1490 results about "Topological optimization" patented technology

The invention provides a news scene three-dimensional reconstruction and visualization method based on multi-source remote sensing data, and relates to the technical field of three-dimensional reconstruction, and the method comprises the steps: extracting multi-modal data feature points through employing a spatial position weighted iterative nearest point algorithm and a tensor projection algorithm; after bidirectional attention deep neural network processing and local geometric consistency constraint screening, registration multi-source remote sensing data is generated, data enhancement is carried out through a variational auto-encoder network and a generative adversarial network, the enhanced data is used for depth information generation and texture feature migration, an initial three-dimensional grid model is generated based on a Poisson reconstruction algorithm, and the initial three-dimensional grid model is constructed; and carrying out topological optimization by combining multi-view projection consistency constraint and a grid optimization algorithm to obtain an optimized three-dimensional scene model, setting a space division function, carrying out space division, constructing a grade model and a depth region, generating a scene semantic segmentation map, and updating a visual scene.

The present invention discloses a digital production operation management method for data quality assessment and analysis, including: step 1: constructing a multi-level distributed data acquisition system; step 2: designing a multimodal data fusion framework, combining graph neural network and time series data processing technology to realize data fusion and multidimensional analysis; step 3: constructing an intelligent scheduling engine to realize dynamic adaptive scheduling strategy to cope with real-time changes in the production environment; step 4: designing a decentralized self-organizing production network to realize flexible adjustment and optimization of the production process; step 5: realizing the coordinated optimization of product design and equipment maintenance through topology optimization, multi-objective optimization and machine learning algorithms; step 6: constructing a virtual production network to realize real-time monitoring, dynamic prediction and optimization decision support of the production system. Through the present invention, enterprises can break the limitations of traditional production models, improve production efficiency, flexibly respond to changes in market demand, and optimize resource allocation.

The invention discloses a load flow calculation and simulation control method and system for a digital twin power grid, and relates to the technical field of digital twin simulation control, and the method comprises the following steps: constructing a digital twin power grid model, and carrying out the dynamic topology optimization processing of the digital twin power grid model based on remote signaling credibility weighting; according to the optimized digital twin power grid model, identifying the power grid operation risk based on an integrated learning model; according to the risk identification result, generating a transfer path control strategy based on an analytic hierarchy process and a fuzzy comprehensive evaluation method; mapping the transfer path control strategy into a control action instruction set, and simulating execution and establishing a feedback correction mechanism on the digital twin power grid model; by generating the optimal path control strategy and performing control strategy analog simulation and self-adaptive feedback correction based on the digital twin power grid model, the problem of lack of intelligent path control strategy selection and simulation verification based on state dynamic identification in the prior art is solved.

The invention relates to a heat dissipation control method and device for a high-order computing power automatic driving FPC sensing module, and the method comprises the following steps: carrying out the temperature data collection of a target FPC sensing module through a preset distributed thermosensitive array, and obtaining the data of a three-dimensional temperature field; performing heat flow path calculation on the target FPC sensing module based on the three-dimensional temperature field data to obtain a heat flow density distribution diagram; performing topological optimization calculation on the target FPC sensing module based on the heat flux density distribution diagram to obtain a heat dissipation structure layout scheme; performing dynamic optimization solution on the heat dissipation structure layout scheme through a branch and bound algorithm to obtain a heat dissipation control parameter set; and inputting the heat dissipation control parameter set into a preset execution unit, and performing real-time heat dissipation control on the target FPC sensing module to obtain an ideal heat dissipation state, thereby solving the problems that a traditional method often cannot accurately and effectively cool local hot spots and lacks flexibility to adapt to dynamic thermal changes under different working loads.

