables, PI-ST-GAN is dynamically guided to enhance high-risk scene generation according to a solution result, an optimization-generation-feedback closed-loop collaborative iteration solution process is realized, and finally an optimal power grid planning scheme is output. The method can improve the toughness and economical efficiency of a power grid under the high permeation of renewable energy sources.

The invention discloses a reaction steam turbine through-flow method and system based on variable reaction degree optimization, and belongs to the technical field of steam turbine through-flow design. The method comprises the steps that an initial geometric model of a steam turbine through-flow part and thermal boundary conditions of all stages are obtained; inputting the initial geometric model and the thermal boundary conditions into a flow field solver, and solving a flow control equation to obtain an original flow field solution; constructing a functional taking through-flow efficiency as a target, and deducing an adjoint equation corresponding to the flow control equation; solving the adjoint equation to obtain gradient information of the target functional to each level of reaction degree; inputting the gradient information into a sequential quadratic programming optimizer, and iteratively updating the reactance of each stage by combining the maximum equivalent stress upper limit of the moving blade and the lower limit of the through-flow throat area to generate a variable reactance distribution scheme; and adjusting molded line geometric parameters of each stage of stationary blade and moving blade, and outputting optimized three-dimensional molded line data and a digital model file for numerical control machining. According to the method, the in-stage flow loss is reduced, and the design robustness is enhanced.