The invention provides a 3D model multi-scale lightweight method and system based on topological optimization, and relates to the technical field of model lightweight, and the method comprises the steps: carrying out the mesh generation of a target 3D model, and generating a finite element model; a multi-scale topological optimization model is established, density distribution is adjusted through a density filtering function, and a macroscopic lightweight structure is obtained in combination with minimum size constraint and gray scale gradient projection processing; based on a macroscopic lightweight structure, a skeleton structure is obtained, based on an adaptive polygon segmentation rule, a porous filling unit is constructed, a multi-parameterized model is fused, a parameterized representation method of three variables of density, size and shape is established, and an intermediate scale structure is obtained through a multi-target genetic algorithm in combination with a sensitivity analysis technology; and embedding the intermediate-scale structure into the macroscopic lightweight structure, generating a multi-scale structure in combination with cascade optimization, calculating equivalent material attributes by adopting a homogenization method, iteratively adjusting model parameters, and finally obtaining a multi-scale lightweight result.

The invention relates to the technical field of fluid pipeline topological optimization, in particular to a fluid pipeline topological optimization design method based on a parameter simulation model. The method comprises the following steps of performing parametric modeling on a fluid pipeline to obtain a pipeline topology geometric parameter set; according to topological feature identification of the pipeline topology geometric parameter set, obtaining pipeline topology description data; constructing a multi-physics field equation set according to the pipeline topology description data to obtain a pipeline coupling field basic equation set; carrying out characteristic decomposition on the pipeline coupling field basic equation set to obtain a pipeline decoupling equation set; performing iterative solution on the pipeline decoupling equation set to obtain a pipeline multi-physics field coupling solution; performing sensitivity coefficient evaluation according to the pipeline multi-physics field coupling solution to obtain pipeline physics field sensitivity; and performing time domain integral response analysis on the pipeline physical field sensitivity to obtain the contribution degree of the pipeline physical field. According to the method, the deviation between the topological design and the practical application of the fluid pipeline can be obviously reduced.

The invention provides a self-adaptive network topology dynamic reconstruction method and system based on deep reinforcement learning, and relates to the technical field of deep reinforcement learning, and the method comprises the steps: obtaining the topology state information, service flow distribution information and historical reconstruction records of a current network; extracting topological correlation characteristics among nodes through graph convolution operation, and generating fusion state representation in combination with service flow information; inputting the fusion state representation into a deep reinforcement learning model to identify bottleneck nodes and redundant links, and outputting a reconstruction action candidate set; searching and evaluating the long-term cumulative income of the candidate actions through a Monte Carlo tree, and screening an optimal reconstruction action sequence; a graph coloring algorithm is utilized to allocate time slots and process resource conflicts, and a resource-feasible topology adjustment scheme is generated; and extracting a network evolution rule through tensor decomposition, and constructing a topological optimization association mapping graph. According to the method, the network bottleneck can be intelligently identified, the network topology structure is dynamically optimized, and the network performance and the resource utilization rate are effectively improved.

The invention provides a forest land tree height measurement and determination method and system based on laser radar point cloud data. Stress wave signals are collected based on a trunk base acoustic emission sensor array to generate an acoustic characteristic parameter set, the digital twin model is driven to complete forest stand structure topological optimization, and a three-dimensional growth vector model reflecting the internal mechanical state of a trunk is formed. And synchronously fusing high-precision slope point cloud data returned by the unmanned aerial vehicle laser radar in real time, correcting a terrain distortion error through a dynamic splicing algorithm in combination with stress distribution characteristics, and generating a crown segmentation boundary constrained by physical characteristics. And finally outputting a tree height parameter corrected by the abrupt slope topography through model iterative optimization and space vector analysis. According to the technical scheme, synchronous sensing of the three-dimensional shape and the mechanical state of the tree in the complex terrain environment is achieved, and the tree height measurement error is reduced.

The invention provides an online monitoring analysis method and system for hydropower station pump equipment, and relates to the technical field of information, and the method comprises the steps: judging that a bolt group has a fatigue failure risk when a fatigue damage degree D value reaches a preset critical threshold value, and comprehensively evaluating the health state of the bolt group in combination with bolt pre-tightening force monitoring data and a loosening failure critical threshold value of the bolt group, obtaining a residual service life prediction result of the bolt group; and by analyzing the stress nephogram and the strain nephogram of the stress concentration area around the bolt hole and the residual service life prediction result, a topological optimization and parameter optimization method is adopted to carry out optimization design on the structural arrangement and the pre-tightening force application scheme of the bolt group, and optimal bolt group design parameters for prolonging the fatigue life are obtained. Accurate prediction and optimal design of the fatigue life of the bolt group are realized, and the operation reliability and safety of the pump are improved.

The invention provides a gradient conformal dot matrix design method and system based on topological optimization density field mapping and application, and relates to the field of structural lightweight design. The design method comprises the following steps: dividing a model into conformal hexahedral grids, wherein the size of a grid unit is set as the size of a lattice cell element; on the basis of topological optimization pretreatment calculation, density field information of grid units is extracted, and a continuous gradient TPMS lattice structure is constructed on the basis of a three-way linear interpolation method through density field mapping; extracting space coordinates of TPMS unit cells of the density field corresponding to each conformal hexahedral grid unit; all vertex coordinates of the TPMS unit cells in the local coordinate system are converted into corresponding conformal hexahedral grid unit coordinates in the overall coordinate system based on isoparametric conversion; traversing all the conformal hexahedral grid units to obtain the gradient conformal TPMS lattice structure and outputting the gradient conformal TPMS lattice structure. According to the method, the defect that the mechanical property of the structure is influenced due to incomplete and non-conformal cells during traditional dot matrix filling can be overcome.

The invention provides a multi-sealing method and system for a high-vacuum die-casting die based on topological optimization, and relates to the technical field of die structures.The multi-sealing method comprises the steps that a sealing structure parameterized model is established, geometric parameters and boundary conditions of a cavity wall face are obtained, an initial design domain is constructed, and grid units are divided; carrying out topological optimization calculation based on a multi-objective function considering contact stress distribution, and iteratively optimizing the relative density of grid cells by adopting an SIMP method; and according to the obtained topological configuration of the sealing structure, multiple sealing structure design is carried out, a three-dimensional entity model is constructed, and the sealing performance is verified through finite element analysis.

The invention discloses a speed reducer gear box lightweight design method based on topological optimization, and relates to the technical field of gear box optimization. A processing end establishes a three-dimensional parameterized geometric model, performs finite element mesh generation on the geometric model, optimizes material distribution of the geometric model based on a topological optimization algorithm, and optimizes the material distribution of the geometric model; a topological optimization result is parameterized into geometric features, a random generation tool is used for generating a plurality of initial variable sets, a genetic algorithm is used for outputting an optimal variable set, a geometric model is reconstructed according to the topological optimization result and a genetic algorithm result, and after dynamic-static simulation is conducted on the reconstructed geometric model, the optimal variable set is obtained. And evaluating whether the performance of the reconstructed geometric model reaches the standard or not, and if not, optimizing genetic algorithm parameters according to an evaluation result and then cyclically reconstructing the geometric model until the performance of the reconstructed geometric model reaches the standard. The design system intelligently optimizes a local high-stress area, so that the lightweight requirement is met, and meanwhile, the strength and rigidity balance of the structure is ensured.

The invention discloses a topological optimization improvement method of an initial layout for extracting mainstream characteristics under a complex heat dissipation working condition, a medium and a program product, and belongs to the technical field of intersection of thermal management and structural topological optimization, and the method is designed for a liquid cooling heat dissipation structure under the conditions of high Reynolds number turbulence and conjugate heat transfer. The method comprises the following steps: constructing a two-dimensional array type parameterized initial layout, generating high-fidelity sample data based on CFD simulation, training a neural network prediction model with an SE attention mechanism, carrying out initialization scheme screening in combination with a correction objective function under a flow field freezing hypothesis and an ETO optimization algorithm, and carrying out prediction on the flow field freezing hypothesis. And topological optimization evolution based on a variable density method is implemented on the basis of the optimization initial state. According to the method, the final effect of topological optimization can be improved while the calculation complexity is reduced, collaborative optimization of the heat dissipation efficiency and the flowing performance is achieved, and an effective means is provided for conjugate heat transfer design of the liquid cooling heat dissipation structure under the high Reynolds number turbulence complex working condition.

The invention discloses a micro-grid network topology flexible configuration method based on matrix topology, and relates to the field of micro-grids, and the method comprises the steps: constructing a heterogeneous database of a micro-grid network and equipment; mapping a micro-grid network and equipment into a graph structure, and constructing a graph neural network model; setting mapping strategies of different heterogeneous hardware units according to heterogeneous characteristics of the different heterogeneous hardware units, and constructing a scheduling model of the heterogeneous hardware units by adopting a reinforcement learning algorithm; inputting the preprocessed data into a graph neural network model to obtain an incidence relation between a micro-grid network topology structure and equipment parameters, and taking the incidence relation as a constraint condition; inputting the preprocessed data into a scheduling model to obtain a scheduling strategy of the heterogeneous hardware unit, and taking the scheduling strategy as an optimization target; constructing a multi-objective function of micro-grid network topology optimization; and solving the multi-objective function of the microgrid network topology optimization by adopting an intelligent algorithm, and generating a microgrid network topology scheme. According to the invention, the robustness of the topology scheme is improved.

The invention relates to the technical field of three-dimensional modeling, in particular to a rural energy facility rapid modeling method based on three-dimensional laser point cloud. The method comprises the following steps: acquiring point cloud data of energy facilities in a rural area by using three-dimensional laser scanning equipment to obtain point cloud data of the rural energy facilities; performing target identification according to the rural energy facility point cloud data to obtain rural energy facility target data; constructing a three-dimensional model according to the rural energy facility target data to obtain a rural energy facility three-dimensional model; and performing refined modeling according to the rural energy facility three-dimensional model to obtain a rural energy facility refined model. According to the method, the energy facility model with high precision, rich details and complete physical attributes is generated by combining geometric refinement, topological optimization and physical attribute simulation, so that the modeling efficiency is remarkably improved, and technical support is provided for various application scenes such as operation and maintenance management, fault detection and simulation analysis of facilities.

The invention relates to the technical field of structure optimization design, and particularly discloses a box-type beam structure lightweight design method based on topological optimization, which comprises the following steps: constructing a multi-physics field coupled three-dimensional parameterized model, and performing cross-scale analysis on integrated structure rigidity, thermal deformation, vibration mode and fatigue life to obtain a three-dimensional parameterized model; a variable density method and level set method mixed topological optimization strategy is adopted, and an initial material distribution scheme meeting the structural function integrity is generated by modeling the relationship between density and stress strain; starting a quantum genetic algorithm and deep reinforcement learning fused intelligent optimization engine; according to the method, through the multi-physics field coupled three-dimensional parameterized model, a variable density method and level set method mixed topological optimization strategy is combined, the relation between the structural lightweight target and the additive manufacturing process is effectively balanced, multi-physics field analysis of thermal response, vibration, fatigue and the like is introduced, and the structural lightweight target is obtained. And it is guaranteed that the box-type beam meets the mechanical property and other functional requirements in actual application.

The invention relates to the technical field of model construction, in particular to a method and a system for constructing a sheet metal working three-dimensional process model. The method comprises the following steps: acquiring equipment specification data and material characteristic data; the method comprises the following steps: acquiring geometric parameters and motion parameters of sheet metal processing equipment by using a three-dimensional scanner, and integrating equipment specification data and material characteristic data to construct a sheet metal three-dimensional initial process model; analyzing a material plastic deformation characteristic of the material characteristic data and setting a processing parameter threshold range; the machining parameter threshold range is input into the three-dimensional initial process model for stage machining process simulation, the flange area strain rate, the dangerous section thinning rate and the rebound angle variable quantity are output, and stage machining process simulation comprises plate inflow stage simulation, forming stage simulation and shaping stage simulation. According to the method, three-dimensional scanning, comprehensive machining process simulation, topological optimization and a real-time error feedback mechanism are introduced, so that the construction accuracy of the sheet metal machining process model is improved.

An offshore wind power plant composite ring current collection system topology planning method based on an N-1 safety criterion comprises the following steps that S1, wind power plant basic data including wind turbine and transformer substation node coordinates, wind turbine rated power, cable electrical parameters and the like are obtained; s2, generating a candidate cable set and a cable cross avoidance constraint pair set based on the basic data; s3, a scene set containing a normal scene and all single cable fault scenes is constructed, an optimization model with the minimum total cost of the whole life cycle as the target is established, and cable construction / operation variables and direct current power flow power balance, node degrees and cable cross avoidance constraints are integrated; s4, solving the model to obtain an initial topology scheme; and S5, carrying out full fault set N-1 security verification on the initial scheme, and bringing a violation scene into a fault set to resolve until the scheme meets an N-1 criterion. According to the method, through non-predefined topological optimization and planning, reliability and economical efficiency are taken into consideration, calculation complexity is reduced, and the method can be expanded to a cross-substation scene.

The invention discloses a fatigue life simulation evaluation method for a lightweight aluminum alloy material of a new energy automobile, and relates to the technical field of material life evaluation. A microstructure image is collected, coupling features are extracted through machine learning, and heterogeneous data fusion and enhancement are completed; generating a topological optimization structure based on a GAN, introducing a VPSC model to describe anisotropy according to a stress gradient dynamic grid, and constructing a dynamic finite element model; fusing vehicle driving data, predicting a load by using LSTM, performing VMD decomposition and environment correction, and realizing space-time correlation load spectrum reconstruction; a phase field model is used in a microcosmic mode, cracks are tracked in a macroscopic mode through XFEM, damage parameters are transmitted in a bidirectional coupling mode, and multi-physics field coupling simulation is carried out; fusing simulation and test data by adopting Bayesian reasoning, calculating life probability distribution, and correcting parameters when errors exceed the limit; according to the method, the fatigue life prediction error is finally reduced, the time consumption of single simulation is reduced, full-life-cycle evaluation and visual early warning are realized, an efficient scheme is provided for lightweight design, and industrial technology upgrading is promoted.

The invention provides a CAD model reconstruction method and device for a topological optimization result, and relates to the technical field of computer modeling, and the method comprises the steps: carrying out grid preprocessing, carrying out component type grid segmentation, carrying out patch type grid segmentation, obtaining at least two sub-grid patches, carrying out parameterization on each sub-grid patch, and obtaining a final parameterization result. And performing uniform interpolation sampling based on the final parameterization result to obtain sampling points, generating a spline surface by using the sampling points, assembling the spline surface, and generating a reconstructed CAD model corresponding to the three-dimensional topological optimization fruit. The grid model reconstruction method has the advantages of being high in automation degree, high in realizability, easy to operate and the like, is not limited to a tubular topological optimization structure, has no obvious difference in effect when processing tubular and non-tubular model characteristics, has higher universality, can adapt to the diversity requirements of industrial structure optimization, and is suitable for large-scale popularization and application. And meanwhile, the reconstruction efficiency and the reconstruction effect from the triangular mesh model to the CAD model can be improved.

The invention discloses an automatic river network grading and river topological relation identification method based on a graph theory, and relates to the technical field of hydrological geographic information. The method comprises the following steps: acquiring and cleaning a vector river network, a key point location and DEM data of a target drainage basin; constructing an initial river network graph model based on the line element connection relationship; integrating DEM topographic evidence and graph theory connection features, constructing and solving a global potential energy field equation containing topographic driving and boundary constraint, and calculating flow potential energy attributes of nodes of the whole network to determine a flow relationship; based on the flow direction relation, identifying topology abnormal structures such as strong connectivity components in the network, and performing ring breaking processing by using direction confidence to generate a ring-free directed network structure; and performing river grade division based on a topology transfer rule, and associating the key point location to a river network skeleton. According to the method, through global potential energy field solving and topological optimization, the problems that the flow direction of the plain micro-geomorphic area is difficult to recognize and complex loops cannot be graded are solved, and automatic construction of the river network topology is achieved.

The invention discloses a data flow optimization method and system based on artificial intelligence. The method comprises the steps of multi-modal data flow collection, digital twin basic modeling, decision enhancement, dynamic topology optimization and resource arrangement optimization. The invention relates to the technical field of network data flow management, adopts a data flow integration optimization scheme combining digital twin basic modeling, dynamic topological optimization and decision comprehensive optimization, not only improves the flexibility and real-time performance of a system, but also optimizes the efficiency and intelligent level of resource allocation; the quantum mapping and quantum optimization technology is adopted, adaptive noise and disturbance simulation are combined, the mapping precision and the dynamic adaptive capacity between a virtual layer and a physical layer are improved, and the state error between a virtual node and a physical node is optimized through quantum mapping parallel processing; hierarchical federated learning is combined with an adaptive learning strategy to optimize a data flow decision, and a joint optimization method of a quantum heuristic algorithm and a graph neural network is introduced into dynamic topological optimization.

The invention discloses a variable air gap domain-based permanent magnet motor topology hierarchy optimization design method and a motor, and the method comprises the steps: firstly determining an electromagnetic performance layer optimization target, constructing a geometric parameter model, carrying out the dimensionality reduction of a high-dimensional parameter, and constructing a first hierarchy optimization sub-model I; performing geometric parameter optimization on the stator and rotor basic domain by combining a multi-objective optimization algorithm to obtain an optimized motor I; analyzing and optimizing the characteristic electromagnetic force of the motor I under different working conditions to serve as a second-level optimization target, constructing a second-level optimization sub-model II, performing small-scale online topological optimization in combination with a multi-target genetic algorithm, performing secondary design on a motor air gap design domain, obtaining a sample data set, constructing a parallel agent model, and constructing a second-level optimization sub-model II; then large-scale off-line topological optimization is carried out, and remodeling of a motor air gap design domain is achieved; and performing simulation evaluation on the final optimized motor. According to the method, on the premise that the vehicle permanent magnet driving electromagnetic performance is kept, electromagnetic vibration noise under multiple working conditions is suppressed, and meanwhile optimization efficiency is remarkably improved.

The invention discloses a power network topology optimization method based on time sequence data analysis, and relates to the technical field of power network topology optimization, and the method comprises the following steps: dividing a power grid into a plurality of sub-regions, and injecting disturbance signals into the sub-regions; obtaining time sequence disturbance response data, extracting oscillation characteristics, and identifying oscillation nodes; constructing an oscillation weakness evaluation model, and carrying out weakness evaluation on the oscillation nodes; correcting a phase difference matrix of the DTW based on an evaluation result, and screening a cross-regional risk propagation path; and power grid topology reconstruction is carried out according to a screening result, so that the problems of inaccurate weak node identification and low cross-regional high-risk path screening accuracy in a traditional network optimization method can be solved.

The invention provides an acquisition and analysis method and system for 3D printing tool clamp data based on big data, and relates to the technical field of 3D printing and intelligent manufacturing, and the method comprises the steps: obtaining a clamp historical design scheme, machining parameters and sensor data, carrying out the feature extraction and data cleaning, and forming structured data; then, the design intention is analyzed based on a BERT-CNN model, and a clamp applicability classification database is constructed in combination with the classification model; and then, generating a lightweight clamp structure by adopting a generative adversarial network and topological optimization, and optimizing the topological structure of the lightweight clamp structure through an optimization model. Thirdly, simulation analysis is carried out, stress, deformation and thermal stress are evaluated, errors are optimized based on simulation parameters, and optimized clamp design data are obtained; and finally, the clamp is manufactured, stress data are collected in real time, and machining process parameters are optimized through dynamic analysis. According to the method, clamp design and manufacturing are optimized through data driving, and design efficiency and clamp performance are improved.

The invention discloses a BIM-based high-speed rail overhead line system elastic sling and electric connection prefabrication processing method, and relates to the field of elastic sling processing, and the method comprises the steps: building a knowledge base, and building an initial elastic sling BIM model of a target project through parametric modeling; establishing an initial finite element analysis model of the target project through grid division and material attribute mapping; calculating the internal force and deformation of the elastic sling under the action of the static load by adopting an incremental iteration method; carrying out dynamic time-history analysis by adopting a direct integration method to obtain time-history curves of the elastic sling under different seismic waves; according to static and dynamic analysis results, stress-strain response of the elastic sling is extracted, and the initial elastic sling BIM model is optimized by adopting a topological optimization method with the minimum strain of the elastic sling as the target and the stress constraint of the elastic sling as the condition; and updating the finite element analysis model by using the optimized elastic sling BIM model. According to the method, the parameterized design degree of the elastic sling is improved.

The invention relates to a liquid silica gel injection molding process optimization method and system in the field of intelligent manufacturing, and the method comprises the steps: obtaining mold structure data adaptive to a shrinkage rate, combining geometric characteristics of a complex structure product, adopting a topological optimization algorithm to redesign a parting surface and a demolding path, and determining a lossless demolding mold parting scheme; whether the product damage risk is lower than a preset threshold value or not is judged by simulating stress distribution of the demolding path, and if the product damage risk is lower than the threshold value, final mold manufacturing parameters are generated, and an optimized production process configuration file is obtained; extracting production rhythm data from the optimized production process configuration file, dynamically adjusting the operation cycle of the injection molding machine in combination with the demolding efficiency monitored in real time, and determining equipment parameter configuration for efficient mass production; and generating a control instruction sequence according to the equipment parameter configuration, and transmitting the control instruction sequence to the injection molding equipment and the mold heating system through the industrial Internet of Things to obtain operation state data of the automatic production line.

The invention discloses a high-voltage integration and cooperative control method and system for an energy storage system, and the method comprises the steps: carrying out the three-dimensional space coupling design of a high-voltage box and a power conversion system PCS through a topological optimization algorithm according to the power level and heat distribution demands of the energy storage system, and generating a physical layer architecture scheme of a high-voltage all-in-one machine; based on a physical layer architecture scheme of the high-voltage all-in-one machine, constructing a collaborative optimization data set by using a timestamp alignment technology; inputting the collaborative optimization data set into a model predictive control framework to generate dynamically adjusted PCS working parameters; and according to the PCS working parameters, the internal temperature distribution of the high-voltage all-in-one machine is calculated in real time through the three-dimensional thermal field reconstruction model, the rotating speed of a cooling fan and the switching frequency of a power device are dynamically adjusted, and a cooperative control instruction is generated. According to the embodiment of the invention, the integration level and the cooperative control capability of the high-voltage energy storage system can be improved, and the efficient and stable operation of the energy storage system is realized.

The invention discloses a personalized design intelligent interaction method based on topological optimization, and the method comprises the steps: S1, constructing a user behavior monitoring system based on multi-modal data collection, and generating a high-dimensional data set containing time sequence features; s2, based on an adaptive deep neural network model, performing dynamic prediction and hierarchical modeling on the personalized demand of the user; s3, generating an optimal solution of the design parameters in real time through a dynamic topology reconstruction algorithm; s4, iteratively updating a design scheme generation rule through a reinforcement learning mechanism; s5, constructing a multi-objective design space exploration and optimization framework supported by the neural network, and rapidly screening and optimizing design parameters; s6, dynamic updating and local optimization of the design scheme are achieved through multi-dimensional data mapping and parameterization regulation and control means; and S7, establishing a data-driven continuous learning and evolution mechanism, continuously optimizing the user demand prediction model and designing an optimization algorithm. The method has the advantages of being high in dynamic adaptability, high in intelligent level and high in personalized meeting precision.

The lightweight design method for the steel-concrete combined wind power tower drum based on the topological optimization algorithm comprises the steps that S1, a multi-physics field coupling model of the steel-concrete combined wind power tower drum is constructed, and coupling parameters are determined; s2, material layouts of the steel-concrete combined wind power tower drum on the macro scale and the micro scale are obtained, and multi-scale collaborative optimization is carried out by dynamically adjusting scale parameters; s3, constructing a material distribution strategy, determining the distribution form and orientation of a steel material in a tension area and a compression area of the tower drum, and reasonably configuring a concrete material; s4, predicting tower performance under different design variables through the deep neural network model; and S5, performing multi-target evaluation on the optimized steel-concrete combined wind power tower drum design scheme, and determining a lightweight design scheme by constructing a comprehensive evaluation function and weighing the relationship between targets. According to the method, on the premise that the mechanical property and the structural reliability of the wind power tower drum are guaranteed, accurate regulation and control and lightweight design of material distribution are achieved.